CRISPR/Cas12a-Assisted Visual Logic-Gate Detection of Pathogenic Microorganisms Based on Water-Soluble DNA-Binding AIEgens

Here, we developed a rapid, visual and double-checked Logic Gate detection platform for detection of pathogenic microorganisms by aggregation-induced emission luminogens (AIEgens) in combination with Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)/CRISPR associated (Cas). DNA light-up AIEgens (1,1,2,2-tetrakis[4-(2-bromo-ethoxy) phenyl]ethene, TTAPE) was non-emissive but the emission was turned on in the presence of large amount of DNA produced by recombinase polymerase amplification (RPA). When CRISPR/Cas12a was added, all long-stranded DNA were cut leading to the emission quenched. Thus, a method that can directly observe the emission changes with the naked eye has been successfully constructed. The detection is speedy within only 20 min, and has strong specificity to the target. The result can be judged by Logic Gate. Only when the output signal is (1,0), does it represent the presence of pathogenic microorganisms in the test object. Finally, the method was applied to the detect pathogenic microorganisms in environmental water samples, which proved that this method has high selectivity, specificity and applicability for the detection of pathogenic microorganisms in environmental water samples.

Climate change penalty and benefit on surface ozone: A global perspective based on CMIP6 earth system models

This work presents an analysis of the effect of climate change on surface ozone discussing the related penalties and benefits around the globe from the global modeling perspective based on simulations with five CMIP6 (Coupled Model Intercomparison Project Phase 6) Earth System Models. As part of AerChemMIP (Aerosol Chemistry Model Intercomparison Project) all models conducted simulation experiments considering future climate (ssp370SST) and present-day climate (ssp370pdSST) under the same future emissions trajectory (SSP3-7.0). A multi-model global average climate change benefit on surface ozone of -0.96±0.07 ppbv oC-1 is calculated which is mainly linked to the dominating role of enhanced ozone destruction with higher water vapour abudances under a warmer climate. Over regions remote from pollution sources, there is a robust decline in mean surface ozone concentration on an annual basis as well as for boreal winter and summer varying spatially from -0.2 to -2 ppbv oC-1, with strongest decline over tropical oceanic regions. The implication is that over regions remote from pollution sources (except over the Arctic) there is a consistent climate change benefit for baseline ozone due to global warming. However, ozone increases over regions close to anthropogenic pollution sources or close to enhanced natural Biogenic Volatile Organic Compounds (BVOC) emission sources with a rate ranging regionally from 0.2 to 2 ppbv oC-1, implying a regional surface ozone penalty due to global warming. Overall, the future climate change enhances the efficiency of precursor emissions to generate surface ozone in polluted regions and thus the magnitude of this effect depends on the regional emission changes considered in this study within the SSP3_7.0 scenario. The comparison of the climate change impact effect on surface ozone versus the combined effect of climate and emission changes indicates the dominant role of precursor emission changes in projecting surface ozone concentrations under future climate change scenarios.

RADIOCARBON IN GLOBAL TROPOSPHERIC CARBON DIOXIDE

ABSTRACT Since the 1950s, observations of radiocarbon (14C) in tropospheric carbon dioxide (CO2) have been conducted in both hemispheres, documenting the so-called nuclear “bomb spike” and its transfer into the oceans and the terrestrial biosphere, the two compartments permanently exchanging carbon with the atmosphere. Results from the Heidelberg global network of Δ14C-CO2 observations are revisited here with respect to the insights and quantitative constraints they provided on these carbon exchange fluxes. The recent development of global and hemispheric trends of Δ14C-CO2 are further discussed in regard to their suitability to continue providing constraints for 14C-free fossil CO2 emission changes on the global and regional scale.

The effect of accounting for public holidays on the skills of the atmospheric composition model SILAM v.5.7

This study quantifies the impact of emission changes during public holidays on air quality (AQ) and analyses the added value of accounting for the holidays in AQ modelling. Spatial and temporal distributions of atmospheric concentrations of the major air pollutants (the main focus was on NO2, but we also included O3, CO, PM2.5, and SO2) were considered at the European scale for all public holidays of 2018. Particular attention was paid to the events with the most pronounced continental- or regional-scale impact: Christmas and New Year, Easter, May Day vacations, and the last days of Ramadan. The simulations were performed with the chemistry transport model SILAM v.5.7 (System for Integrated modeLling of Atmospheric coMposition). Three model runs were made: the baseline with no treatment of holidays, the run considering holidays as Sundays, and the run forcing 80 % reduction in emissions during holidays for the weekday-sensitive sectors. The emission scaling was applied on a country basis. The model predictions were compared with in situ observations collected by the European Environment Agency. The experiment showed that even conservative treatment of official holidays has a large positive impact on NOx (up to 30 % of reduction in the bias inhomogeneity during the holiday days) and improves the CO, PM2.5, and O3 predictions. In many cases, the sensitivity simulations suggested a greater emission reduction than the level of Sundays. An individual consideration of the holiday events in different countries may further improve their representation in the models: specific diurnal pattern of emissions, additional emission due to fireworks, and different driving patterns.

