Design and implementation of a sensing platform to assess and forecast environmental conditions

Local environments are altered due to natural phenomena caused by climate change. Some nature changes are, for instance, heat waves, floods, wildfires, and air pollution, among others. Monitoring these alterations is compulsory for users who require a better understanding of their surroundings, such as farmers, transportation companies, and climate researchers. There are already solutions implemented to address this, such as fixed weather stations, satellite images, or drone mapping; However, the limitation of these approaches is that they are unaffordable for small users due to high-cost sensors and the trained personnel needed for operation and maintenance. Also, they have limited coverage and do not share information with the public. Therefore, this paper shows the design, development, and evaluation ofan open-source automatic weather station (OSWS) based on low-cost sensors that monitor environmental variables, including temperature, relative humidity, atmospheric pressure, CO2 concentration, and particulate matter (PM1, PM2.5, and PM10). This station provides a fast solution for in-site measurements for different users; it can monitor the variables remotely and forecast values within a short-time period based on the historical data captured using the ARIMA model.