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Background: Fungi are one of the main causes of degradation in feeds and foodstuffs. Fungal infections in plants can result in reduced yield and quality of the products, which lead to economic losses. AflatoxinB1 (AFB1) is a secondary fungal metabolite produced in proper conditions. It is considered as a public health concern due to its adverse effect on human and animal. This study was carried out to determination of AFB1 in wheat flour and wheat bran samples of Alborz province factories. Methods: A total of 180 wheat flour and 60 wheat bran samples were collected from 20 Alborz province factories. HPLC method with immune-affinity chromatography was applied to measure AFB1 in three types of flour including Barbary, Tafton, and Sangag (A, B, and C). It was performed both in different manufactures and within three groups of flours regardless of the type of manufactures. Statistical analysis was conducted by the One-way ANOVA with post-hoc test. Results: According to the findings, 144 out of 180 wheat flour samples and 54 out of 60 wheat bran samples were found to be contaminated with AFB1. The amount of AFB1 in wheat flour and wheat bran samples was detected to be 0.01-0.5 ng/g and 0.06-0.99 ng/g, respectively. The highest means of AFB1 in Sangag, Barbary, and Tafton flours from different manufactures were 0.5, 0.4 and, 0.3 ng/g, respectively. In addition, no significant difference was found within three groups of Barbary (A), Tafton (B), and Sangag (C) flours samples (p>0.05). Despite the contamination of wheat flour and wheat bran samples, there was no contamination higher than the standard limit of Iran Standard Institute (ISRI) and European Union (EU) 2 ng/g, 5 ng/g, respectively. Conclusions: Since wheat flours are widely used in foods preparation, determination of contamination with AFB1 in wheat-based products is highly essential in order to prevent the potential adverse effects on health. As the level of AB1 in the current study was low, it is not a serious concern for consumers at this time.

Abstract We present unprecedented datasets of current and future projected weather files for building simulations in 15 major cities distributed across 10 climate zones worldwide. The datasets include ambient air temperature, relative humidity, atmospheric pressure, direct and diffuse solar irradiance, and wind speed at hourly resolution, which are essential climate elements needed to undertake building simulations. The datasets contain typical and extreme weather years in the EnergyPlus weather file (EPW) format and multiyear projections in comma-separated value (CSV) format for three periods: historical (2001–2020), future mid-term (2041–2060), and future long-term (2081–2100). The datasets were generated from projections of one regional climate model, which were bias-corrected using multiyear observational data for each city. The methodology used makes the datasets among the first to incorporate complex changes in the future climate for the frequency, duration, and magnitude of extreme temperatures. These datasets, created within the IEA EBC Annex 80 “Resilient Cooling for Buildings”, are ready to be used for different types of building adaptation and resilience studies to climate change and heatwaves.

The analysis of the near-surface air temperature is vital for many applications such as urban heat islands and climate change studies. In particular, extreme weather events are typically localized and so should the corresponding adaptation measures. However, climate scientists are often confronted with the difficulty of providing reliable predictions at high spatial resolutions in the order of 1 km. We propose to train a convolutional neural network model to emulate the hourly high-resolution near-surface air temperature field simulated by the Weather Research and Forecasting (WRF) software over a period of 18 months. The model is driven by current and past lags of coarse SEVIRI land surface temperature fields. This nowcasting application implements a downscaling of the spatial resolution of the input by about a factor of four, while establishing a correlation between current and past land surface temperature maps and the current near-surface air temperature field. The U-Net variant that is proposed in this study uses regularization to prevent over-fitting and implements a novel space-time approach, where multiple time steps are fed into the model through 3D convolution layers. Besides LST, the model also uses urban density as additional static input to be able to predict the temperature more accurately in urban areas and to improve the generalizability of the trained model. The performance of our U-Net model is assessed via comparison to an MLR benchmark (ridge regularization). The model is shown to be superior on all performance metrics. It achieves a mean absolute error of 1.36 °C versus 1.49 °C for benchmark (a 9% relative improvement) and a root mean square error of 1.77 °C versus 1.91 °C for benchmark (a 7% relative improvement). The absolute error of the model is less than 2 °C for 77% of the prediction points versus 72% for the benchmark (a 7% relative improvement). The improvement over the benchmark is even more significant during extreme hot periods. We demonstrate the generalizability of the approach by testing the trained model on unseen spatial domains.

