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Mapping of air temperature (Ta) at high spatiotemporal resolution is critical to reducing exposure assessment errors in epidemiological studies on the health effects of air temperature. In this study, we applied a three-stage ensemble model to estimate daily mean Ta from satellite-based land surface temperature (Ts) over Sweden during 2001-2019 at a high spatial resolution of 1 × 1 km2. The ensemble model incorporated four base models, including a generalized additive model (GAM), a generalized additive mixed model (GAMM), and two machine learning models (random forest [RF] and extreme gradient boosting [XGBoost]), and allowed the weights for each model to vary over space, with the best-performing model for each grid cell assigned the highest weight. [https://www.selleckchem.com/ selleck chemical] Various spatial predictors were included as adjustment variables in all the base models, including land cover type, normalized difference vegetation index (NDVI), and elevation. The ensemble model showed high performance with an overall R2 of 0.98 and a root mean square error of 1.38 °C in the ten-fold cross-validation, and outperformed each of the four base models. Although each base model performed well, the two machine learning models (RF [R2 = 0.97], XGBoost [R2 = 0.98]) had better performance than the two regression models (GAM [R2 = 0.95], GAMM [R2 = 0.96]). In the machine learning models, Ts was the dominant predictor of Ta, followed by day of year, NDVI, latitude, elevation, and longitude. The highly spatiotemporally-resolved Ta can improve temperature exposure assessment in future epidemiological studies.India has suffered from the second wave of COVID-19 pandemic since March 2021. This wave of the outbreak has been more serious than the first wave pandemic in 2020, which suggests that some new transmission characteristics may exist. COVID-19 is transmitted through droplets, aerosols, and contact with infected surfaces. Air pollutants are also considered to be associated with COVID-19 transmission. However, the roles of indoor transmission in the COVID-19 pandemic and the effects of these factors in indoor environments are still poorly understood. Our study focused on reveal the role of indoor transmission in the second wave of COVID-19 pandemic in India. Our results indicated that human mobility in the home environment had the highest relative influence on COVID-19 daily growth rate in the country. The COVID-19 daily growth rate was significantly positively correlated with the residential percent rate in most state-level areas in India. A significant positive nonlinear relationship was found when the residential percent ratio ranged from 100 to 120%. Further, epidemic dynamics modelling indicated that a higher proportion of indoor transmission in the home environment was able to intensify the severity of the second wave of COVID-19 pandemic in India. Our findings suggested that more attention should be paid to the indoor transmission in home environment. The public health strategies to reduce indoor transmission such as ventilation and centralized isolation will be beneficial to the prevention and control of COVID-19.One of the main causes for Alzheimer disease is the abnormal self-assembly of the amyloid-beta (Aβ) peptide, which in turn forms a toxic β-rich aggregation. A recent study suggests that gold nanoparticles (AuNPs) can inhibit the Aβ aggregation. Nevertheless, the effects of AuNPs on Aβ peptide system are still ambiguous and needs exploration that is more detailed. Molecular dynamics simulations have been carried out to investigate the aggregation mechanism of Aβ42 peptide for 500 ns. During simulation, C-terminus regions of Met 35-Ala42 residues exhibits β-sheet conformations. Meanwhile, the Au144MC coordination induces substantial α-helical character, both α-helix and 310-helix structure at 0-500ns, in the region of Asp1-Arg5 and Val36-Ile41 residues. The Au144MC strongly coordinates with Asp1, Ala2, Glu3, Phe4, Asp7, Tyr10 and Gln15 residues that plays the significant effects to loss the β-sheet geometry in the N-terminal region and it converted into random α-helix, turn and bend conformation. On comparing the RMSF of the Aβ42 peptide and Aβ42-Au144MC complex shows that the coordination of Au144MC results in greater rigidity of the Aβ42 peptide backbone regions with exemptions for the Asp1, Ala2, Glu3, Leu34, Ile41 and Ala42 residues due to the strong binding between the metal cluster and the CHC (Leu17-Ala21) region. The structural stability of the Aβ42 peptide and Aβ42-Au144MC complex is enhanced by the several intermolecular and intramolecular interactions and it was visibly revealed in the H-bond. From the above results, it is very evident that the Au144MC can be used as inhibitor agent for the oligomerization of Aβ42.Dehydration of food waste is a technique in which food waste is dewatered to form a low moisture product. This research characterised the physicochemical properties of different dehydrated food waste products and examined their value in improving physical, biological, and chemical properties of soils. Dehydrated food waste products were slightly acidic (4.7-5.1) with high levels of electrical conductivity (EC) (4.83-7.64 mS cm-1). The products were composed of complex carbohydrates, polysaccharides, alcohols, phenols, carboxylic acid, lipids, and fats and contained high levels of total and available nutrients. Dehydrated food wastes slightly impacted the soil pH; however, they significantly increased soil EC, which may cause soil salinity when applied repeatedly. The food waste products also increased macro-nutrients (N, P, and K) for plants across different soil types. Carbon and nutrients in dehydrated food waste increased microbial activity, measured by basal respiration. Delayed germination and reduced plant growth of corn (Zea mays) and wheat (Triticum