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Nota técnica / Technical note=

 

Validación del uso de teléfonos inteligentes para medición de rui= do ambiental urbano

 

Valida= tion of the use of smartphones for measuring environmental urban noise

 

Andrea Sangurima1= , Michelle Medina1, MarĂ­a-Laura Guerrero2 , Daniel Orellana2,3

1 Carrera de Ingen= ierĂ­a Ambiental, Facultad de Ciencias QuĂ­micas, Universidad de Cuenca, Ecuador

2 Lla= ctaLAB-Ci= udades Sustentables, Departamento Interdisciplinario de Espacio y PoblaciĂłn, Universidad de Cuenca, Ecuador.

3 Fac= ultad de Ciencias Agropecuarias, Universidad de Cuenca, Ecuador.

Aut= or de correspondencia: daniel.orellana@ucuenca.edu.ec<= span style=3D'color:windowtext'>

Fecha de recepciĂłn: 2 de octubre de 2020 - Fecha de aceptaciĂłn: 15 de noviembre de= 2020

 

 

RES= UMEN

El presente estudio evalúa la fiabilidad del uso de teléfonos inteligentes para monitorear el ruido urbano, como una alternativa de bajo costo a los instrumentos de monitoreo tradicionales. La metodología consis= tió en diseñar una herramienta de evaluación de ruido en KoBoToolbox, la cual registró las mediciones recopiladas simultáneamente con un sonóm= etro y dos aplicaciones para teléfonos inteligentes, Sound Meter X Standard y Sou= nd Meter Pro, en los sistemas iOS y Android respectivamente. Las tres medicion= es fueron comparadas con el método de análisis de varianza (ANOVA) de un fac= tor. Las pruebas se realizaron en el parque El Paraíso, de la ciudad de Cuenca, basándose en la legislación ecuatoriana. Los resultados sugieren que la aplicación utilizada para la plataforma Android presenta menor grado de variación de medición con respecto a la utilizada en la plataforma iOS. A= sí también, muestran una relación significativa entre niveles de ruido eleva= dos y focos de contaminación acústica por presencia de tráfico vehicular. La investigación tiene implicaciones para el uso futuro en programas de monit= oreo participativo de ruido ambiente bajo un enfoque de ciencia ciudadana, complementando las normas y legislaciones vigentes.

Palabras clave: Aplicación, teléfono inteligente, ruido urbano, KoBoToolbox, espacio público.

 

 

ABSTRACT<= /b>

This study evaluates the reliabilit= y of using smartphones for monitoring environmental urban noise as a low-cost alternative to conventional monitoring instruments. A noise evaluation tool= was designed using KoBoToolbox enabling the simulta= neous recording of sound measurements with a professional sound level meter and t= wo smartphone apps: Sound Meter X Standard on iOS, and Sound Meter Pro on Andr= oid. The measurements were compared with the one-factor analysis of variance (AN= OVA) method. Sampling was carried out in “El Paraíso” park in Cuenca, by fo= llowing the Ecuadorian legislation. Results suggest that Android app shows less measurement variation compared to the iOS platform. Results also showed a significant relation between high noise levels and noise pollution hotspots caused by traffic. Indeed, this research establishes a background for future use in noise level community monitoring programs under a citizen science fo= cus, while also complements current national laws and regulations.

Keywords: App, smartphone, urban noise, KoBoToolbox, publ= ic space.

 

 

1.<= span style=3D'font:7.0pt "Times New Roman"'>      =       INTRODUCCIĂ“N=

 

La contaminación acústica en el casco u= rbano de las ciudades es un tema de gran impacto e importancia puesto que, el rui= do ambiental es uno de los principales problemas de contaminación ambiental u= rbana ya que interfiere en las actividades de la población, perturbando su biene= star en general. Su fuente primaria radica en el parque automotor (Bocher, Petit, Picaut, Fortin, & Guillaume, 2017). Debido a esto, varios países han adoptado medidas para cuantificar y controlar el nivel de ruido= a través de regulaciones y del establecimiento de métodos de medición estandarizados, los cuales dependen de instrumentos especializados como sonómetros y dosímetros, que son de alto costo, difícil acceso y manejo,= por lo que su uso está restringido a entidades especializadas (Wessels & Basten, 2016).

