Human security is threatened by terrorism in the 21st century. A rapidly growing field of study aims to understand terrorist attack patterns for counter-terrorism policies. Existing research aimed at predicting terrorism from a single perspective, typically employing only background contextual information or past attacks of terrorist groups, has reached its limits. Here, we propose an integrated deep-learning framework that incorporates the background context of past attacked locations, social networks, and past actions of individual terrorist groups to discover the behavior patterns of terrorist groups. The results show that our framework outperforms the conventional base model at different spatio-temporal resolutions. Further, our model can project future targets of active terrorist groups to identify high-risk areas and offer other attack-related information in sequence for a specific terrorist group. Our findings highlight that the combination of a deep-learning approach and multi-scalar data can provide groundbreaking insights into terrorism and other organized violent crimes.

Understand whether and how the COVID-19 pandemic affects the risk of different types of conflict worldwide in the context of climate change.
Methodology
Based on the database of armed conflict, COVID-19, detailed climate, and non-climate data covering the period 2020–2021, we applied Structural Equation Modeling specifically to reorganize the links between climate, COVID-19, and conflict risk. Moreover, we used the Boosted Regression Tree method to simulate conflict risk under the influence of multiple factors.
Findings
The transmission risk of COVID-19 seems to decrease as the temperature rises. Additionally, COVID-19 has a substantial worldwide impact on conflict risk, albeit regional and conflict risk variations exist. Moreover, when testing a one-month lagged effect, we find consistency across regions, indicating a positive influence of COVID-19 on demonstrations (protests and riots) and a negative relationship with non-state and violent conflict risk.
Conclusion
COVID-19 has a complex effect on conflict risk worldwide under climate change.
Implications
Laying the theoretical foundation of how COVID-19 affects conflict risk and providing some inspiration for the implementation of relevant policies.

We present the satellite vegetation index time series model for detecting historical floods in ungauged hyperarid regions (SatVITS-Flood). SatVITS-Flood is based on observations that floods are the primary cause of local vegetation expansion in hyperarid regions. To detect such expansion, we used two time-series metrics: (a) trend change detection from the Breaks For Additive Season and Trend and (b) a newly developed seasonal change metric based on Temporal Fourier Analysis (TFA) and the growing-season integral anomaly (TFA-GSIanom). The two metrics complement each other by capturing changes in perennial plant species following extreme, rare floods and ephemeral vegetation changes following more frequent floods. Metrics were derived from the time series of the normalized difference vegetation index, the modified soil-adjusted vegetation index, and the normalized difference water index, acquired from MODIS, Landsat, and Advanced Very High-Resolution Radiometer. The timing of the change was compared with the date of the flood and the magnitude of change with its volume and duration. We tested SatVITS-Flood in three regions on different continents with 40-year-long, systematic, reliable gauge data. Our results indicate that SatVITS-Flood can predict flood occurrence with an accuracy of 78% and precision of 67% (Recall = 0.69 and F1 = 0.68; p < 0.01), and the flood volume and duration with NSE of 0.79 (RMSE = 15.4 ? 106 m3 event?1), and R2 of 0.69 (RMSE = 5.7 days), respectively. SatVITS-Flood proved useful for detecting historical floods and may provide valuable long-term hydrological information in poorly documented areas, which can help understand the impacts of climate change on the hydrology of hyperarid regions.

Treated wastewater is an important source of water for irrigation. As a result, irrigated crops are chronically exposed to wastewater-derived pharmaceuticals, such as the anticonvulsant drug lamotrigine. Lamotrigine is known to be taken up by plants, but its plant-derived metabolites and their distribution in different plant organs are unknown. This study aimed to detect and identify metabolites of lamotrigine in cucumber plants grown for 35 days in a hydroponic solution by using LC-MS/MS (Orbitrap) analysis. Our data showed that 96% of the lamotrigine taken up was metabolized. Sixteen metabolites possessing a lamotrigine core structure were detected. Reference standards confirmed two; five were tentatively identified, and nine molecular formulas were assigned. The data suggest that lamotrigine is metabolized via N-carbamylation, N-glucosidation, N-alkylation, N-formylation, N-oxidation, and amidine hydrolysis. The metabolites LTG-N2-oxide, M284, M312, and M370 were most likely produced in the roots and were translocated to the leaves. Metabolites M272, M312, M314, M354, M368, M370, and M418 were dominant in leaves. Only a few metabolites were detected in the fruits. With an increasing exposure time, lamotrigine leaf concentrations decreased because of continuous metabolism. Our data showed that the metabolism of lamotrigine in a plant is fast and that a majority of metabolites are concentrated in the roots and leaves.