aestivum) plants were observed at high application rates of dehydrated food waste. This may have resulted from a combination of phytotoxins, anoxic conditions, salinity as well as the water-repellent nature of dehydrated food waste. However, release of nutrients increased nutrient uptake and plant biomass in corn and wheat plants at low levels of food waste application. The dehydrated food waste products may require composting prior to soil application or incorporation into soil for a long duration prior to planting. These processes will overcome the limitations of phytotoxins, anoxic conditions, salinity, and water repellence. Further work is required to optimise the levels of dehydrated food waste application to improve soil health and crop productivity.Ongoing climate variability and change is impacting pollen exposure dynamics among sensitive populations. However, pollen data that can provide beneficial information to allergy experts and patients alike remains elusive. The lack of high spatial resolution pollen data has resulted in a growing interest in using phenology information that is derived using satellite observations to infer key pollen events including start of pollen season (SPS), timing of peak pollen season (PPS), and length of pollen season (LPS). However, it remains unclear if the agreement between satellite-based phenology information (e.g. start of season SOS) and the in-situ pollen dynamics vary based on the type of satellite product itself or the processing methods used. To address this, we investigated the relationship between vegetation phenology indicator (SOS) derived from two separate sensor/satellite observations (MODIS, Landsat), and two different processing methods (double logistic regression (DLM) vs hybrid piecewise logistic regression (HPLM)) with in-situ pollen season dynamics (SPS, PPS, LPS) for three dominant allergenic tree pollen species (birch, oak, and poplar) that dominate the springtime allergy season in North America. Our results showed that irrespective of the data processing method (i.e. DLM vs HPLM), the MODIS-based SOS to be more closely aligned with the in-situ SPS, and PPS while upscaled Landsat based SOS had a better precision. The data products obtained using DLM processing methods tended to perform better than the HPLM based methods. We further showed that MODIS based phenology information along with temperature and latitude can be used to infer in-situ pollen dynamic for tree pollen during spring time. Our findings suggest that satellite-based phenology information may be useful in the development of early warning systems for allergic diseases.The application of rhamnolipids in a fungal-cultured biotrickling filter (BTF) has a significant impact on toluene removal. Two BTFs were used; BTF-A, a control bed, and BTF-B fed with rhamnolipids. The effect of empty bed residence times (EBRTs) on toluene bioavailability was investigated. Removal of toluene was carried out at EBRTs of 30 and 60 s and inlet loading rates (LRs) of 23-184 g m-3 h-1. At 30 s EBRT, when inlet LR was increased from 23 to 184 g m-3 h-1, the removal efficiency (RE) decreased from 93% to 50% for the control bed, and from 94% to 87% for BTF-B. Increasing the EBRT simultaneously with inlet LRs, confirms that BTF-A was diffusion-limited by registering a RE of 62% for toluene inlet LR of 184 g m-3 h-1, whereas BTF-B, achieved RE &gt; 96%, confirming a significant improvement in toluene biodegradability. Overall, the best performance was observed at 60 s EBRT and inlet LR of 184 g m-3 h-1, providing a maximum elimination capacity (EC) of 176.8 g m-3 h-1 under steady-state conditions. While a maximum EC of 114 g m-3 h-1 was observed under the same conditions in the absence of rhamnolipids (BTF-A). Measurements of critical micelle concentration showed that 150 mg L-1 of rhamnolipids demonstrated the lowest aqueous surface tension and maximum formation of micelles, while 175 mg L-1 was the optimum dose for fungal growth. Production rate of carbon dioxide, and dissolved oxygen contents highlighted the positive influence of rhamnolipids on adhesive forces, improved toluene mineralization, and promotion of microbial motility over mobility.Membrane distillation (MD) is considered as a promising and attractive technology due to its effective production of fresh water. However, the low permeability and easy wetting of MD membranes limit its practical applications. Herein carbon nanotubes (CNTs) and polyvinylidene fluoride-co-hexafluoropropylene (PcH) were used to fabricate nanofiber membranes by electrospinning. Effects of heat-press temperature and CNTs concentration on the morphology and performance of the as-fabricated membranes were systematically investigated. Dye rejections of CNTs/PcH membranes were also studied and role of CNTs played in the as-prepared MD membranes were analyzed. Results suggest that heat-press treatment effectively improved the mechanical strength as well as liquid entry pressure of membranes, and the optimal heat-press temperature was 150 °C. CNTs were proved to be successfully blended in nanofibers. Hydrophobicity and mechanical strength of membranes increased with CNTs incorporation. The 0.5 wt % CNTs loaded membrane heat-pressed at 150 °C exhibited the highest permeate flux (16.5-18.5 L m-2 h-1), which signified an increase of 42-50 % compared to the commercial MD membrane (11-13 L m-2 h-1) when 35 and 70 g L-1 NaCl solutions were used as feed solutions, respectively. It was noteworthy that salt rejection efficiencies of tested membranes achieved more than 99.99 %. When CNTs/PcH nanofiber membrane was applied to the treatment of dyeing wastewater, the removal rates of acid red and acid yellow reached 100 %. The removal rates of methylene blue and crystal violet were 99.41 % and 99.91 %, respectively. The present study suggested that the as-prepared membranes showed high potential towards MD application.