Por ese motivo, la medición de ruido amb= iente suele depender de campañas y programas específicos, diseñados para tener= datos representativos a nivel de ciudad (Motta, 2020). Sin embargo, existe una gr= an cantidad de casos en los que se requiere mediciones puntuales de ruido ambi= ente para contrastar con los valores límites permisibles, tales como la evaluac= ión de parques y

espacios pĂşblicos, campus universitarios, centros de ocio y comercio, y otros lugares colectivos. En estos casos, la = disponibilidad de datos de ruido ambiente y la posibilidad de levantar mediciones estandarizadas suele ser muy baja o nula; y en los pocos programas de monit= oreo que se han realizado a lo largo del tiempo, se ha descubierto que el 97% de= la poblaciĂłn en las urbes experimentan exposiciones a niveles de ruido mayore= s a 55 dB= [1](A)= [2] (GAD Municipal del CantĂłn Cuenca, 2016), que acorde con la Organiz= aciĂłn Mundial de la Salud (OMS), es nocivo. Y conforme a Tro= mbetta Zannin, Coelho Ferreira, & Szeremetta (2006) los parques urbanos, pese a que suponen un papel esencial en el desarrollo de localidad, figuran como focos de contaminaciĂłn acĂşstica (Maristany, 2016).

La importancia de establecer estas bases = de datos certeros radica en las alternativas de uso que presentan como: la determinaciĂłn de los puntos crĂ­ticos para adoptar las medidas de mitigaci= Ăłn pertinentes (Maheswaran, N= ishant, Senthil Murugan, &a= mp; Prabaharan, 2020), para apoyar el control de los plan= es ya convenidos y aplicados (DĹľambas & DragÄŤeviÄ= ‡ 2020), y para elaboraciĂłn de modelos predictivos (Coral, Moromenacho, Moreta, Villalba, & Oviedo, 2020). Por ese motivo, medidas convenientes= y de bajo costo que ayuden a obtener esta informaciĂłn son de gran interĂ©s p= ara personas particulares, familias, asociaciones barriales u organizaciones gubernamentales, entre otras.

Los avances tecnológicos en el desarroll= o de dispositivos móviles, en particular de teléfonos inteligentes y aplicacio= nes de uso práctico han permitido que el uso de los mismos no se limite únicamente a la comunicación. La portabilidad y sinergia con los sensores que poseen, como micrófono, cámara, sistema de posicionamiento g= lobal (GPS), acelerómetros, entre otros (Garg, Lim, = & Lee, 2019), los convierten en instrumentos útiles para realizar monitoreos ambientales (Zuo<= span style=3D'mso-bookmark:_Hlk56242315'>, Xia, Liu, & Qiao, 2016). Estudios previos, han= demostrado que es posible medir niveles de ruido ambiental mediante aplicaciones de dispositivos móviles con una precisión equiparable a los instrumentos profesionales (Aumond et al., 2017; Lefe= vre & Issarny, 2018; McLen= non, Patel, Behar, & Abdoli-Eramaki, 2019; Murphy & King, 2016b). Existen variedades de aplicaciones de medición de soni= do tanto para el sistema operativo Android como para iOS, pero solo una fracci= ón logran la precisión suficiente para evaluar los niveles de ruido (Murphy &= amp; King, 2016b).

