Atmospheric Gas-Phase Formation of Methanesulfonic Acid.

Despite its impact on the climate, the mechanism of methanesulfonic acid (MSA) formation in the oxidation of dimethyl sulfide (DMS) remains unclear. The DMS + OH reaction is known to form methanesulfinic acid (MSIA), methane sulfenic acid (MSEA), the methylthio radical (CH3S), and hydroperoxymethyl thioformate (HPMTF). Among them, HPMTF reacts further to form SO2 and OCS, while the other three form the CH3SO2 radical. Based on theoretical calculations, we find that the CH3SO2 radical can add O2 to form CH3S(O)2OO, which can react further to form MSA. The branching ratio is highly temperature sensitive, and the MSA yield increases with decreasing temperature. In warmer regions, SO2 is the dominant product of DMS oxidation, while in colder regions, large amounts of MSA can form. Global modeling indicates that the proposed temperature-sensitive MSA formation mechanism leads to a substantial increase in the simulated global atmospheric MSA formation and burden.

TMNet: A Two-Branch Multi-Scale Semantic Segmentation Network for Remote Sensing Images.

Pixel-level information of remote sensing images is of great value in many fields. CNN has a strong ability to extract image backbone features, but due to the localization of convolution operation, it is challenging to directly obtain global feature information and contextual semantic interaction, which makes it difficult for a pure CNN model to obtain higher precision results in semantic segmentation of remote sensing images. Inspired by the Swin Transformer with global feature coding capability, we design a two-branch multi-scale semantic segmentation network (TMNet) for remote sensing images. The network adopts the structure of a double encoder and a decoder. The Swin Transformer is used to increase the ability to extract global feature information. A multi-scale feature fusion module (MFM) is designed to merge shallow spatial features from images of different scales into deep features. In addition, the feature enhancement module (FEM) and channel enhancement module (CEM) are proposed and added to the dual encoder to enhance the feature extraction. Experiments were conducted on the WHDLD and Potsdam datasets to verify the excellent performance of TMNet.

Toward a Global Model for Soil Inorganic Phosphorus Dynamics: Dependence of Exchange Kinetics and Soil Bioavailability on Soil Physicochemical Properties.

The representation of phosphorus (P) cycling in global land models remains quite simplistic, particularly on soil inorganic phosphorus. For example, sorption and desorption remain unresolved and their dependence on soil physical and chemical properties is ignored. Empirical parameter values are usually based on expert knowledge or data from few sites with debatable global representativeness in most global land models. To overcome these issues, we compiled from data of inorganic soil P fractions and calculated the fraction of added P remaining in soil solution over time of 147 soil samples to optimize three parameters in a model of soil inorganic P dynamics. The calibrated model performed well (r 2 > 0.7 for 122 soil samples). Model parameters vary by several orders of magnitude, and correlate with soil P fractions of different inorganic pools, soil organic carbon and oxalate extractable metal oxide concentrations among the soil samples. The modeled bioavailability of soil P depends on, not only, the desorption rates of labile and sorbed pool, inorganic phosphorus fractions, the slope of P sorbed against solution P concentration, but also on the ability of biological uptake to deplete solution P concentration and the time scale. The model together with the empirical relationships of model parameters on soil properties can be used to quantify bioavailability of soil inorganic P on various timescale especially when coupled within global land models.

High-resolution soil moisture mapping using PSO-based optimized cerebellar model articulation controller (CMAC).

