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In recent years, Industrial Internet of Things (IIoT) has gradually attracted the attention of the industry owing to its accurate time synchronization, communication accuracy, and high adaptability. As an important data structure, graphs are widely used in IIoT applications, where entities and their relationships can be expressed in the form of graphs. With the widespread adoption of IIoT and cloud computing, an increasing number of individuals or organizations are outsourcing their IIoT graph data to cloud servers to enjoy the unlimited storage space and fast computing service. To protect the privacy of graph data, graphs are usually encrypted before being outsourced. In this article, we propose a search pattern privacy system for approximate shortest distance query of encrypted graphs in IIoT. To realize search pattern privacy, we adopt two noncolluded cloud servers to accomplish different tasks. We leverage the first server to store the encrypted data and perform query operations, and use the second one to rerandomize the contents and shuffle the locations of the queried records. Before queries, we generate the trapdoors by using different random numbers. After queries, we ask the second server to rerandomize the contents of the records that the first server touched. In addition, we shuffle the physical locations of original records by inserting some fake records. In this way, all contents and physical locations of the touched records change, so that the first server cannot distinguish whether two queries are the same or not. To enhance the efficiency on the user side, we further improve this system by moving some heavy workloads from the user to the cloud. The security analysis and the performance evaluation show that our work is secure and efficient. [ABSTRACT FROM AUTHOR]

• A dynamic ammonia emission model was further developed and applied to Germany. • Meteorology, manure properties, and application methods were accounted for. • Emission fractions and timing differ between years mainly due to meteorology. • Modeling results better reflect the sensitivity of ammonia levels to temperature. • Both dynamic timing and emission fractions contribute to the improvement. Ammonia is one of the most impactful pollutants emitted from agricultural activities, harming human health and contributing to biodiversity loss. In ammonia emission inventories, the spatial distribution of annual emissions is mostly approximated by constant empirical emission fractions, which do not account for spatial variability, nor for temporal variability within a year or between years caused by weather variations. Besides, factors like manure properties, soil properties, and manure application techniques also lead to differences in the amount of ammonia emitted into the atmosphere. By not or only partly accounting for these factors, significant uncertainties are introduced into ammonia emission estimates at regional and national scales. In this study, we applied the empirical ALFAM2 model to derive spatially explicit slurry application emission fractions from cropland for use in the large-scale INTEGRATOR model, using the information on slurry properties (dry matter content and pH), manure application rate, application technique, incorporation time, air temperature, wind speed, and rainfall rate. In addition, the impact of weather on the ammonia emissions from animal housing and manure storage systems was included through a temperature-dependent scaling. We applied the method to investigate the year-to-year spatio-temporal variabilities of ammonia emissions and modeled concentrations across Germany from 2015 to 2018. Through the comparison with in situ measurements and satellite-derived observations, we studied how surface concentrations and total columns relate to local meteorology. We found that the spatio-temporal variability in emission fractions improves the ability to reproduce the interannual variability observed in ammonia concentration and total column measurements. This study shows that the developed approach to derive spatially explicit emission fractions can significantly improve ammonia emission modeling and is of great importance for studying the temporal variability between years. [ABSTRACT FROM AUTHOR]

This paper presents the retrieval method that was developed to derive tropospheric NO2 columns from UV/VIS spectral measurements obtained with the Spectrolite Breadboard Instrument during the AROMAPEX campaign in Berlin (April 2016). A typical DOAS retrieval approach is followed. For the calculation of air mass factors this study specifically focuses on the impact of the surface reflectance, which varies considerably from pixel to pixel over this urban region. Ground-based aerosol optical thickness measurements are used as prior information. It is shown that retrieved surface reflectance shows good agreement with those derived from Landsat 8 measurements performed on the same day. Furthermore we demonstrate that tropospheric NO2 columns retrieved for pairs of adjacent pixels are self-consistent in the sense that they do not show a substantial systematic dependence on surface reflectance, in contrast to differential slant column densities. Also some cases are identified to illustrate this on a pixel-by-pixel level. An error budget is provided to quantify the impact of various assumptions on the accuracy of the retrieval of surface reflectance and tropospheric NO2 columns. Both in the morning and afternoon flight a NO2 plume is observed stretching out over Berlin from West to East. Peak values between 15 × 1015 and 20 × 1015 molec/cm² are detected, whereas - at much lower spatial resolution - OMI detects peak values between 9 × 1015 (first overpass) and 4 × 1015 molec/cm² (second overpass). [ABSTRACT FROM AUTHOR]