En Ecuador, el uso de dispositivos móvil= es inteligentes se ha elevado significativamente en los últimos años. Según= la Encuesta Nacional de Empleo, Desempleo y Subempleo (ENEMDU) realizada por el Instituto Nacional de Estadística y Censos (INEC), del 2011 al 2013 el porcentaje de personas con teléfonos inteligentes no era mayor al 16.9%, mientras que para el 2018 el 70.2% de la población poseía un teléfono inteligente (Instituto Nacional de Estadística y Censos, 2018; Ministerio = de Telecomunicaciones y Sociedad de la Información, 2018), por lo que la factibilidad de monitoreo ciudadano de la contaminación acústica puede se= r muy alta. Los sistemas operativos móviles más comunes en el mercado son Andro= id y iOS (Celestina, Hrovat, & Kardous, 2018), e investigaciones preliminares han encontrado que la medición de ru= ido con sistemas basados en iOS produce menores diferencias con respecto a valo= res de referencia (Kardous & Shaw, 2014).<= /o:p>

Al considerar diversos estudios donde se demuestra la viabilidad del uso aplicaciones y programas en telĂ©fonos inteligentes para obtener mapas y mediciones de ruido certeros (EiĂźfeldt, 2020; Garg = et al., 2019; Kanjo, 2010; Lee, Ga= rg, & Lim, 2020; Murphy & King, 2016a); y que al integrarlo con KoBoToolbox dinamizarĂ­a el proceso de recolecciĂłn de informaciĂłn de datos masiva para este tipo de procedimientos, podemos expl= orar la posibilidad de usarlos para la recopilaciĂłn de informaciĂłn acĂşstica p= untual en espacios urbanos por parte de ciudadanos, como complemento de las campaĂ= ±as y programas especializados de monitoreo de contaminaciĂłn acĂşstica. De esta = manera serĂ­a posible para los usuarios adquirir informaciĂłn sobre la calidad acĂ= şstica del entorno de forma puntual y crear conciencia de la realidad en cuanto a temas ambientales para tomar las medidas pertinentes.

El objetivo de este est= udio comparativo es constatar la viabilidad y precisión del uso de dos aplicaci= ones de teléfonos inteligentes, en conjunto con la plataforma KoBoToolbox para sondeos de ruido ambiental en espacios públicos, como alternativa a l= os instrumentos de medición convencionales. Para esto, = se analizan las diferencias estadísticas en cuanto a la exactitud de medició= n para las aplicaciones móviles Sound Meter X Standard y Sound Meter Pro, en sist= emas iOS y Android respectivamente, con respecto a las mediciones realizadas con= un sonómetro profesional, siguiendo el procedimiento estándar del país. Esto permitirá implementar programas de monitoreo participativo de ruido ambien= te bajo un enfoque de ciencia ciudadana, con una herramienta adaptable a las diversas metodologías empleadas para este fin, como complemento a normas y legislaciones vigentes nacionales referentes al ruido ambiente.<= /span>

 

 

2.<= span style=3D'font:7.0pt "Times New Roman"'>      =       MÉTODOS

 

Para establecer la viabilidad de disposit= ivos móviles como medidores de contaminación acústica, se compararon las medi= ciones simultáneas de ruido ambiente en espacios públicos obtenidas con Sound Me= ter X Standard para iOS y Sound Meter Pro para Android con mediciones de referenc= ia obtenidas mediante un sonómetro integrador de clase 2, marca CENTER TECHNO= LOGY CORP, modelo 390. Dicho artefacto cumplía con los requisitos de la Norma d= e la Comisión Electrotécnica Internacional IEC 61672-1:2013 (International Electrotechnical Commission (IEC), 2013) y ANSI/ASA S1.4-2014 (Acoustical Society<= /span> of America (ASA, 20= 14)).

La calibraciĂłn es un factor importante q= ue influye significativamente en un registro adecuado de los niveles de presiĂ= łn sonora y el espectro de frecuencia (Garg et = al., 2019). En consecuencia, fue necesario calibrar los micrĂłfonos de los telĂ©= fonos inteligentes previo a las mediciones de campo; con la suscripciĂłn a Sound = Meter Pro para iOS se produjo un tono de referencia de 1 kHz para la calibraciĂłn= del micrĂłfono integrado, esta señal fue conectada a un altavoz y posterior se colocĂł el micrĂłfono de los dispositivos lo más cerca posible de la fuent= e, con una posiciĂłn y orientaciĂłn adecuada (Serpanos= , Ren= ne, Schoepflin, & Davis, 2018).

