Amsterdam circular economy ambitions raise the question of what would be the impact of an organic waste collection system on inhabitants. Impact is an effect of a source on a receptor. Existing impact modelling studies describe impact either from source or from receptor perspective. In spatial planning, both source and receptor are variant over space. However, incorporation of the spatial dimension in existing impact modelling studies is limited, especially in presentation of outcomes. The objective of this research is to answer: what spatio-temporal method is suitable to assess the impacts of organic waste collection system scenarios in Amsterdam? Tools used are QGIS and GRASS GIS, combined with PyQGIS scripting. Five impact indicators were defined: three nuisance indicators (noise, odour and congestion), all quantified in three factors (intensity, temporality and affected population), and two sustainable planning indicators (CO2 emission and waste collection), quantified in intensity factor only. Calculations are performed using simple theoretical principles, implemented from both source and receptor point of view. It was found that basic spatial tools and calculation principles suffice to quantify impact spatio-temporally. For congestion and waste collection modelling network calculations are needed. All indicator outcomes except for CO2 emission are relevant to present spatially. Both source and receptor point of view have their advantages and challenges, and therefore are complementing each other. Aggregation of factor values into one results in loss of information and transparency, which is contradicting to information and transparency gained by the high granularity spatio-temporal model. However, overview and simplicity in presentation of outcomes could be maintained by describing all impact factors per indicator in one clock-like marker. Impact assessment outcomes should be presented for individual sources and receptors. When presenting them on small scale, spatial aggregation into total values per neighbourhood is needed as well. Compared to presentation in a non-spatial graph, the spatial presentation of outcomes is of added value.

Along with the rise of the smart city movement, Internet of Things is an upcoming phenomenon. Objects and devices are becoming more and more wirelessly interconnected, communicating information between themselves and to human beings. As an extension on static sensor networks that gather real-time environmental data, the feasibility of implementing a dynamic sensor network based on LoRa communication is researched. To achieve such a dynamic system, a self-developed sensor platform was constructed, based on the microcontroller LoPy. Sensors attached to it include a hygrometer, thermometer and microphone. The emphasis of the research was on localisation of the sensors, to put the gathered sensor data into geographical context. A WiFi fingerprinting radiomap was constructed based on available MAC-addresses, their signal strengths, and GPS coordinates. The GPS module was only used for composing the radiomap. When the radiomap is completed, the module can be switched off, only to be switched on for periodical updates of the radiomap. The quality of the radiomap methodology was evaluated by constructing it of measurements gathered in four days, and testing it for the remaining three days. This test gave a correctness of 50% while another 38% of measurements were localised in a neighbouring cell. The correctness can be improved by having a longer training period. The quality of the collected sensor data turned out to be dependent on the weather conditions and the placement location on the carrier vehicle. Vehicle requirements were specified as driving through the city centre and having a schedule and route producing as little noise, heat and air pollution as possible. Another topic of research was LoRa communication, which was deemed as very limited for dynamic implementations, as the sending of location-related data takes up a large part of the already limited message size. To decrypt the sent message and store it in a meaningful database, Node-RED was used. Despite visualisation of measurements showed promising results, there is margin for improvement as far as data capturing is concerned.