The availability of novel, high-resolution pollution maps enables a wide range of new application scenarios, which were not possible before. In this paper, we combine high-resolution pollution maps available for the city of Zurich, Switzerland, with road network data to analyze how much urban dwellers can reduce their exposure to air pollution by not taking the shortest path between origin and destination but a healthier and slightly longer alternative route. We introduce a new weight function to assess the exposure on each street segment and evaluate the benefits of the healthier path. Finally, we efficiently implement the algorithm as stand-alone application for iOS and Android devices. The app helps city residents to understand and reduce their exposure to air pollutants.

Frequent sensor calibration is essential in sensor networks with low-cost sensors. We exploit the fact that temporally and spatially close measurements of different sensors measuring the same phenomenon are similar. Hence, when calibrating a sensor, we adjust its calibration parameters to minimize the differences between co-located measurements of previously calibrated sensors. In turn, freshly calibrated sensors can now be used to calibrate other sensors in the network, referred to as multi-hop calibration.

Up-to-date information on urban air pollution is of great importance for health protection agencies to assess air quality and provide advice to the general public in a timely manner. In particular, ultrafine particles (UFPs) are widely spread in urban environments and may have a severe impact on human health. However, the lack of knowledge about the spatio-temporal distribution of UFPs hampers profound evaluation of these effects. In this paper, we analyze one of the largest spatially resolved UFP data set publicly available today containing over 25 million measurements. We collected the measurements throughout more than a year using mobile sensor nodes installed on top of public transport vehicles in the city of Zurich, Switzerland. Based on these data, we develop land-use regression models to create pollution maps with a high spatial resolution of 100m × 100 m. We compare the accuracy of the derived models across various time scales and observe a rapid drop in accuracy for maps with subweekly temporal resolution. To address this problem, we propose a novel modeling approach that incorporates past measurements annotated with metadata into the modeling process. In this way, we achieve a 26% reduction in the root-mean-square error-a standard metric to evaluate the accuracy of air quality models-of pollution maps with semi-daily temporal resolution. We believe that our findings can help epidemiologists to better understand the adverse health effects related to UFPs and serve as a stepping stone towards detailed real-time pollution assessment.

Wireless sensor networks are increasingly used in application scenarios where a high data quality is inevitable, e.g., the control of industrial production areas. Nevertheless, many deployments must live with strict constraints regarding the sensing hardware and may not employ newest sensing technologies, e.g., due to limited energy budget, size, and bandwidth. Additionally, many applications would benefit from not only gathering absolute sensor readings but also knowing the quality of their low-cost sensor measurements. In this paper, we introduce a model-driven approach that (i) provides reliable accuracy bounds for individual noisy sensor readings and (ii) detects systematic and transient sensor errors. We apply our method to static and mobile real-world deployments of noisy and unstable low-cost sensors by analyzing large sets of urban temperature and ozone measurements. We find that the proposed algorithm successfully calculates precise accuracy bounds. We compare them to measurements of high-quality instruments and show that up to 96 % of the reference measurements are inside the computed accuracy bounds in the static scenario and up to 94 % in the mobile scenario. This is surprisingly high for the used low-cost sensors. By analyzing data from our static longterm deployment, we reveal that the ozone sensor's reliability is dependent on seasonal weather conditions.

Up-to-date information on urban air pollution, such as reliable pollution maps, is of great importance for health protection agencies to timely assess the air quality situation and provide advice to the general public. Ultrafine particles (UFPs) are widely spread in urban environments and believed to have severe impact on the human health. However, the lack of spatially resolved data hampers profound evaluation of these effects. In this work, we introduce one of the largest spatially resolved UFP data set available today, with over 25 million measurements to build high-resolution pollution maps for an urban area of 100 km2. The data is collected throughout more than one year using mobile sensor nodes, which are installed on top of public transport vehicles in the city of Zurich, Switzerland. We develop land-use regression models to create pollution maps with a high spatial resolution and study their temporal resolution limit.

Monitoring and managing urban air pollution is a significant challenge for the sustainability of our environment. We quickly survey the air pollution modeling problem,introduce a new dataset of mobile air quality measurements in Zurich, and discuss the challenges of making sense of these data.

We analyze push and pull for data collection in wireless sensor networks. Most applications to date use the traditional push approach, where nodes transmit sensed data immediately to the sink. Using a pull approach, nodes store the data in their local flash memory, and only engage in communication during dedicated collection phases. We show how one can transform an existing push-based collection protocol into a pull-based one, and compare the power consumption of both approaches on a 35-node testbed. Our results show that substantial energy gains are possible with pull, provided that the application can tolerate a long latency.