The feasibility of quantitatively measuring ultrasound in air with a Schlieren arrangement has been demonstrated before, but previous work demonstrating calibration of the system combined with computation to yield the 3D pressure field does not exist. The present work demonstrates the feasibility of this both in theory and practice, and characterizes the setup used to gain the results. Elementary ray optical and Schlieren theory is exhibited to support the claims. Derivation of ray optical equations related to quantitative Schlieren measurements are shown step by step to help understand the basics. A numerical example based on the theoretical results is then displayed: Synthetic Schlieren images are computed for a theoretical ultrasonic field using direct numerical integration, then the ultrasonic field is recovered from the Synthetic Schlieren images using the inverse Abel transform. Accuracy of the inverse transform is evaluated in presence of synthetic noise. The Schlieren arrangement, including the optics, optomechanics, and electronics, to produce the results is explained along with the stroboscopic use of the light source to freeze ultrasound in the photographs. Postprocessing methods such as background subtraction and median and Gaussian filtering are used. The repeatability and uncertainty of the calibration is examined by performing repeated calibration while translating or rotating the calibration targets. The ultrasound fields emitted by three transducers (100 kHz, 175 kHz, and 300 kHz) when driven by 5 cycle sine bursts at 400 Vpp are measured at two different points in time. The measured 3D pressure fields measured for each transducer are shown along with a line profile near the acoustic axis. Pressure amplitudes range near 1 kPa, which is near the acoustic pressure, are seen. Nonlinearity is seen in the waveforms as expected for such high pressures. Noise estimates from the numerical example suggest that the pressure amplitudes have an uncertainty of 10% due to noise in the photographs. Calibration experiments suggest that additional uncertainty of about 2% per degree of freedom (Z, X, rotation) is to be expected unless especial care is taken. The worst-case uncertainty is estimated to be 18%. Limitations and advantages of the method are discussed. As Schlieren is a non-contacting method it is advantageous over microphone measurements, which may affect the field they are measuring. As every photograph measures the whole field, no scanning of the measurement device is required, such as with a microphone or with an LDV. Suggestions to improve the measurement setup are provided.

Air pollution introduces a major challenge for societies, where it leads to the premature deaths of millions of people each year globally. Massive deployment of air quality sensing devices and data analysis for the resultant data will pave the way for the development of real-time intelligent applications and services, e.g., minimization of exposure to poor air quality either on an individual or city scale. 5G and edge computing supports dense deployments of sensors at high resolution with ubiquitous connectivity, high bandwidth, high-speed gigabit connections, and ultralow latency analysis. This article conceptualizes AI-powered scalable air quality monitoring and presents two systems of calibrating low-cost air quality sensors and the image processing of pictures captured by hyperspectral cameras to better detect air quality. We develop and deploy different AI algorithms in these two systems on a 5G edge testbed and perform a detailed analytics regarding to 1) the performance of AI algorithms and 2) the required communication and computation resources.

Glycerol dialkyl glycerol tetraethers (GDGTs) are membrane-spanning lipids from Bacteria and Archaea that are ubiquitous in a range of natural archives and especially abundant in peat. Previous work demonstrated that the distribution of bacterial branched GDGTs (brGDGTs) in mineral soils is correlated to environmental factors such as mean annual air temperature (MAAT) and soil pH. However, the influence of these parameters on brGDGT distributions in peat is largely unknown. Here we investigate the distribution of brGDGTs in 470 samples from 96 peatlands around the world with a broad mean annual air temperature (-8 to 27 degrees C) and pH (3-8) range and present the first peat-specific brGDGT-based temperature and pH calibrations. Our results demonstrate that the degree of cyclisation of brGDGTs in peat is positively correlated with pH, pH = 2.49 x CBTpeat + 8.07 (n = 51, R-2 = 0.58, RMSE = 0.8) and the degree of methylation of brGDGTs is positively correlated with MAAT, MAAT(peat) (degrees C) = 52.18 x MBT'(5me) - 23.05 (n = 96, R-2 = 0.76, RMSE = 4.7 degrees C). These peat-specific calibrations are distinct from the available mineral soil calibrations. In light of the error in the temperature calibration (similar to 4.7 degrees C), we urge caution in any application to reconstruct late Holocene climate variability, where the climatic signals are relatively small, and the duration of excursions could be brief. Instead, these proxies are well-suited to reconstruct large amplitude, longer-term shifts in climate such as deglacial transitions. Indeed, when applied to a peat deposit spanning the late glacial period (similar to 15.2 kyr), we demonstrate that MAAT(peat) yields absolute temperatures and relative temperature changes that are consistent with those from other proxies. In addition, the application of MAAT(peat) to fossil peat (i.e. lignites) has the potential to reconstruct terrestrial climate during the Cenozoic. We conclude that there is clear potential to use brGDGTs in peats and lignites to reconstruct past terrestrial climate. (C) 2017 The Authors. Published by Elsevier Ltd.

Novel miniaturized multi- and hyperspectral imaging sensors on board of unmanned aerial vehicles have recently shown great potential in various environmental monitoring and measuring tasks such as precision agriculture and forest management. These systems can be used to collect dense 3D point clouds and spectral information over small areas such as single forest stands or sample plots. Accurate radiometric processing and atmospheric correction is required when data sets from different dates and sensors, collected in varying illumination conditions, are combined. Performance of novel radiometric block adjustment method, developed at Finnish Geospatial Research Institute, is evaluated with multitemporal hyperspectral data set of seedling stands collected during spring and summer 2016. Illumination conditions during campaigns varied from bright to overcast. We use two different methods to produce homogenous image mosaics and hyperspectral point clouds: image-wise relative correction and image-wise relative correction with BRDF. Radiometric datasets are converted to reflectance using reference panels and changes in reflectance spectra is analysed. Tested methods improved image mosaic homogeneity by 5% to 25%. Results show that the evaluated method can produce consistent reflectance mosaics and reflectance spectra shape between different areas and dates. © Authors 2017.