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Mrs. Nidia Isabel Molina Gómez
Hydraulic and Environmental Engineering Department, Universitat Politècnica de València, 46022 Valencia, Spain

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0 Sustainable Development
0 urban development
0 Sustainable Development Goals
0 GIS Spatial Analysis
0 air pollution modelling

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Review
Published: 19 August 2020 in International Journal of Environmental Science and Technology
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Air quality has an effect on a population’s quality of life. As a dimension of sustainable urban development, governments have been concerned about this indicator. This is reflected in the references consulted that have demonstrated progress in forecasting pollution events to issue early warnings using conventional tools which, as a result of the new era of big data, are becoming obsolete. There are a limited number of studies with applications of machine learning tools to characterize and forecast behavior of the environmental, social and economic dimensions of sustainable development as they pertain to air quality. This article presents an analysis of studies that developed machine learning models to forecast sustainable development and air quality. Additionally, this paper sets out to present research that studied the relationship between air quality and urban sustainable development to identify the reliability and possible applications in different urban contexts of these machine learning tools. To that end, a systematic review was carried out, revealing that machine learning tools have been primarily used for clustering and classifying variables and indicators according to the problem analyzed, while tools such as artificial neural networks and support vector machines are the most widely used to predict different types of events. The nonlinear nature and synergy of the dimensions of sustainable development are of great interest for the application of machine learning tools.

ACS Style

N. I. Molina-Gómez; J. L. Díaz-Arévalo; P. A. López-Jiménez. Air quality and urban sustainable development: the application of machine learning tools. International Journal of Environmental Science and Technology 2020, 18, 1029 -1046.

AMA Style

N. I. Molina-Gómez, J. L. Díaz-Arévalo, P. A. López-Jiménez. Air quality and urban sustainable development: the application of machine learning tools. International Journal of Environmental Science and Technology. 2020; 18 (4):1029-1046.

Chicago/Turabian Style

N. I. Molina-Gómez; J. L. Díaz-Arévalo; P. A. López-Jiménez. 2020. "Air quality and urban sustainable development: the application of machine learning tools." International Journal of Environmental Science and Technology 18, no. 4: 1029-1046.

Journal article
Published: 13 July 2020 in International Journal of Biometeorology
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Thousands of deaths associated with air pollution each year could be prevented by forecasting the behavior of factors that pose risks to people’s health and their geographical distribution. Proximity to pollution sources, degree of urbanization, and population density are some of the factors whose spatial distribution enables the identification of possible influence on the presence of respiratory diseases (RD). Currently, Bogotá is among the cities with the poorest air quality in Latin America. Specifically, the locality of Kennedy is one of the zones in the city with the highest recorded concentration levels of local pollutants over the last 10 years. From 2009 to 2016, there were 8619 deaths associated with respiratory and cardiovascular diseases in the locality. Given these characteristics, this study set out to identify and analyze the areas in which the primary socioeconomic and environmental conditions contribute to the presence of symptoms associated with RD. To this end, information collected in field by performing georeferenced surveys was analyzed through geostatistical and machine learning tools which carried out cluster and pattern analyses. Random forests and AdaBoost were applied to establish hot spots where RD could occur, given the conjugation of predictor variables in the micro-territory. It was found that random forests outperformed AdaBoost with 0.63 AUC. In particular, this study’s approach applies to densely populated municipalities with high levels of air pollution. In using these tools, municipalities can anticipate environmental health situations and reduce the cost of respiratory disease treatments.

ACS Style

Nidia Isabel Molina-Gómez; Dayam Soret Calderón-Rivera; Ronal Sierra-Parada; José Luis Díaz-Arévalo; P. Amparo López-Jiménez. Analysis of incidence of air quality on human health: a case study on the relationship between pollutant concentrations and respiratory diseases in Kennedy, Bogotá. International Journal of Biometeorology 2020, 65, 119 -132.

AMA Style

Nidia Isabel Molina-Gómez, Dayam Soret Calderón-Rivera, Ronal Sierra-Parada, José Luis Díaz-Arévalo, P. Amparo López-Jiménez. Analysis of incidence of air quality on human health: a case study on the relationship between pollutant concentrations and respiratory diseases in Kennedy, Bogotá. International Journal of Biometeorology. 2020; 65 (1):119-132.