A continuación, a través de la opción = de configuración de las aplicaciones seleccionamos la opción de calibración= en donde se pudo visualizar el nivel de sonido de entrada medido como un valor= de texto, de este modo se consideró que si el nivel de entrada medido concord= aba con el nivel de referencia emitido entonces el micrófono se encontraba calibrado, caso contrario se ajustó manualmente el nivel de entrada con el nivel de referencia (Faber Acoustical, LLC, 2019). En caso de no contar co= n la suscripción mencionada anteriormente, se recomienda utilizar el programa d= e uso libre Audacity para generar el tono de referencia requerido. Para una mejor ejecución de la calibración, es preferible realizar este procedimiento en= una habitación o lugar tranquilo, con las mínimas influencias de sonidos exte= rnos (Crook, 2020; Rana, Ch= ou, Bulusu, Kanhere, & Hu, 2015). En la Tabla 1 se presenta información detallada de los equipos utilizados.

Para la recolección de datos en campo, primero se configuró KoBoToolbox, una herramie= nta de código abierto que permite recolectar información rápidamente in situ y exportarla, permitiendo establecer una base de datos. En esta plataforma se diseñó la metodología a seguir y se fundamentó en la normativa ecuatori= ana establecida en el Anexo 5 del Texto Unificado de Legislación Secundaria pa= ra determinar el nivel de ruido para fuentes fijas (Ministerio del Ambiente, 2015). Se esquematizó un formulario de evaluación que registró todos los= datos para los 3 tipos de mediciones realizados, calculó automáticamente el rui= do total utilizando la fórmula de cálculo de nivel presión sonora equivalen= te (LeqProm) y se ubicó en una escala predefinida el niv= el de ruido encontrado.

Donde n, corresponde al nĂşmero de mediciones, L nivel de presiĂłn sonora, eq se atribuye a equivalente y, p es el promedio de las mues= tras Leq<= /span> (promedio logarĂ­tmico).

Las mediciones fueron realizadas simultáneamente con dos aplicaciones mĂłviles, usando las versiones de pag= o de las mismas para facilitar el proceso de recolecciĂłn de datos debido a que dichas versiones permitĂ­an preestablecer el tiempo de mediciĂ= łn de cada muestra; y se instalaron en distintos dispositiv= os de diferente sistema operativo. Para su selecciĂłn, se usĂł como base los resultados de estudios previos ya mencionados (Murphy & King, 2016b; Nast, Speer, & Prell,= 2014), considerando su disponibilidad para nuestra regiĂłn. AsĂ­, se seleccionaron= Sound Meter X Standard para iOS y Sound Meter Pro para Android.=

Se seleccionó el Parque El Paraíso, uno= de los principales parques de la ciudad de Cuenca porque se encuentra en una z= ona donde la exposición al ruido oscila entre los 60 y 65 dB(A) en el periodo diurno, según el informe de Monitoreo de Ruido Ambiente en la ciudad de Cu= enca para el año 2018 (GAD Municipal del Cantón Cuenca & Universidad del A= zuay, 2018), en el que, el máximo permisible es 55 dB(A) (Ministerio del Ambient= e, 2015). Y la medición en campo se ejecutó durante los días 19 y 23 de sep= tiembre de 2019, en el horario comprendido de 07h01 a 21h00.

La determinaciĂłn de los puntos de medici= Ăłn se elaborĂł segĂşn el muestreo de conglomerados, para ello se considerĂł como factores la delimitaciĂłn geográfica del área de interĂ©s y el nĂşmero mĂ= ­nimo de puntos para representar adecuadamente la distribuciĂłn de una variable (Lind, Mason, & Marchal, 2004). Se definieron 30 p= untos de mediciĂłn posicionados en su mayorĂ­a a lo largo de caminos peatonales preferenciales dentro del parque, contemplando las zonas de mayor afluencia= de usuarios y mayoritariamente adyacentes a calles con circulaciĂłn vehicular.=

