The emergence of low-cost, user-friendly and very compact air pollution platforms enable observations at high spatial resolution in near-real-time and provide new opportunities to simultaneously ...enhance existing monitoring systems, as well as engage citizens in active environmental monitoring. This provides a whole new set of capabilities in the assessment of human exposure to air pollution. However, the data generated by these platforms are often of questionable quality.
We have conducted an exhaustive evaluation of 24 identical units of a commercial low-cost sensor platform against CEN (European Standardization Organization) reference analyzers, evaluating their measurement capability over time and a range of environmental conditions. Our results show that their performance varies spatially and temporally, as it depends on the atmospheric composition and the meteorological conditions. Our results show that the performance varies from unit to unit, which makes it necessary to examine the data quality of each node before its use.
In general, guidance is lacking on how to test such sensor nodes and ensure adequate performance prior to marketing these platforms. We have implemented and tested diverse metrics in order to assess if the sensor can be employed for applications that require high accuracy (i.e., to meet the Data Quality Objectives defined in air quality legislation, epidemiological studies) or lower accuracy (i.e., to represent the pollution level on a coarse scale, for purposes such as awareness raising).
Data quality is a pertinent concern, especially in citizen science applications, where citizens are collecting and interpreting the data. In general, while low-cost platforms present low accuracy for regulatory or health purposes they can provide relative and aggregated information about the observed air quality.
•We find challenges associated with sensor robustness and measurement repeatability.•Field performance evaluation under diverse environmental conditions is necessary.•We show low-cost sensors are not ready for applications that require high accuracy.•Low-cost sensors can provide coarse information about the observed air quality.
We present a numerical method for the accurate and efficient simulation of strongly localized light sources, such as quantum dots, embedded in dielectric micro-optical structures. We apply the method ...in order to optimize the photon extraction efficiency of a single-photon emitter consisting of a quantum dot embedded into a multi-layer stack with further lateral structures. Furthermore, we present methods to study the robustness of the extraction efficiency with respect to fabrication errors and defects.
Surface temperatures were extracted from nighttime thermal infrared imagery of 167 large inland water bodies distributed worldwide beginning in 1985 for the months July through September and January ...through March. Results indicate that the mean nighttime surface water temperature has been rapidly warming for the period 1985–2009 with an average rate of 0.045 ± 0.011°C yr−1 and rates as high as 0.10 ± 0.01°C yr−1. Worldwide the data show far greater warming in the mid‐ and high latitudes of the northern hemisphere than in low latitudes and the southern hemisphere. The analysis provides a new independent data source for assessing the impact of climate change throughout the world and indicates that water bodies in some regions warm faster than regional air temperature. The data have not been homogenized into a single unified inland water surface temperature dataset, instead the data from each satellite instrument have been treated separately and cross compared. Future work will focus on developing a single unified dataset which may improve uncertainties from any inter‐satellite biases.
Bone's remarkable mechanical properties are a result of its hierarchical structure. The mineralized collagen fibrils, made up of collagen fibrils and crystal platelets, are bone's building blocks at ...an ultrastructural level. The organization of bone's ultrastructure with respect to the orientation and arrangement of mineralized collagen fibrils has been the matter of numerous studies based on a variety of imaging techniques in the past decades. These techniques either exploit physical principles, such as polarization, diffraction or scattering to examine bone ultrastructure orientation and arrangement, or directly image the fibrils at the sub-micrometre scale. They make use of diverse probes such as visible light, X-rays and electrons at different scales, from centimetres down to nanometres. They allow imaging of bone sections or surfaces in two dimensions or investigating bone tissue truly in three dimensions, in vivo or ex vivo, and sometimes in combination with in situ mechanical experiments. The purpose of this review is to summarize and discuss this broad range of imaging techniques and the different modalities of their use, in order to discuss their advantages and limitations for the assessment of bone ultrastructure organization with respect to the orientation and arrangement of mineralized collagen fibrils.
