Air Quality Monitoring System in Semarang City Based on IoT

Agus Alwi Mashuri, Nely Zulfa

Abstract

Air is one of the most important elements of life for living things in the world. For humans, the air is an element that is very concerned because it is related to health. In the city of Semarang, the Air Pollution Standard Index (ISPU) at the KLHK station (Ministry of Life and Environment) Semarang City recorded a PM 10 of 7 while the air quality in Jakarta was monitored by the Air Quality Index (AQI) of 67 with parameters in the form of very small pollutant particles with a diameter less than 2.5 micrometres (PM 2.5). The Indonesian government has made efforts to reduce the air pollution index, such as reducing the number of vehicles with odd-even systems, users of environmentally friendly transportation modes such as BRT and Trans Central Java and clearing green lands in the middle of the city. The purpose of this research is how we make a tool that can determine the quality of the air around us and can be carried (portable) anywhere easily. Following the 4.0 industrial revolution that everything has been integrated with the Internet of Things (IoT) technology where the community can find out the air condition in real-time. In this research, later using the prototype method as a test. The main components are sensors consisting of MQ-6 (CO2 and smoke), MQ-7 (CO, LPG, CH4), MQ135 (Butane, AirQuality), and DHT-11 (Humidity, temperature) From the research that has been done where the air quality in urban areas has a low air quality index by measuring using a prototype consisting of a gas sensor and Arduino microntroller, which has been made to produce an average CO2 of 25 ppm, CO 2330 which has exceeded the threshold, while NH3 1.23 and C4H10 1120 are still below the threshold. These values are influenced by pollutants generated by transportation such as motorbikes, cars, and land transportation.

Keywords

Air quality; sensors; IoT; Internet; pollution

Full Text:

PDF

References

Blum, J. (n.d.). Exploring Arduino: Tools and Techniques for Engineering Wizardry.

Burke, C. J., Aleo, P. D., Chen, Y. C., Liu, X., Peterson, J. R., Sembroski, G. H., & Lin, J. Y. Y. (2019). Deblending and classifying astronomical sources with Mask R-CNN deep learning. Monthly Notices of the Royal Astronomical Society, 490(3), 3952–3965. https://doi.org/10.1093/mnras/stz2845

Carter, B., & Ragade, R. (2014). Message Transformation Services for Wireless Sensor Networks ( MTS-WSN ). January 2006.

Chen, C., Wen, T., Sun, C., & Juang, J. (2017). A Community Health Service Architecture Based on the Internet of Things on Health-Care. April 2015. https://doi.org/10.21307/ijssis-2017-477

Deshmukh, M. R., Chavan, S. J., Chavan, R. A., & Atarde, P. G. (2018). IOT Based Air & Sound Pollution Monitoring System. 3, 750–753.

Kaivonen, S., & Ngai, E. (2019). AC. Digital Communications and Networks. https://doi.org/10.1016/j.dcan.2019.03.003

Kurniawati, A. (2013). DIPONEGORO LAW REVIEW Volume 1, Nomor 2, Tahun 2013 (Vol. 1, pp. 1–11).

Leman, A. M. (2013). Occupational Safety and Health : Workers and Industrial Safety Monitoring For Sustainable Work Industrialisation and urban renewal has caused widespread problems relating to environments such Author Details : May, 34–36.

Nag, A., Mukhopadhyay, S. C., & Kosel, J. (2017). Wearable Flexible Sensors : A Review. c. https://doi.org/10.1109/JSEN.2017.2705700

Nayak, R., Panigrahy, M. R., Rai, V. K., Rao, T. A., & Diagram, B. (2017). IoT Based Air Pollution Monitoring System. 4, 571–575.

Sai, P. Y. (2017). An IoT Based Automated Noise and Air Pollution Monitoring System. 6(3), 419–423. https://doi.org/10.17148/IJARCCE.2017.6397

Swagarya, G., Kaijage, S., & Sinde, R. S. (2014). Air Pollution Monitoring System based on Wireless Networks - Simulation. 5(8), 9–16.

Refbacks

  • There are currently no refbacks.