Los niveles de ruido se determinaron acat= ando el procedimiento estipulado en la legislación ecuatoriana, midiendo el val= or de Nivel de Presión Sonora Equivalente (Leq) para fuentes fijas. Con ello, se obtuvieron valores de 15 segundos cada una con = una pausa de aproximadamente 10 segundos entre cada medición; para cada punto y horario determinado en base a las cada una. Los instrumentos empleados fuer= on colocados en ponderación (A), utilizada para control de ruido urbano por la semejanza al oído humano en la percepción de sonidos, y modo de respuesta= lenta dado que esta modalidad arroja valores cada segundo. En la primera mitad del análisis, todos los equipos fueron soportados en trípodes, modelo Slik f740, a una altura de 1.5 m (Montes González, B= arrigón Morillas, Rey Gozalo, & Godinho, 2020) y distancia del equipo al momento de la medición de 1.0 m, como se establece en el Anexo 5 del Texto Unificado de Legislación Secundaria para determinar el nivel de ruido para fuentes fijas citado previamente.

 

Mientras que, en la segunda mitad, única= mente el sonómetro integrador continuó soportado; debido a que en gran parte de= los proyectos de ciencia ciudadana relacionados a este campo sujetan el disposi= tivo móvil con la mano, tomando en cuenta siempre ciertas condiciones para obte= ner una medición real de ruido in-situ (Picaut et al., 2019). Luego, en KoBoToolbox se registraron los valores de niveles de ruido marcados en los dispositivos= de cada punto crítico, y esta plataforma calculó automáticamente el promedio niv= eles de ruido marcados en los dispositivos de cada punto crítico, y esta plataforma calculó automáticamente el promedio logarítmico (Le= qProm) de los datos obtenidos, mediante la ecuación referida en el Anexo 5 del TU= LSMA del Ministerio del Ambiente (2015).

Para evaluar la existencia de diferencias estadísticas significativ= as en la precisión de medición de los 240 datos levantados durante el horario de muestro establecido, por cada punto crítico del Parque El Paraíso, se tra= bajó con un Análisis de Varianza (ANOVA) de un factor, que en este caso fue el equipo de medición de ruido, el cual poseía tres variantes: dos dispositi= vos móviles y el sonómetro. De la misma manera, para determinar el rendimient= o de las aplicaciones móviles y conocer la magnitud del error de medición con respecto al sonómetro, se realizó un análisis de estadística descriptiv= a.

Por otro lado, se trabajó con diagramas de caja para observar a gro= sso modo la distribución de los datos y así conocer la influencia del tráfico vehicular en la generación de niveles de ruido elevados, así se categoriz= ó los puntos críticos distinguiendo su cercanía a calles transitadas. Todos los análisis se realizaron con el programa RStudio versión 1.2.1335.

 

 

3.<= span style=3D'font:7.0pt "Times New Roman"'>      =       RESULTADOS

 =

En la Tabla 2 se mu= estra que el valor calculado de F fue 0.09 resultando menor al valor crítico de = 3.95, además el valor de p fue mayor que el nivel de significancia de 0.0= 5 (α=3D0.05), p= or lo que las diferencias entre las medias no fueron estadísticamente significat= ivas de modo que se puede afirmar que no existe diferencia significativa entre l= as mediciones de ruido realizadas por los tres distintos equipos.

En la Tabla 3 se expresa mediante un análisis descriptivo la difere= ncia media entre los valores medidos con los teléfonos inteligentes y el valor = de referencia considerado, en este caso como el sonómetro. Se puede observar = que entre los 50 y 56 dB(A) la diferencia media en la medición de las aplicaci= ones con respecto a las condiciones de referencia es de 2.5 y 2 respectivamente, mientras que para mediciones mayores a 60 dB(A) el resultado de la media es= más variable.

