A low cost georeferenced air-pollution measurement system used as early warning tool

The consequences of the exploitation of natural resources are evident in the current bad environmental conditions. For this reason, governments have implemented realtime environmental measurement systems and they have applied strategies focused to restrict the physical activities and avoid futures health problem. Even, in critical cases, the governments take measures to stop the movement of motor vehicles when the threshold of World Health Organization (WHO) is exceeded. The governmental measurement systems are limited because of high cost and they are typically located in big cities with few points of measure losing a lot of information in the uncovered areas. This work presents the development and implementation of a low cost georeferenced air-pollution measurement system that offers information of particulate measurement PM1, PM2.5 y PM10 by scatter. In addition, the system measures the levels of ozone concentration, and atmospheric variables such as temperature, humidity and barometric pressure. The whole system is connected to a low cost microprocessor with integrated Wi-Fi allowing to send the data to the cloud in real-time using MQTTprotocol, and thus the data can be georeferenced and published on an open access platform, used to the Internet of Things (IoT), for the acquisition and visualization of the data. In this way, the information is public and the residents of a particular area can look for the nearest measure of the environmental conditions. This work proposes a simple and cheap way to implement a measurement system allowing it to be easily replicated. The consequences for the health because of exposition to pollutants are serious, it implies a public policy issues with direct effect in the economy and development of cities. Because of all this, the air-pollution measurement systems should be improved and available in many more points. Governments can make public policy decisions about the environment using the information from systems like the proposed in this work.