Deep learning architecture for air quality predictions

With the rapid development of urbanization and industrialization, many developing countries are suffering from heavy air pollution. Governments and citizens have expressed increasing concern regarding air pollution because it affects human health and sustainable development worldwide. Current air quality prediction methods mainly use shallow models; however, these methods produce unsatisfactory results, which inspired us to investigate methods of predicting air quality based on deep architecture models. In this paper, a novel spatiotemporal deep learning (STDL)-based air quality prediction method that inherently considers spatial and temporal correlations is proposed. A stacked autoencoder (SAE) model is used to extract inherent air quality features, and it is trained in a greedy layer-wise manner. Compared with traditional time series prediction models, our model can predict the air quality of all stations simultaneously and shows the temporal stability in all seasons. Moreover, a comparison with the spatiotemporal artificial neural network (STANN), auto regression moving average (ARMA), and support vector regression (SVR) models demonstrates that the proposed method of performing air quality predictions has a superior performance.     

Gliding arc plasma processing for decomposition of chloroform

The decomposition of chloroform (CHCl₃) diluted in air was studied. The experiment was carried out by using a gliding arc plasma. Different values of initial concentrations of chloroform, total gas flow rates, and input power frequencies have been used to investigate this effects on the conversion reaction products both qualitatively and quantitatively. Experimental results indicate that the maximum conversion of chloroform was 97% at a total gas flow rate of 180?L?h^?1 containing 1% chloroform. Using air as carrier gas, decomposition of CHCl₃ produces CCl₄, CO₂, CO, and Cl₂ as the main products. Small amounts of HCl and COCl₂ are also detected. Liquid products were also produced.     

The effect of preparation conditions of Pt/Al2O3 on its catalytic performance for the H2-SCR in the presence of oxygen

Selective catalytic reduction of NO _x by H₂ in the presence of oxygen has been investigated over Pt/ Al₂O₃ catalysts pre-treated under different conditions. Catalyst preparation conditions exert significant influence on the catalytic performance, and the catalyst pre-treated by H₂ or H₂ then followed by O₂ is much more active than that pre-treated by air. The higher surface area and the presence of metallic Pt over Pt/Al₂O₃ pre-treated by H₂ or pretreated by H₂ then followed by O₂ can contribute to the formation of NO₂, which then promotes the reaction to proceed at low temperatures.     

Space-time stick-breaking processes for small area disease cluster estimation

We propose a space-time stick-breaking process for the disease cluster estimation. The dependencies for spatial and temporal effects are introduced by using space-time covariate dependent kernel stick-breaking processes. We compared this model with the space-time standard random effect model by checking each model’s ability in terms of cluster detection of various shapes and sizes. This comparison was made for simulated data where the true risks were known. For the simulated data, we have observed that space-time stick-breaking process performs better in detecting medium- and high-risk clusters. For the real data, county specific low birth weight incidences for the state of South Carolina for the years 1997–2007, we have illustrated how the proposed model can be used to find grouping of counties of higher incidence rate.     

Phototransformation of <Emphasis Type="SmallCaps">l</Emphasis>-tryptophan and formation of humic substances in water

Humic substances are poorly known, though they represent a major pool of non-biotic organic carbon on earth. In particular, there is little knowledge on the formation of humic substances by irradiation of organic matter dissolved in waters. Specifically, it is known that humic substances can be formed from proteins by photochemical processes in surface waters, but the role of single amino acids and their transformation pathways are not yet known. Therefore, here we studied the phototransformation of aqueous l-tryptophan under simulated sunlight. Irradiated l-tryptophan solutions were analyzed by absorption, fluorescence, nuclear magnetic resonance (NMR) and electron paramagnetic resonance (EPR) spectroscopies, chromatography, potentiometry and mass spectrometry (MS). The solutions appeared turbid after irradiation; therefore, nephelometry and dynamic laser light scattering were used to characterize the suspended particles. Results show that about 95% of l-tryptophan was degraded in 8-h irradiation, undergoing deamination and decarboxylation of the amino acidic moieties to release ammonium and formate. The MS signal at m/z 146 suggests the formation of 3-ethylindole, while pH-metric and NMR data revealed the presence of hydroxylated compounds. The phototransformation intermediates of l-tryptophan had fluorescence and absorption spectra similar to those of humic substances, they were able to produce ^·OH upon irradiation and tended to aggregate by both ionic and hydrophobic interactions. Overall, our findings reveal for the first time the nature of products formed upon phototransformation of l-tryptophan. Interestingly, the transformation of l-tryptophan is quite different from that of the previously studied l-tyrosine, although both compounds produce humic-like materials under irradiation.     