Del tamaño de muestra (n=3D30), se determinaron 18 puntos lejanos de calles transitadas y 12 puntos cercanos a las mismas. Como resultado de la interpretación de los datos registrados alejados de focos de tráfico vehi= cular comparando las mediciones registradas por el Sonómetro, Android y iOS se observó en la Figura 1 que los niveles de ruido oscilan en un rango de 52 = a 56 dB(A) con valores máximos de 61 dB(A) aproximadamente. Por otra parte, en = la interpretación de los datos registrados cercanos a focos de tráfico vehic= ular se observó en la Figura 1= que los niveles de ruido registrados se situaron en un rango 57 a 67 dB(A) con valores máximos de 74 dB(A) aproximadamente.

Adicionalmente se observa por un lado que, existe una tendencia de l= os datos a una distribución positivamente asimétrica, ya que existió una ce= rcanía de la mediana al primer cuartil. Por otra parte, se muestra que la aplicaci= ón con menor grado de variabilidad está asociada a la plataforma Android, ya = que la asociada a la plataforma iOS presenta una distribución de datos más va= riada con respecto al sonómetro (Fig. 1).

 

Figura 1. Diagrama de caja de los niveles de ruido registrados lejanos y cerc= anos a calles transitadas.

4.<= span style=3D'font:7.0pt "Times New Roman"'>      =       DISCUSIĂ“N

 =

El estudio demuestra que, para la medición de niveles de ruido ambi= ente en espacios públicos con fines informativos, es posible reemplazar el uso = del sonómetro por teléfonos inteligentes a través de las aplicaciones Sound Meter X Sta= ndard y Sound Meter Pro, con la ayuda de KoBoToolbox.= De este modo, se facilita a la población la accesibilidad a herramientas de determinación de niveles de ruido urbano, ya que no depende de la adquisic= ión de un equipo profesional para realizar las mediciones.

Además, con la determinación del nivel de significancia estadísti= ca, se confirma la confiabilidad de medición de los dispositivos móviles, cuyo u= so está sujeto a una previa capacitación para la aplicación correcta de la metodología. Estos hallazgos apoyan estudios que proponen la adopción generalizada de teléfonos inteligentes como dispositivos de detección colectivos (D’Hondt, S= tevens, & Jacobs, 2013; Eißfeldt, 2020; McLennon et al., 2019; Zamora, Calafate, Cano,= & Manzoni, 2017) y promueven la participación de la sociedad en el control de la contaminación acústica, en espacios abiertos (Aumond et al., 2017).=

Adicionalmente, se determina una distribuciĂłn de datos de mayor gra= do de variabilidad en la plataforma iOS con respecto al sonĂłmetro, por lo q= ue se recomienda usar Android. De igual manera, las diferencias medias de mediciĂ= łn encontradas denotan una semejanza entre los datos registrados por los disti= ntos equipos; aun asĂ­, comparando la variabilidad de error estándar se muestra= que las aplicaciones son menos eficientes midiendo niveles de ruido elevado. A pesar de esto las aplicaciones realizan un trabajo adecuado al medir dentro= de un grado de error aceptable que es tĂ­picamente de ±2 dB(A) (Murphy & King, 2016b). Con ello podemos mencionar que, en condiciones reales la precisiĂłn de mediciĂłn variará segĂşn el dispositivo que se est= Ă© empleando, influida por la calidad del micrĂłfono (Pic= aut et al., 2019).

De igual manera, se pudo ratificar la eficacia de las mediciones con valores certeros. Complementado el análisis de Murphy & King (2016b), = así como el de McLennon et al. (2019) con re= specto al hecho de que las aplicaciones no están listas para reemplazar a los equ= ipos de medición convencionales.

Por otro lado, mediante diagramas de caja se comprueba una relación entre niveles de ruido elevados y focos de contaminación acústica por pre= sencia de tráfico vehicular en la urbe, corroborando el hecho de que el ruido urb= ano es proveniente en su mayoría del parque automotor (Maristany, 2016). Ya qu= e, los valores de ruido encontrados, en su mayoría han superado el nivel permisible para esa zona de 55 dB(A); implicando una afectación a la salud física (enfermedades cardiovasculares) y psicosocial (trastornos de sueño= ) al estar expuestos a esos niveles manera continua (Johansson, 2020; Zamorano <= span class=3DSpellE>Gonzáleg et al., 2019). Demostrando que exist= e una relación directa con la calidad de vida de los ciudadanos, de igual manera limita el uso del espacio por lo poco satisfactorio y a largo plazo peligro= so, que resulta.