The recent emergence of low-cost microsensors measuring various air pollutants has significant potential for carrying out high-resolution mapping of air quality in the urban environment. However, the ...data obtained by such sensors are generally less reliable than that from standard equipment and they are subject to significant data gaps in both space and time. In order to overcome this issue, we present here a data fusion method based on geostatistics that allows for merging observations of air quality from a network of low-cost sensors with spatial information from an urban-scale air quality model. The performance of the methodology is evaluated for nitrogen dioxide in Oslo, Norway, using both simulated datasets and real-world measurements from a low-cost sensor network for January 2016. The results indicate that the method is capable of producing realistic hourly concentration fields of urban nitrogen dioxide that inherit the spatial patterns from the model and adjust the prior values using the information from the sensor network. The accuracy of the data fusion method is dependent on various factors including the total number of observations, their spatial distribution, their uncertainty (both in terms of systematic biases and random errors), as well as the ability of the model to provide realistic spatial patterns of urban air pollution. A validation against official data from air quality monitoring stations equipped with reference instrumentation indicates that the data fusion method is capable of reproducing city-wide averaged official values with an R2 of 0.89 and a root mean squared error of 14.3 μg m−3. It is further capable of reproducing the typical daily cycles of nitrogen dioxide. Overall, the results indicate that the method provides a robust way of extracting useful information from uncertain sensor data using only a time-invariant model dataset and the knowledge contained within an entire sensor network.
•A method is presented to map urban air quality in real time with low-cost sensors.•The method merges low-cost sensor data with time-invariant model output.•Resulting maps exhibit realistic spatial patterns of urban air pollution.•Comparison against station data shows good replication of temporal daily cycle.•Validation indicates an R2 value of 0.89 against reference observations.
In 2022, the European Union introduced the Digital Services Act (DSA), a new legislation to report and moderate harmful content from online social networks. Trusted flaggers are mandated to identify ...harmful content, which platforms must remove within a set delay (currently 24 h). Here, we analyze the likely effectiveness of EU-mandated mechanisms for regulating highly viral online content with short half-lives. We deploy self-exciting point processes to determine the relationship between the regulated moderation delay and the likely harm reduction achieved. We find that harm reduction is achievable for the most harmful content, even for fast-paced platforms such as Twitter. Our method estimates moderation effectiveness for a given platform and provides a rule of thumb for selecting content for investigation and flagging, managing flaggers' workload.
We propose the combination of forward shape derivatives and the use of an iterative inversion scheme for Bayesian optimization to find optimal designs of nanophotonic devices. This approach widens ...the range of applicability of Bayesian optmization to situations where a larger number of iterations is required and where derivative information is available. This was previously impractical because the computational efforts required to identify the next evaluation point in the parameter space became much larger than the actual evaluation of the objective function. We demonstrate an implementation of the method by optimizing a waveguide edge coupler.
Numerical optimization is an important tool in the field of computational physics in general and in nano-optics in specific. It has attracted attention with the increase in complexity of structures ...that can be realized with current nanofabrication technologies for which a rational design is no longer feasible. Also, numerical resources are available to enable the computational photonic material design and to identify structures that meet predefined optical properties for specific applications. However, the optimization objective function is in general nonconvex and its computation remains resource demanding such that the right choice for the optimization method is crucial to obtain excellent results. Here, we benchmark five global optimization methods for three typical nano-optical optimization problems: particle swarm optimization, differential evolution, and Bayesian optimization as well as multistart versions of downhill simplex optimization and the limited-memory Broyden–Fletcher–Goldfarb–Shanno (L-BFGS) algorithm. In the shown examples from the field of shape optimization and parameter reconstruction, Bayesian optimization, mainly known from machine learning applications, obtains significantly better results in a fraction of the run times of the other optimization methods.
A theoretical model is presented describing the confinement-induced resonances observed in the recent loss experiment of Haller et al. Phys. Rev. Lett. 104, 153203 (2010). These resonances originate ...from possible molecule formation due to the coupling of center-of-mass and relative motion. A corresponding model is verified by ab initio calculations and predicts the resonance positions in 1D as well as in 2D confinement in agreement with the experiment. This resolves the contradiction of the experimental observations to previous theoretical predictions.
X-ray tomography is an invaluable tool in biomedical imaging. It can deliver the three-dimensional internal structure of entire organisms as well as that of single cells, and even gives access to ...quantitative information, crucially important both for medical applications and for basic research. Most frequently such information is based on X-ray attenuation. Phase contrast is sometimes used for improved visibility but remains significantly harder to quantify. Here we describe an X-ray computed tomography technique that generates quantitative high-contrast three-dimensional electron density maps from phase contrast information without reverting to assumptions of a weak phase object or negligible absorption. This method uses a ptychographic coherent imaging approach to record tomographic data sets, exploiting both the high penetration power of hard X-rays and the high sensitivity of lensless imaging. As an example, we present images of a bone sample in which structures on the 100 nm length scale such as the osteocyte lacunae and the interconnective canalicular network are clearly resolved. The recovered electron density map provides a contrast high enough to estimate nanoscale bone density variations of less than one per cent. We expect this high-resolution tomography technique to provide invaluable information for both the life and materials sciences.
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