Machine Learning Methods Applied for Modeling the Process of Obtaining Bricks Using Silicon-Based Materials

Most of the time, industrial brick manufacture facilities are designed and commissioned for a particular type of manufacture mix and a particular type of burning process. Productivity and product quality maintenance and improvement is a challenge for process engineers. Our paper aims at using machine learning methods to evaluate the impact of adding new auxiliary materials on the amount of exhaust emissions. Experimental determinations made in similar conditions enabled us to build a database containing information about 121 brick batches. Various models (artificial neural networks and regression algorithms) were designed to make predictions about exhaust emission changes when auxiliary materials are introduced into the manufacture mix. The best models were feed-forward neural networks with two hidden layers, having MSE < 0.01 and r2 > 0.82 and, as regression model, kNN with error < 0.6. Also, an optimization procedure, including the best models, was developed in order to determine the optimal values for the parameters that assure the minimum quantities for the gas emission. The Pareto front obtained in the multi-objective optimization conducted with grid search method allows the user the chose the most convenient values for the dry product mass, clay, ash and organic raw materials which minimize gas emissions with energy potential.

Examining the competing effects of contemporary land management vs. land cover changes on global air quality

Our work explores the impact of two important dimensions of land system changes, land use and land cover change (LULCC) as well as direct agricultural reactive nitrogen (Nr) emissions from soils, on ozone (O3) and fine particulate matter (PM2.5) in terms of air quality over contemporary (1992 to 2014) timescales. We account for LULCC and agricultural Nr emissions changes with consistent remote sensing products and new global emission inventories respectively estimating their impacts on global surface O3 and PM2.5 concentrations as well as Nr deposition using the GEOS-Chem global chemical transport model. Over this time period, our model results show that agricultural Nr emission changes cause a reduction of annual mean PM2.5 levels over Europe and northern Asia (up to −2.1 µg m−3) while increasing PM2.5 levels in India, China and the eastern US (up to +3.5 µg m−3). Land cover changes induce small reductions in PM2.5 (up to −0.7 µg m−3) over Amazonia, China and India due to reduced biogenic volatile organic compound (BVOC) emissions and enhanced deposition of aerosol precursor gases (e.g., NO2, SO2). Agricultural Nr emission changes only lead to minor changes (up to ±0.6 ppbv) in annual mean surface O3 levels, mainly over China, India and Myanmar. Meanwhile, our model result suggests a stronger impact of LULCC on surface O3 over the time period across South America; the combination of changes in dry deposition and isoprene emissions results in −0.8 to +1.2 ppbv surface ozone changes. The enhancement of dry deposition reduces the surface ozone level (up to −1 ppbv) over southern China, the eastern US and central Africa. The enhancement of soil NO emission due to crop expansion also contributes to surface ozone changes (up to +0.6 ppbv) over sub-Saharan Africa. In certain regions, the combined effects of LULCC and agricultural Nr emission changes on O3 and PM2.5 air quality can be comparable (>20 %) to anthropogenic emission changes over the same time period. Finally, we calculate that the increase in global agricultural Nr emissions leads to a net increase in global land area (+3.67×106km2) that potentially faces exceedance of the critical Nr load (>5 kg N ha−1 yr−1). Our result demonstrates the impacts of contemporary LULCC and agricultural Nr emission changes on PM2.5 and O3 in terms of air quality, as well as the importance of land system changes for air quality over multidecadal timescales.

Environment-dependent Emission Tuning in the Multicolor Nitrogen-doped Carbon Quantum Dots

Carbon quantum dots (CQDs) are fascinating luminous materials from the carbonaceous family and are increasingly being investigated in many optoelectronic applications due to their unique photoluminescence (PL) characteristics. Herein, we report the synthesis of nitrogen-doped carbon quantum dots (NCQDs) from citric acid and m-phenylenediamine using a one-pot hydrothermal approach. The environment-dependent emission changes of NCQDs were extensively investigated in various solvents, in solid-state, and in physically assembled PMMA-PnBA-PMMA copolymer gels in 2-ethyl hexanol. The NCQDs display bright emission in various solvents as well as in solid-state and a temperature-dependent enhanced emission in gels. In detail, these NCQDs exhibit multicolor PL emission across the visible region and its enhancement upon changing the environment (solutions and polymer matrices). The NCQDs also exhibit excitation-dependent PL and solvatochromism, which are rarely observed in CQDs. Most CQDs are non-emissive in the aggregated or solid-state due to the aggregation-caused quenching (ACQ) effect, limiting their solid-state applications. However, these NCQDs display a strong solid-state emission centered at 568 nm ascribed to the presence of abundant surface functional groups, which helps to prevent the - interaction between the NCQDs and to overcome the ACQ effect in the solid-state. Interestingly, the NCQD containing gels display a significant fluorescence enhancement than the NCQDs in 2-ethyl hexanol solution because of the interaction between the polar PMMA blocks and NCQDs. This research opens up the development of large-scale, low-cost multicolor phosphor for the fabrication of optoelectronic devices, sensing, and bioimaging applications.