Cabe destacar que en el estudio solamente se midió el ruido total. = En cuanto al ruido de fondo proveniente de las conversaciones, no se consideró para el estudio, dado que no fue posible determinarlo porque en la normativ= a ecuatoriana, no está claro el procedimiento a seguir. Por último, antes de desarrollar= un estudio encaminado a este tema, es aconsejable efectuar un recorrido previo= al sito, para determinar las zonas de posible interferencia con el instrumento= de medición, como los postes de alta tensión eléctrica. Estos afectan principalmente a sonómetros y dificultan la toma de medición.<= /span>

 

 

5.&n= bsp;           CONCLUSIONES

 =

Es innegable que, en la ciudad de Cuenca el uso de mapas de ruido para calificar el grado de polución nos proporciona una idea general de calidad del entorno sonoro. Pero el uso de otras herramientas y metodologías, como la propuesta en esta investigación, nos ha permitido procesar con mayor agilidad temas de esta índole, además de establecer va= lores más puntuales; en especial en espacios públicos donde la recolección de = esta información es complicada y poco precisa.

Debido a el potencial que han mostrado estas aplicaciones: Sound Meter X Standard y Sou= nd Meter Pro, con la ayuda de KoBoToolbox para ser utilizada como instrumento de mediciĂłn popular, se ha probado que es posib= le tener resultados verĂ­dicos siguiendo procedimientos estandarizados con los dispositivos mĂłviles, sin llegar a ser complicada su ejecuciĂłn o requerir equipos de alto costo.

Con ello, n= os permitirĂ­a la creaciĂłn de una planificaciĂłn urbana sostenible a nivel sectorial o ba= rrial, asĂ­ como de polĂ­ticas pĂşblicas, centradas en minimizar la exposiciĂłn de= los espacios pĂşblicos al ruido de carretera; la cual podrĂ­a contener propuest= as de control, preventivas y correctivas; como la integraciĂłn de vegetaciĂłn cuya funciĂłn serĂ­a de amortiguar los sonidos desagradables y mejorar el confort urbano/paisajĂ­stico.

Finalmente, concluimos que la principal limitaciĂłn de este estudio = fue la falta de profundidad en las especificaciones de los dispositivos mĂłvile= s en los resultados. Como sugerencia para futuras investigaciones se considera q= ue es importante reunir varios dispositivos mĂłviles de diferentes fabricantes= , y asociar sus caracterĂ­sticas con los datos recolectados.

 

 

AGRADECIMIENTO

 

Este trabajo forma parte del proyecto “Evaluación del Espacio Pú= blico Abierto (EPA) en la ciudad de Cuenca” y fue patrocinado por el Grupo Llactalab-Ciudades Sustentables del Departamento de E= spacio y Población de la Universidad de Cuenca. Se remarca la contribución de Di= ana Moscoso, PhD docente de la Universidad de Cuenca, por sus valiosos consejos= en cuanto a Calidad de Aire Ambiente; de igual manera al Ingeniero Javier Urgilés y René Fernández por su apoyo durante las = excursiones para toma de mediciones.

 

 

 

 

 

REFERENCIAS

 

Acoustical Society of America (ASA). (2014). American National Standard Electroacoustics - Sound Level Meters= . Disponible enAumondA study of the accuracy of mobile t= echnology for measuring urban noise pollution in large scale participatory sensing campaigns. Applied Acoustics, 117, 219-226. https://doi.org/10.1016/ j.apacoust.2016.07.011<= o:p>

Applied= D’Hondt, E., Stevens, M., & Jacobs, A. (2013). Participatory noise mapping works! An evaluation of participatory sensing as an alternative to standard techniques for environmental monitori= ng. Pervasive and Mobile Computing, 9(5), 681-694. https://doi.org/10.1016/j.p= mcj.2012.09.002

Džambas, T., & Dragčević, V. (2020). Ocjena učinkovitosti mjera za smanjenje razina buke u urbanim sredinama. e-Zbo= rnik : Elektronički Zbornik Radova Građevinskog Fakulteta, 10(19), 1-= 9.