Chicago/Turabian Style

Nidia Isabel Molina-Gómez; Dayam Soret Calderón-Rivera; Ronal Sierra-Parada; José Luis Díaz-Arévalo; P. Amparo López-Jiménez. 2020. "Analysis of incidence of air quality on human health: a case study on the relationship between pollutant concentrations and respiratory diseases in Kennedy, Bogotá." International Journal of Biometeorology 65, no. 1: 119-132.

Journal article
Published: 20 April 2020 in Sustainability
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Different studies have been carried out to evaluate the progress made by countries and cities towards achieving sustainability to compare its evolution. However, the micro-territorial level, which encompasses a community perspective, has not been examined through a comprehensive forecasting method of sustainability categories with machine learning tools. This study aims to establish a method to forecast the sustainability levels of an urban ecosystem through supervised modeling. To this end, it was necessary to establish a set of indicators that characterize the dimensions of sustainable development, consistent with the Sustainable Development Goals. Using the data normalization technique to process the information and combining it in different dimensions made it possible to identify the sustainability level of the urban zone for each year from 2009 to 2017. The resulting information was the basis for the supervised classification. It was found that the sustainability level in the micro-territory has been improving from a low level in 2009, which increased to a medium level in the subsequent years. Forecasts of the sustainability levels of the zone were possible by using decision trees, neural networks, and support vector machines, in which 70% of the data were used to train the machine learning tools, with the remaining 30% used for validation. According to the performance metrics, decision trees outperformed the other two tools.

ACS Style

Nidia Isabel Molina-Gómez; Karen Rodríguez-Rojas; Dayam Calderón-Rivera; José Luis Díaz-Arévalo; P. Amparo López-Jiménez. Using Machine Learning Tools to Classify Sustainability Levels in the Development of Urban Ecosystems. Sustainability 2020, 12, 3326 .

AMA Style

Nidia Isabel Molina-Gómez, Karen Rodríguez-Rojas, Dayam Calderón-Rivera, José Luis Díaz-Arévalo, P. Amparo López-Jiménez. Using Machine Learning Tools to Classify Sustainability Levels in the Development of Urban Ecosystems. Sustainability. 2020; 12 (8):3326.

Chicago/Turabian Style

Nidia Isabel Molina-Gómez; Karen Rodríguez-Rojas; Dayam Calderón-Rivera; José Luis Díaz-Arévalo; P. Amparo López-Jiménez. 2020. "Using Machine Learning Tools to Classify Sustainability Levels in the Development of Urban Ecosystems." Sustainability 12, no. 8: 3326.

Conference paper
Published: 14 August 2019 in 2019 Congreso Colombiano y Conferencia Internacional de Calidad de Aire y Salud Pública (CASP)
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Around the term sustainable development, generated plans, programs and strategies focused on meeting the needs of today's generations without limiting the satisfaction of the needs of generations future. To this end, governments worldwide have carried out important efforts, on the one hand, to achieve the objectives of the millennium and now within the framework of the Development Goals Sustainable (ODS). The Colombian government assumes the challenge of guarantee the achievement of a healthy life for its inhabitants, which requires the interconnection and comprehensiveness of the SDGs. To level urban areas include, among other challenges, the improvement in health indicators, associated with air pollution and that are achievable from a policy framework and for their achievement requires the participation of the stakeholders headed by the community. This study analyzed the information published in various virtual media, of the national order, in the period 2009 to 2018; text mining techniques were applied and machine learning tools, by using the software R. Central themes were identified, on which the country and its capital city have given relevance from the media Communication. It was possible to know the scope of the disclosure challenges, progress and opportunities with the implementation of the Millennium Goals, the SDGs and the role of air quality. It was found that sustainable development does not present greater outreach in the media and sustainability relates mainly to biodiversity and financial activities. Efforts are focused on specific information and it is scarce the dissemination of the challenges, goals and progress in SDG. Although the air quality is a topic of interest, did not present a paper relevant as supported by the facts of security, poverty, education and quality of life. Such an analysis allows establish thematic, information and communication priorities trend political development, disclosed to the community, as actor on whom the implementation of the guidelines of politics.

