AirKit: A Citizen-Sensing Toolkit for Monitoring Air Quality.

Increasing urbanisation and a better understanding of the negative health effects of air pollution have accelerated the use of Internet of Things (IoT)-based air quality sensors. Low-cost and low-power sensors are now readily available and commonly deployed by individuals and community groups. However, there are a wide range of such IoT devices in circulation that differently focus on problems of sensor validation, data reliability, or accessibility. In this paper, we present AirKit, which was developed as an integrated and open source "social IoT technology". AirKit enables a comprehensive approach to citizen-sensing air quality through several integrated components: (1) the Dustbox 2.0, a particulate matter sensor; (2) Airsift, a data analysis platform; (3) a reliable and automatic remote firmware update system; (4) a "Data Stories" method and tool for communicating citizen data; and (5) an AirKit logbook that provides a guide for designing and running air quality projects, along with instructions for building and using AirKit components. Developed as a social technology toolkit to foster open processes of research co-creation and environmental action, Airkit has the potential to generate expanded engagements with IoT and air quality by improving the accuracy, legibility and use of sensors, data analysis and data communication.This research was supported by the European Research Council under the European Union’s Seventh Framework Programme (FP/2007–2013)/ERC Grant Agreement n. 313347, “Citizen Sensing and Environmental Practice: Assessing Participatory Engagements with Environments through Sensor Technologies”, and from the European Research Council under the European Union’s Horizon 2020 research and innovation programme (ERC Grant Agreement n. 779921), “AirKit: Citizen Sense Air Monitoring Kit”. The University of Cambridge provided additional support through the ESRC Impact Acceleration Account (2020) for enabling impact

Diseño de una aplicación de monitoreo de emanación de gases en la ciudad mediante el uso de tecnologías de inteligencia artificial

El presente trabajo de investigación realiza una revisión de referencias bibliográficas relacionadas a sistemas de monitoreo y modelos de pronóstico de contaminación atmosférica basados en técnicas de inteligencia artificial. La revisión consta principalmente de artículos de investigación científica obtenidos de bases de datos revistas indexadas, donde se exponen sus principales hallazgos, así como se presentan puntos de encuentro y desencuentro entre los diferentes autores. Finalmente, se presentan las conclusiones obtenidas a partir de la síntesis de la información revisada.Trabajo de investigaciónCampus Lima Centr