EiĂźfeldt, H. (2020). Sustainable urban air mobility supported with participatory noise sensing. Sustainability= Garg, S., Lim, K. M., & Lee, H. P. (2019). Apple Acoustics, 143, 222-228. https://doi.org/10.1016/j.apacoust.2018.08.013

Kanjo, E. (2010). N= oiseSPY: A real-time mobile phone platform for urban noise monitoring and mapping. <= i>Mobile Networks and Applications, 15, 562-574. https://doi.org/10.1007/s11036-009-0217-y

Kardous, C. A., & Shaw, P. B. (2014). Evaluation of smartphone sound measurement applications. The Journal of = the Acoustical Society of America, 135(4), 186-192. https://doi.org/10.1121/1.4865269

Lee, H. P., Garg, S., & Lim, K. M. (2020). Lind, D. A., Mason, R. D., & Marchal, G. W. (2004= ). Maheswaran<= span style=3D'mso-bookmark:_Hlk59275858'>Maristany, A. R. (2016). PENSUMMcLennon, T., Patel, S., Behar, A., & <= span class=3DSpellE>Abdoli-Eramaki, M. (2019). Evaluation of smartphone s= ound level meter applications as a reliable tool for noise monitoring. Journal= Evaluation of exposure to road traffic noise: Effects of microphone height and urban configuration. Environmental<= /span>Nast, D. R., Speer, W. S., & Le Prell, C. G. (2014). Sound level measurements using smartphone “Apps”: Useful or inaccurate? Noise and Health, 16= (72), 251. https://doi.org/10.4103/1463-1741.140495

Picaut, J., Fortin, N., Bocher, E., Petit, G., Aumond, P., & Guillaume, G. (2019). An open-science crowdsourcing approach for producing community noise maps using smartphones. Building and Environment, 148, 20-33. https://doi.org/10.1016/j.buildenv.2018.10.049

= SerpanosThe accuracy of smartphone sound le= vel meter applications with and without calibration. American Journal of Speech-Language Pathology, 27(4), 1319-1328. https://doi.org/10.1044/ 2018_AJSLP-17-0171

Trombetta Zanni= n, P. H., Coelho Ferreira, A. M., & Szeremetta= , B. (2006). Evaluation of noise pollution in urban parks. Environmental Monitoring and Assessment, 118(1-3), 423-433. https://doi.org/10= .1007/s10661-006-1506-6

Wessels, P. W., & Basten, T. G. H. (2016). ZuoMapping Urban Environmental Noise U= sing Smartphones. Sensors (Basel, Switzerland), 16(10), 1692. https://doi= .org/10.3390/s16101692

 

 

 

 

 

 

 

 

 

 


[1]= dB (decibelio) es una unidad utilizada para medir generalmente la intesidad del sonido.

[2] dB(A) es una unidad adimensional que expresa el logaritmo de la razĂłn entre una cantidad medida y una cantidad de referencia para describir los niveles de presiĂłn sonora, corregido para adaptar estos valores a la sensibilidad del oĂ­do hu= mano

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MASKANA, Vol. 11, No. 2, 81-87, 2020

https://publicaciones.ucuenca.edu.ec/ojs/index.php/maskana/article/vi= ew/3399

doi: 10.18537/mskn.11.02.08

© Aut= hor(s) 2020. CC Attribution 4.0 License.

 

<= span style=3D'mso-spacerun:yes'>  Publicado por DIUC - DirecciĂłn de InvestigaciĂłn de la Universidad de Cuenca                                   Â=  Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â =                                                  =                                      Â=  Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â  3

A Sangurima et al.: Uso de teléfonos inteligentes para medición de r= uido ambiental urbano

MA= SKANA, Vol. 11, No. 2, 81-87, 2020

doi: 10.18537/mskn.11.02.08                        =                                                =                                      Â=  Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â  3

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