Horizontally averaged wind profiles inside the urban canopy are used in many studies and numerical models. The existing analytical models are only applicable to a small range of aspect ratios and mostly neutral atmospheric conditions due to their underlying assumptions. In this study, a surrogate model for predicting horizontally averaged wind profiles in the street canyons of an idealized urban canopy for a wide range of urban morphologies and thermal forcing scenarios is developed with the help of machine learning techniques and computational fluid dynamics (CFD) simulation data. The influence of urban morphological parameters, atmospheric stability and wind conditions on the urban canopy wind flow is modeled using machine learning algorithms applied on CFD simulation results. The numerical model is validated using wind-tunnel data. Steady-state Reynolds averaged Navier–Stokes (RANS) simulations with a standard k-ϵ turbulence model for 252 different simulation conditions are performed on an idealized building geometry that consists of a regular array of 4 × 4 cubes. The simulation results are averaged horizontally to obtain the mean velocity and temperature profiles. Surrogate models are developed using the simulation outputs as training examples and the best model is chosen by comparing the performance of different machine learning models. The surrogate artificial neural network (ANN) model of this study outperforms the current state-of-the-art models in the prediction of horizontally averaged mean wind profile inside the urban canopy. The mean error (ME) and root-mean-square error (RMSE) of the discrete point prediction ANN model of this study are 0.016 m/s and 0.060 m/s, respectively, which is significantly lower compared to the best of the legacy models for which the errors are 0.048 m/s and 0.387 m/s, respectively.

A GIS-based approach is used in this study to obtain a better LCZ map of Berlin in comparison to the remote-sensing-based WUDAPT L0 approach. The LCZ classification of land use/cover can be used, among other applications, to characterize the urban heat island. An improved fuzzy logic method is employed for the purpose of classification of the zone properties to yield the GIS-LCZ map over 100 m × 100 m grid tiles covering the Berlin region. The zone properties are calculated from raster and vector datasets with the aids of the urban multi-scale environmental predictor (UMEP), QGIS and Python scripts. The standard framework is modified by reducing the threshold for the zone property impervious fraction for LCZ E to better detect paved surfaces in urban areas. Another modification is the reduction in the window size in the majority filter during post-processing, compared to the WUDAPT L0 method, to retain more details in the GIS-LCZ map. Moreover, new training areas are generated considering building height information. The result of the GIS-LCZ approach is compared to the new training areas for accuracy assessment, which shows better overall accuracy compared to that of the WUDAPT L0 method. The new training areas are also submitted to the LCZ generator and the resulting LCZ-map gives a better overall accuracy value compared to the previous (WUDAPT) submission. This study shows one shortcoming of the WUDAPT L0 method: it does not explicitly use building height information and that leads to misclassification of LCZs in several cases. The GIS-LCZ method addresses this shortcoming effectively. Finally, an unsupervised machine learning method, k-means clustering, is applied to cluster the grid tiles according to their zone properties into custom classes. The custom clusters are compared to the GIS-LCZ classes and the results indicate that k-means clustering can identify more complex city-specific classes or LCZ transition types, while the GIS-LCZ method always divides regions into the standard LCZ classes.

Characterizing the urban form of cities from an aerodynamic point of view is essential to forecast pollutant dispersion, to implement and understand natural ventilation strategies or to enhance outdoor thermal comfort and pedestrian wind environment, to name a few. Nonetheless, accurate results make use, normally, of Computational Fluid Dynamic (CFD) simulations, wind tunnel experiments and/or field measurements. However, these methodologies are expensive, time consuming and sometimes impossible to carry out. Therefore, simplified ways of characterizing a city aerodynamically must be put forward to bridge this gap. Two of the most common aerodynamic parameters, namely z0 (roughness length) and zd (zero displacement length), can be related to the morphometry of the urban shape through simple morphometric indexes. This paper explores and analyses these indexes, and suggests an alternative and improved definition that might achieve more accurate results. Resumen Caracterizar la forma urbana de las ciudades desde un punto de vista aerodinámico es esencial para poder prever la dispersión de contaminantes, para implementar y comprender las estrategias de ventilación natural o para mejorar el control térmico en exteriores o el ambiente de viento peatonal, por citar sólo algunos casos. Para cualquiera de estos análisis, si se desea disponer de valores de cierta precisión es habitual emplear simulaciones mediante dinámica de fluidos computacional (CFD), experimentos en túnel de viento y/o tomas de datos de campo. Estos métodos, sin embargo, implican una inversión económica y temporal que en ocasiones no es posible realizar. El empleo de métodos simplificados para caracterizar la ciudad desde un punto de vista aerodinámico puede ayudar a hacer más sencillos estos procesos. Dos de los parámetros aerodinámicos más habituales, z0 (rugosidad superficial) y zd (longitud desplazamiento cero) pueden ponerse en relación con la morfometría de la forma urbana a través de índices morfométricos simples. Esta comunicación explora y analiza estos índices, y sugiere una definición alternativa y mejorada que puede posibilitar resultados más ajustados.