A good knowledge in eco-hydrological processes requires significant understanding of geospatial distribution of soil moisture (SM). However, SM monitoring remains challenging due to its large spatial variability and its dynamic time response. This study was performed to assess the performance of a particle swarm optimization (PSO)-based optimized Cerebellar Model Articulation Controller (CMAC) in generating high-resolution surface SM estimates using sentinel-2 imagery over a Mediterranean agro-ecosystem. Furthermore, the results were compared with those of PSO-based optimized group method of data handling (GMDH) as a more common data-driven method. Two different modeling approaches i.e. modeling in homogenous clusters (local approach) and modeling in entire area as an entity (global approach) were examined. Candidate predictors namely sentinel-2 spectral bands, normalized difference vegetation index (NDVI) and normalized difference water index (NDWI), digital elevation model (DEM), slope and aspect were used as the input variables to estimate SM. An intensive field survey had been done to gather in-situ SM data using a time-domain reflectometer (TDR). K-fold validation based on in-situ SM measurements demonstrated the reasonability of the SM estimation of the proposed methodology. Detecting homogeneous areas was done using genetic and particle swarm optimization algorithms. Synthesized SM product of PSO-GMDH showed a mean Normalized Root-Mean-Square Error (NRMSE) of 13.6 to 8.91 for global and local approaches in the test phase. PSO-CMAC method with an average NRMSE of 12.47 to 8.72 for global and local approaches shows the highest accuracy and outperforms the PSO-GMDH method at both local and global approaches. Overall, results revealed that clustering study area prior to running machine learning (ML) models coupled with optical satellite imagery and geophysical properties, boosts their predictive performance and can lead to more accurate mapping of SM with more heterogeneity. The results also showed that the global approach had a moderate performance in capturing the SM heterogeneity.

Integrated Analysis of Machine Learning and Deep Learning in Silkworm Pupae (Bombyx mori) Species and Sex Identification.

Hybrid pairing of the corresponding silkworm species is a pivotal link in sericulture, ensuring egg quality and directly influencing silk quantity and quality. Considering the potential of image recognition and the impact of varying pupal postures, this study used machine learning and deep learning for global modeling to identify pupae species and sex separately or simultaneously. The performance of traditional feature-based approaches, deep learning feature-based approaches, and their fusion approaches were compared. First, 3600 images of the back, abdomen, and side postures of 5 species of male and female pupae were captured. Next, six traditional descriptors, including the histogram of oriented gradients (HOG), and six deep learning descriptors, including ConvNeXt-S, were utilized to extract significant species and sex features. Finally, classification models were constructed using the multilayer perceptron (MLP), support vector machine, and random forest. The results indicate that the {HOG + ConvNeXt-S + MLP} model excelled, achieving 99.09% accuracy for separate species and sex recognition and 98.40% for simultaneous recognition, with precision-recall and receiver operating characteristic curves ranging from 0.984 to 1.0 and 0.996 to 1.0, respectively. In conclusion, it can capture subtle distinctions between pupal species and sexes and shows promise for extensive application in sericulture.

NASA GEOS Composition Forecast Modeling System GEOS-CF v1.0: Stratospheric Composition.

The NASA Goddard Earth Observing System (GEOS) Composition Forecast (GEOS-CF) provides recent estimates and 5-day forecasts of atmospheric composition to the public in near-real time. To do this, the GEOS Earth system model is coupled with the GEOS-Chem tropospheric-stratospheric unified chemistry extension (UCX) to represent composition from the surface to the top of the GEOS atmosphere (0.01 hPa). The GEOS-CF system is described, including updates made to the GEOS-Chem UCX mechanism within GEOS-CF for improved representation of stratospheric chemistry. Comparisons are made against balloon, lidar, and satellite observations for stratospheric composition, including measurements of ozone (O3) and important nitrogen and chlorine species related to stratospheric O3 recovery. The GEOS-CF nudges the stratospheric O3 toward the GEOS Forward Processing (GEOS FP) assimilated O3 product; as a result the stratospheric O3 in the GEOS-CF historical estimate agrees well with observations. During abnormal dynamical and chemical environments such as the 2020 polar vortexes, the GEOS-CF O3 forecasts are more realistic than GEOS FP O3 forecasts because of the inclusion of the complex GEOS-Chem UCX stratospheric chemistry. Overall, the spatial patterns of the GEOS-CF simulated concentrations of stratospheric composition agree well with satellite observations. However, there are notable biases-such as low NO x and HNO3 in the polar regions and generally low HCl throughout the stratosphere-and future improvements to the chemistry mechanism and emissions are discussed. GEOS-CF is a new tool for the research community and instrument teams observing trace gases in the stratosphere and troposphere, providing near-real-time three-dimensional gridded information on atmospheric composition.