ACS Style

Nidia Isabel Molina Gomez; Camilo Andres Rodriguez; P. Amparo Lopez; Jose Luis Diaz Arevalo. Text Mining and Machine Learning to Identify Sustainable Development Priorities. 2019 Congreso Colombiano y Conferencia Internacional de Calidad de Aire y Salud Pública (CASP) 2019, 1 -5.

AMA Style

Nidia Isabel Molina Gomez, Camilo Andres Rodriguez, P. Amparo Lopez, Jose Luis Diaz Arevalo. Text Mining and Machine Learning to Identify Sustainable Development Priorities. 2019 Congreso Colombiano y Conferencia Internacional de Calidad de Aire y Salud Pública (CASP). 2019; ():1-5.

Chicago/Turabian Style

Nidia Isabel Molina Gomez; Camilo Andres Rodriguez; P. Amparo Lopez; Jose Luis Diaz Arevalo. 2019. "Text Mining and Machine Learning to Identify Sustainable Development Priorities." 2019 Congreso Colombiano y Conferencia Internacional de Calidad de Aire y Salud Pública (CASP) , no. : 1-5.

Conference paper
Published: 01 August 2019 in 2019 Congreso Colombiano y Conferencia Internacional de Calidad de Aire y Salud Pública (CASP)
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Alrededor del término desarrollo sostenible se han generado planes, programas y estrategias enfocados en la satisfacción de las necesidades de las generaciones del presente sin limitar la satisfacción de las necesidades de las generaciones futuras. Bajo este fin, los gobiernos a nivel mundial han realizado importantes esfuerzos, por un lado, para alcanzar los objetivos del milenio y ahora en el marco de los Objetivos de Desarrollo Sostenible (ODS). El gobierno colombiano asume el desafío de garantizar el logro de una vida sana para sus habitantes, la cual requiere de la interconexión e integralidad de los ODS. A nivel urbano se incluyen, entre otros desafíos, la mejora en los indicadores de salud, asociados a la contaminación del aire y que son alcanzables desde un marco de política y para su logro requiere de la participación de los grupos de interés encabezados por la comunidad. Este estudio analizó la información publicada en diversos medios de comunicación virtuales, del orden nacional, en el periodo 2009 a 2018; se aplicaron técnicas de minería de texto y herramientas de aprendizaje automático, mediante el uso del software R. Se identificaron temas centrales, sobre los cuales el país y su ciudad capital han prestado relevancia desde los medios de comunicación. Fue posible conocer el alcance en la divulgación de los retos, avances y oportunidades con la implementación de los Objetivos del Milenio, de los ODS y el papel de la calidad del aire. Se encontró que el desarrollo sostenible no presenta mayor divulgación en los medios y la sostenibilidad se relaciona principalmente a la biodiversidad y las actividades financieras. Los esfuerzos se concentran en información específica y es escaza la divulgación de los retos, metas y avances en ODS. Aunque la calidad del aire es un tema de interés, no presentó un papel relevante como lo soportan los hechos de seguridad, pobreza, educación y calidad de vida. Un análisis de este tipo permite establecer las prioridades temáticas, de información y de desarrollo político tendencial, divulgados a la comunidad, como actor sobre quien recae la implementación de los lineamientos de política

ACS Style

Nidia Isabel Molina Gomez; Camilo Andres Rodriguez; P. Amparo Lopez; Jose Luis Diaz-Arevalo. Minería de texto y aprendizaje automático para identificar prioridades de desarrollo sostenible [Not available in English]. 2019 Congreso Colombiano y Conferencia Internacional de Calidad de Aire y Salud Pública (CASP) 2019, 1 -5.

AMA Style

Nidia Isabel Molina Gomez, Camilo Andres Rodriguez, P. Amparo Lopez, Jose Luis Diaz-Arevalo. Minería de texto y aprendizaje automático para identificar prioridades de desarrollo sostenible [Not available in English]. 2019 Congreso Colombiano y Conferencia Internacional de Calidad de Aire y Salud Pública (CASP). 2019; ():1-5.

Chicago/Turabian Style

Nidia Isabel Molina Gomez; Camilo Andres Rodriguez; P. Amparo Lopez; Jose Luis Diaz-Arevalo. 2019. "Minería de texto y aprendizaje automático para identificar prioridades de desarrollo sostenible [Not available in English]." 2019 Congreso Colombiano y Conferencia Internacional de Calidad de Aire y Salud Pública (CASP) , no. : 1-5.