This synthesis project is focused on implementing an Internet of Things (IoT) network to measure environmental data in the city of Delft. This network consists of sensor platforms that are placed in the urban environment. Each sensor platform is mounted on fixed locations and it is not moved during the measurement time. The aim is to raise community’s environmental awareness to improve the quality of the environment. Recent developments in technology made it possible to fabricate small, efficient, and reliable sensors boards which are the base of these sensors platforms and making them efficient and reliable. Sensor boards like Arduino, Raspberry Pi, and LoPy are some examples of these small sensor boards. In this project, the LoPy is used which is a sensor board that is equipped with Bluetooth Low Energy, Wifi and a LoRa radio. This last one is a communication technology that makes longer communication distances possible. The sensor network measures four different environmental indicators that will be distributed to the public: temperature, humidity, noise and air quality. The network then communicates via LoRa this data to one centralized server where the data is stored, processed and sent back to the citizens. This data is made publicly accessible to academia, citizens and the stakeholders alike. The network is also made interactive, people who pass by can interact with the sensors and request specific environmental data in real time. The sensor network has been build and deployed in the city. During the uptime of the network it succeeded to provide the data to the citizens via the feedback mechanisms: a website with a dashboard and an automated twitter account. Local differences have been measured with temperature and humidity sensors. With regard to the noise sensor and air quality sensors no definitive conclusions could be drawn.

Low-cost air quality sensors can fill gaps between the sparse measurements done with high-quality national monitoring grids and might contribute to creating a more complete understanding of air pollution in an urban area. However, until there is no agreement on what degree of sensor accuracy is acceptable, the sensor data quality should be validated before governmental bodies use it as input for decision-making (Lewis2017). This research proposes a method to assess and improve the data quality of low-cost air quality sensors measuring Particulate Matter (PM). To answer the research question "How can accuracy and precision of Particulate Matter measurement results from a low-cost outdoor sensor network be improved by using a correction model, using data from reference sensors and additional sensors measuring inferencing phenomena?" an experiment setup with sensors operating under real-world conditions is applied. Two low-cost sensor nodes, both containing a microcontroller, two low-cost PM sensors, and a temperature and humidity sensor, are placed at two locations in the city of Rotterdam. At those two locations, they are placed next to a high-quality air quality monitoring station from the environmental agency of Rotterdam. These monitoring stations provide benchmark data for the low-cost sensor nodes. A third data source provides data on air pressure and wind speed for the whole city of Rotterdam. The data that originates from both sensor nodes and monitoring stations are matched and correlated with each other. Subsequently, the measurements from the low-cost sensor nodes are evaluated. Correlations and cross inferences of PM with other independent variables such as humidity, ambient temperature, wind speed and air pressure are investigated. Thereafter, utilizing the Stepwise Multiple Linear Regression method, various correction models are created that take various combinations of external variables into account. The correction models vary with respect to the amount of included external environmental variables and the polynomial degree. From all those possible correction models, the best correction model per location is selected by evaluating the Root Mean Square Error (RMSE) of the corrected dataset. Consequently, the results of the chosen correction model are validated. It is found that the best performing correction models are those that include only the original PM data and the effect of adding more independent variables is limited. The best correction models for the four low-cost PM sensors are able to decrease the RMSE of the observations: the original normalized RMSE ranged from 0.0918 to 0.1249, while the corrected normalized RMSE range from 0.03110 to 0.03759. So, it is possible to improve the data quality of low-cost PM sensors with the stepwise MLR method and setup as shown in this research. However, including parameters for independent variables humidity, temperature, air pressure or wind speed does not improve the data quality significantly. Besides, when an extra sensor node is placed in an air quality monitoring network as described in this research, it is necessary to create a correction model for that specific sensor. Like Castell (2017) and Mukherjee (2017) also found, it is necessary to calibrate each individual low-cost sensor before adding it to an air quality measuring network of the type as described in this research. Namely, it is found that for each low-cost PM sensor in the network different correction models are created.