Insect Herbivory Caused Plant Stress Emissions Increases the Negative Radiative Forcing of Aerosols.

Plant stress in a changing climate is predicted to increase plant volatile organic compound (VOC) emissions and thus can affect the formed secondary organic aerosol (SOA) concentrations, which in turn affect the radiative properties of clouds and aerosol. However, global aerosol-climate models do not usually consider plant stress induced VOCs in their emission schemes. In this study, we modified the monoterpene emission factors in biogenic emission model to simulate biotic stress caused by insect herbivory on needleleaf evergreen boreal and broadleaf deciduous boreal trees and studied the consequent effects on SOA formation, aerosol-cloud interactions as well as direct radiative effects of formed SOA. Simulations were done altering the fraction of stressed and healthy trees in the latest version of ECHAM-HAMMOZ (ECHAM6.3-HAM2.3-MOZ1.0) global aerosol-climate model. Our simulations showed that increasing the extent of stress to the aforementioned tree types, substantially increased the SOA burden especially over the areas where these trees are located. This indicates that increased VOC emissions due to increasing stress enhance the SOA formation via oxidation of VOCs to low VOCs. In addition, cloud droplet number concentration at the cloud top increased with increasing extent of biotic stress. This indicates that as SOA formation increases, it further enhances the number of particles acting as cloud condensation nuclei. The increase in SOA formation also decreased both all-sky and clear-sky radiative forcing. This was due to a shift in particle size distributions that enhanced aerosol reflecting and scattering of incoming solar radiation.

Description of the NASA GEOS Composition Forecast Modeling System GEOS-CF v1.0.

The Goddard Earth Observing System composition forecast (GEOS-CF) system is a high-resolution (0.25°) global constituent prediction system from NASA's Global Modeling and Assimilation Office (GMAO). GEOS-CF offers a new tool for atmospheric chemistry research, with the goal to supplement NASA's broad range of space-based and in-situ observations. GEOS-CF expands on the GEOS weather and aerosol modeling system by introducing the GEOS-Chem chemistry module to provide hindcasts and 5-days forecasts of atmospheric constituents including ozone (O3), carbon monoxide (CO), nitrogen dioxide (NO2), sulfur dioxide (SO2), and fine particulate matter (PM2.5). The chemistry module integrated in GEOS-CF is identical to the offline GEOS-Chem model and readily benefits from the innovations provided by the GEOS-Chem community. Evaluation of GEOS-CF against satellite, ozonesonde and surface observations for years 2018-2019 show realistic simulated concentrations of O3, NO2, and CO, with normalized mean biases of -0.1 to 0.3, normalized root mean square errors between 0.1-0.4, and correlations between 0.3-0.8. Comparisons against surface observations highlight the successful representation of air pollutants in many regions of the world and during all seasons, yet also highlight current limitations, such as a global high bias in SO2 and an overprediction of summertime O3 over the Southeast United States. GEOS-CF v1.0 generally overestimates aerosols by 20%-50% due to known issues in GEOS-Chem v12.0.1 that have been addressed in later versions. The 5-days forecasts have skill scores comparable to the 1-day hindcast. Model skills can be improved significantly by applying a bias-correction to the surface model output using a machine-learning approach.

Is Black Carbon an Unimportant Ice-Nucleating Particle in Mixed-Phase Clouds?