In this study, we present a meso-scale simulation of the urban microclimate in Berlin, Germany, using the Weather Research and Forecasting (WRF) numerical weather prediction platform. The objective of the study is to derive an accurate estimate of the near-surface urban heat island (UHI) intensity. The simulation is conducted over a two-week summer period. We compare different physical schemes, different urban canopy schemes and different methods for estimating the UHI intensity. The urban fraction of each urban category is derived using the Copernicus Impervious Density data and the Corine Land Cover data. High-resolution City Geography Markup Language (CityGML) data is used to estimate the building height densities required by the multi-layer urban canopy model (UCM). Within the single-layer UCM, we implement an anthropogenic heat profile based on the large scale urban consumption of energy (LUCY) model. The optimal model configuration combines the WRF Single Moment Five-Class (WSM5) microphysics scheme, the Bougeault–Lacarrère planetary boundary layer scheme, the eta similarity (Mellor–Yamada–Janjic) surface layer scheme, the Noah Multi-Parameterization land surface model, the Dudhia and Rapid Radiative Transfer Model (RRTM) radiation schemes, and the multi-layer UCM (including the building energy model). Our simulated UHI intensity results agree well with measurements with a root mean squared error of 0.86K and a mean bias error of 0.20K. After model validation, we proceed to compare several UHI intensity calculation methods, including the ‘ring rural reference’ (RRR) method and the ‘virtual rural reference’ (VRR) method. The VRR mthod is also known as the ‘urban increment’ method. We suggest and argument that the VRR approach is superior.

Background: Toxoplasma specific IgM antibodies; the common serologic marker in diagnosis of acute toxoplasmosis has its own limitations. Confirmatory testing with other markers, introduced as a complementary tool in distinguish acute and chronic infections is unusual in Iran. In the present study, we investigated the cor-relation between the results of IgM ELISA, IgA ELISA, and IgG avidity tests in the diagnosis of toxoplasmosis to demonstrate the necessity of confirmatory testing in serodiagnosis of infection in the country. Methods: A total of 107 positive Toxoplasma IgG and IgM sera were obtained from patients referred to private laboratories and stored at -20 ºC for futures use. Sero-logic tests were set up in duplicate to analyze the serum levels of IgG, IgM, IgA, and IgG avidity antibodies using commercial ELISA kits. The results were pre-sented as semi quantitative for IgG, IgM and IgA ELISA, and Relative Avidity In-dex in percentage for IgG avidity test. Pearson’s correlation coefficient (rp) was applied to analyze the data. Results: Of 107 serum samples, T. gondii specific IgM and IgA antibodies were positive in 67.3% and 53.3%, respectively. Besides, 29.9% of the sera displayed low avidity for IgG antibodies. The rp was - 0.572 (P

The measurement of anti-streptokinase antibodies can distinguish the patients who may benefit from streptokinase and those who should be treated with some other thrombolytic regimen. Neutralising titration test is a commonly used classical assay for measuring anti-streptokinase antibodies and in this assay the ability of anti-streptokinase antibodies in patients' sera in preventing the lytic effect of streptokinase is assessd. As we showed previously the presence of r-tPA in the serum may interefere with the neutralising titration test, we investigated this interference in vitro. The level of neutralising anti-streptokinase antibodies in a serum sample with known levels of the antibodies were measured in absence and presence of increasing amount of r-tPA including therapeutic values. Increasing amount of r-tPA in vitro induced a sudden reduction in the measurable titre of anti-streptokinase antibody levels in a serum with elevated levels of anti-streptokinase antibodies. This effect of r-tPA, which is through activation of plasminogen has not been reported previously. We suggest this assay is unsuitable for clinical diagnosis of anti-streptokinase antibodies.