Aerogels and metal–organic frameworks for environmental remediation and energy production

Environmental pollution and climate change are requiring new methods to clean pollutants and produce sustainable energy. Aerogels and metal organic frameworks are emerging as advanced porous materials with higher functionality, high surface area, high porosity and flexible chemistry. Aerogels are dried gels prepared using the sol–gel procedure, whereas metal organic frameworks are networks of organic ligands and metal ions connected by coordination bonds. Applications of aerogels include the removal of heavy metals, CO₂ capture and reduction, photodegradation of pollutants, air cleanup and water splitting. This article reviews the synthesis and types of aerogels and metal organic frameworks, and the application to pollutant removal, energy production including hydrogen, methane reforming, CO₂ conversion and NOx removal.     

Granulation of anammox microorganisms for autotrophic nitrogen removal from wastewater

Excessive discharge of nutrients in waters induces pollution such as such as eutrophication. Conventional methods to treat waters are expensive. Alternatively, anaerobic ammonium oxidation, termed “anammox”, has been recently developped with benefits such as low sludge production, 50% less aeration demand, no external carbon supply, 60% less power consumption, and 90% reduction of greenhouse gas emissions. However, anammox is limited by long start-up periods due to the low growth rate of anammox bacteria. This issue can be solved by complete retention of biomass by reactor modification or by formation of anammox granules. This article reviews the mechanisms of anammox granulation and biogranulation models. We present factors involved in the granulation processes such as hydrodynamic shear force, extracellular polymeric substances, hydraulic retention time, seed sludge and bioreactors. We also discuss the interaction of proteins and polysaccharides in anammox granules.     

Insights on plant interaction between dominating species from patterns of plant association: expected covariance of pin-point cover measurements of two species

It has been suggested that in order to infer ecological processes from observed patterns of species abundance we need to investigate the covariance in species abundance. Consequently, an expression for the expected covariance of pin-point cover measurements of two species is developed. By comparing the observed covariance with the expected covariance we get a new type of information on the spatial arrangement of two species. Here the discrepancy between the observed and expected covariance may be thought of as a measure of the spatial configuration of the two species that may indicate underling ecological processes. The method is applied in a case study of Calluna vulgaris and Deschampsia flexuosa on dry heathland sites. The observed covariance of Calluna and Deschampsia at the level of the sites was positively and significantly correlated with the expected covariance. Negative covariance was observed on sites where both Calluna and Deschampsia had a high cover, which is in agreement with the notion that both species form distinct patches. Oppositely, at sites where both species have a low cover, we found that both the expected and observed covariance were positive. The proposed measure for the expected covariance of two species does capture information on the combined spatial configuration of the two species if the species are common. We show how this may be relevant for understanding the underlying ecological processes leading to the observed covariance.     

Halton iterative partitioning: spatially balanced sampling via partitioning

A new spatially balanced sampling design for environmental surveys is introduced, called Halton iterative partitioning (HIP). The design draws sample locations that are well spread over the study area. Spatially balanced designs are known to be efficient when surveying natural resources because nearby locations tend to be similar. The HIP design uses structural properties of the Halton sequence to partition a resource into nested boxes. Sample locations are then drawn from specific boxes in the partition to ensure spatial diversity. The method is conceptually simple and computationally efficient, draws spatially balanced samples in two or more dimensions and uses standard design-based estimators. Furthermore, HIP samples have an implicit ordering that can be used to define spatially balanced over-samples. This feature is particularly useful when sampling natural resources because we can dynamically add spatially balanced units from the over-sample to the sample as non-target or inaccessible units are discovered. We use several populations to show that HIP sampling draws spatially balanced samples and gives precise estimates of population totals.