It has been hypothesized that black carbon (BC) influences mixed-phase clouds by acting as an ice-nucleating particle (INP). However, the literature data for ice nucleation by BC immersed in supercooled water are extremely varied, with some studies reporting that BC is very effective at nucleating ice, whereas others report no ice-nucleating ability. Here we present new experimental results for immersion mode ice nucleation by BC from two contrasting fuels (n-decane and eugenol). We observe no significant heterogeneous nucleation by either sample. Using a global aerosol model, we quantify the maximum relative importance of BC for ice nucleation when compared with K-feldspar and marine organic aerosol acting as INP. Based on the upper limit from our laboratory data, we show that BC contributes at least several orders of magnitude less INP than feldspar and marine organic aerosol. Representations of its atmospheric ice-nucleating ability based on older laboratory data produce unrealistic results when compared against ambient observations of INP. Since BC is a complex material, it cannot be unambiguously ruled out as an important INP species in all locations at all times. Therefore, we use our model to estimate a range of values for the density of active sites that BC particles must have to be relevant for ice nucleation in the atmosphere. The estimated values will guide future work on BC, defining the required sensitivity of future experimental studies.

Effect of electrical field strength applied by PEF processing and storage temperature on the outgrowth of yeasts and moulds naturally present in a fresh fruit smoothie.

Pulsed electrical field (PEF) technology offers an alternative to thermal pasteurisation of high-acid fruit juices, by extending the shelf life of food products, while retaining its fresh taste and nutritional value. Substantial research has been performed on the effect of electrical field strength on the inactivation kinetics of spoilage and pathogenic micro-organisms and on the outgrowth of spoilage micro-organisms during shelf life. However, studies on the effect of electrical field strength on the inactivation and outgrowth of surviving populations during shelf life are missing. In this study, we assessed the influence of electrical field strength applied by PEF processing and storage temperature on the outgrowth of surviving yeast and mould populations naturally present in fresh fruit smoothie in time. Therefore, an apple-strawberry-banana smoothie was treated in a continuous-flow PEF system (130L/h), using similar inlet and outlet conditions (preheating temperature 41°C, maximum temperature 58°C) to assure that the amount of energy across the different conditions was kept constant. Smoothies treated with variable electrical field strengths (13.5, 17.0, 20.0 and 24.0kV/cm) were compared to smoothies without treatment for outgrowth of yeasts and moulds. Outgrowth of yeasts and moulds stored at 4°C and 7°C was analysed by plating and visual observation and yeast growth was modelled using the modified logistic growth model (Zwietering model). Results showed that the intensity of the electrical field strength had an influence on the degree of inactivation of yeast cells, resulting in a faster outgrowth over time at lower electrical field strength. Outgrowth of moulds over time was not affected by the intensity of the electrical field strength used. Application of PEF introduces a trade-off between type of spoilage: in untreated smoothie yeasts lead to spoilage after 8days when stored at 4 or 7°C, whereas in PEF treated smoothie yeasts were (partly) inactivated and provided outgrowth opportunities for moulds, which led to spoilage by moulds after 14days (7°C) or 18days (4°C).

Flood risk and adaptation strategies under climate change and urban expansion: A probabilistic analysis using global data.

An accurate understanding of flood risk and its drivers is crucial for effective risk management. Detailed risk projections, including uncertainties, are however rarely available, particularly in developing countries. This paper presents a method that integrates recent advances in global-scale modeling of flood hazard and land change, which enables the probabilistic analysis of future trends in national-scale flood risk. We demonstrate its application to Indonesia. We develop 1000 spatially-explicit projections of urban expansion from 2000 to 2030 that account for uncertainty associated with population and economic growth projections, as well as uncertainty in where urban land change may occur. The projections show that the urban extent increases by 215%-357% (5th and 95th percentiles). Urban expansion is particularly rapid on Java, which accounts for 79% of the national increase. From 2000 to 2030, increases in exposure will elevate flood risk by, on average, 76% and 120% for river and coastal floods. While sea level rise will further increase the exposure-induced trend by 19%-37%, the response of river floods to climate change is highly uncertain. However, as urban expansion is the main driver of future risk, the implementation of adaptation measures is increasingly urgent, regardless of the wide uncertainty in climate projections. Using probabilistic urban projections, we show that spatial planning can be a very effective adaptation strategy. Our study emphasizes that global data can be used successfully for probabilistic risk assessment in data-scarce countries.