Nowadays, cloud-assisted e-healthcare systems are playing a more and more important role in intelligent medical services. By uploading Electronic Medical Records (EMRs) to the cloud, users can facilitate data sharing and decrease local data management overhead. Before EMRs are outsourced to the cloud, the sensitive information in them is usually encrypted for protecting the privacy. Though the techniques of traditional searchable encryption could achieve the retrieval of ciphertext, it results in EMRs unable to be shared with external researchers. Moreover, the existing searchable encryption schemes for cloud-assisted e-healthcare systems rarely consider fault-tolerant search and result verification. To address these issues, this paper proposes a verifiable fuzzy keyword search scheme supporting sensitive information hiding for data sharing in cloud-assisted e-healthcare systems. We encrypt sensitive information and share the rest information in EMRs among all users. We improve the approaches of keyword transformation to support the type of keyword in EMRs and realize fuzzy keyword search. For purpose of accomplishing the public verification, we adopt the BLS signature algorithm to generate two kinds of verification tags. We also generate the auxiliary match information to realize condition search. To enhance search efficiency, we build a secure index based on the balanced binary tree. In addition, the proposed scheme can also achieve dynamic update in real-time. We analyze the security and implement a series of experiments for evaluating the effectiveness of this scheme. The experimental results illustrate our scheme could reach high accuracy. [ABSTRACT FROM AUTHOR]

We present an intercomparison study of four airborne imaging DOAS instruments, dedicated to the retrieval and high-resolution mapping of tropospheric nitrogen dioxide (NO2) vertical column densities (VCDs). The AROMAPEX campaign took place in Berlin, Germany, in April 2016 with the primary objective to test and intercompare the performance of experimental airborne imagers. The imaging DOAS instruments were operated simultaneously from two manned aircraft, performing synchronised flights: APEX (VITO–BIRA-IASB) was operated from DLR's DO-228 D-CFFU aircraft at 6.2 km in altitude, while AirMAP (IUP-Bremen), SWING (BIRA-IASB), and SBI (TNO–TU Delft–KNMI) were operated from the FUB Cessna 207T D-EAFU at 3.1 km. Two synchronised flights took place on 21 April 2016. NO2 slant columns were retrieved by applying differential optical absorption spectroscopy (DOAS) in the visible wavelength region and converted to VCDs by the computation of appropriate air mass factors (AMFs). Finally, the NO2 VCDs were georeferenced and mapped at high spatial resolution. For the sake of harmonising the different data sets, efforts were made to agree on a common set of parameter settings, AMF look-up table, and gridding algorithm. The NO2 horizontal distribution, observed by the different DOAS imagers, shows very similar spatial patterns. The NO2 field is dominated by two large plumes related to industrial compounds, crossing the city from west to east. The major highways A100 and A113 are also identified as line sources of NO2. Retrieved NO2 VCDs range between 1×1015 molec cm -2 upwind of the city and 20×1015 molec cm -2 in the dominant plume, with a mean of 7.3±1.8×1015 molec cm -2 for the morning flight and between 1 and 23×1015 molec cm -2 with a mean of 6.0±1.4×1015 molec cm -2 for the afternoon flight. The mean NO2 VCD retrieval errors are in the range of 22 % to 36 % for all sensors. The four data sets are in good agreement with Pearson correlation coefficients better than 0.9, while the linear regression analyses show slopes close to unity and generally small intercepts. [ABSTRACT FROM AUTHOR]