In response to the decline in natural water sources, treated wastewater (TWW) has been introduced into the water cycle as a new water source for irrigation. However, this practice exposes the agricultural environment to various contaminants of emerging concern (CECs). To better understand their fate in the soil and to effectively predict their bioavailability for plant uptake, there is a need to quantify their concentrations in soil solutions. In this study, we examined the concentration of TWW-derived CECs in soil solution under three scenarios: (1) shifting from irrigation with freshwater (FW) to TWW (FW→TWW), (2) long-term continuous irrigation with TWW (TWW→TWW), and (3) prolonged irrigation with TWW followed by FW (TWW→FW). Carbamazepine, 1H-benzotriazole, lamotrigine, venlafaxine, and thiabendazole were ubiquitous in the TWW (mean concentrations of 125, 945, 180, 3630, and 90 ng/L, respectively) and irrigated soils. Interestingly, their concentrations in the soil solutions were not similar (higher or lower) to the corresponding concentrations in the irrigation water. In both the FW→TWW and TWW→FW irrigation scenarios, lower CECs concentrations were observed in soil solutions compared to the TWW→TWW scenario, indicating that a steady-state condition was not achieved after a single irrigation season (FW→TWW). For example, the concentrations of 1H-benzotriazole in Nir Oz soil solutions were 638, 310, and 1577 ng/L for the three irrigation scenarios (FW→TWW, TWW→FW, and TWW→TWW), respectively. Moreover, CECs concentrations in soil solutions were slightly lower in the TWW→FW irrigation scenario compared to the TWW→TWW scenario. This suggests that rain-fed crops are also exposed to TWW-derived CECs released from the adsorbed phase into the soil solution. The readily available CEC concentration in soil solutions depends on the soil irrigation history and the CEC concentration in the irrigation water, the soil characteristics, and the physicochemical properties of the CEC.

Vertical green-living walls (VGWs) are a promising solution for sustainable building design. However, their effectiveness in improving indoor air quality and reducing energy consumption in real-world settings still needs to be studied. Here we aim to contribute to this understanding by examining six indoor plant species (Peperomia obtusifolia, Tradescantia spathacea, Chlorophytum comosum, Spathiphyllum wallisii, Aeschynanthus radicans, and Philodendron hederaceum) in a 15 m2 Patrick Blanc's VGW system established in a shared office space (∼140 m3 volume). Carbon dioxide (CO2) assimilation, transpiration, and stomatal conductance were measured under varying light conditions and CO2 levels. In addition, numerous sensors were placed in the room to assess impacts on the indoor environment. Results indicate that all species but one (Philodendron) were equally effective in reducing CO2. Tradescantia had the highest cooling effect via transpiration. All species except Tradescantia had a very low light compensation point (<5 μmol m−2 s−1 PPFD), indicating their efficiency at reducing CO2 levels even under low light conditions. The net cooling effect of the VGW was 2.5°C–4.5 °C when the ventilation system was on and 1.2°C–3.6 °C when it was off. There was also a positive effect on indoor air quality, with an average CO2 reduction of 5% and sometimes up to 50%. By conducting controlled CO2 enrichment experiments, we estimated a 20% energy consumption savings from reduced air ventilation, equivalent to 1400 kWh/year. These results suggest that VGWs can improve indoor environments and thermal comfort in workplace settings and highlight the importance of choosing appropriate plant species.

Carbonate rocks are highly reactive and presumably have higher ratios of chemical weathering to total denudation relative to most other rock types. Their chemical reactivity affects the first-order morphology of carbonate-dominated landscapes and their climate sensitivity. However, there have been few efforts to quantify the partitioning of denudation into mechanical erosion and chemical weathering in carbonate landscapes such that their sensitivity to changing climatic and tectonic conditions remains elusive. Here, we compile bedrock and catchment-average cosmogenic calcite-³⁶Cl denudation rates and compare them to weathering rates from the same regions. Local bedrock denudation and weathering rates are comparable, ~20–40 mm/ka, whereas catchment-average denudation rates are ~2.7 times higher. This discrepancy is 5 times lower compared to silicate-rich rocks illustrating that elevated weathering rates make denudation more spatially uniform in carbonate-dominated landscapes. Catchment-average denudation rates correlate well with topographic relief and hillslope gradient, and moderate correlations with runoff can be explained by concurrent increases in weathering rate. Comparing denudation rates with weathering rates shows that mechanical erosion processes contribute ~50 % of denudation in southern France and ~70 % in Greece and Israel. Our results indicate that the partitioning between largely slope-independent chemical weathering and slope-dependent mechanical erosion varies based on climate and tectonics and impacts the landscape morphology. In humid, slowly uplifting regions, carbonates are associated with low-lying, flat topography because slope-independent chemical weathering dominates denudation. In contrast, in arid climates with rapid rock uplift rates, carbonate rocks form steep mountains that facilitate rapid, slope-dependent mechanical erosion required to compensate for inefficient chemical weathering and runoff loss to groundwater systems. This result suggests that carbonates represent an end-member for interactions between climate, tectonics, and earth materials.  10.5194/esurf-11-247-2023

Root systems form a significant part of tree biomass and function. Yet, roots are hidden from our eyes, making it difficult to track the belowground processes. By contrast, our capacity to detect aboveground changes in trees has been continuously improving using optical methods. Here, we tested two fundamental questions: (1) To what extent can we detect aboveground responses to mechanical damage of the root system? (2) To what extent are roots redundant? We applied three different non-destructive remote sensing means: (1) optical means to derive leaf greenness, (2) infrared means to detect the changes in leaf surface temperature and (3) spectral means to derive five vegetation indices (i.e. the photochemical reflectance index (PRI), the chlorophyll photosynthesis index (CIRed-edge), the anthocyanin reflectance index 1, the structure insensitive pigment index and the normalized difference water index (NDWI)). We recorded the above metrics for hours and days and up to a month following induced root damage in three key Mediterranean tree species: Aleppo pine (Pinus halepensis Mill.), Palestine oak (Quercus calliprinos Webb.) and Carob (Ceratonia siliqua L.). To induce root damage, we removed 25, 50 and 75 percent of the root system in each species and compared it with control saplings. Tree aboveground (canopy) responses to root damage increased over time and with damage level. Leaf warming (up to 3°C) and decreased PRI were the most significant and rapid responses, with temperature differences being visible as early as 2 days following root damage. NDWI and greenness were the least sensitive, with responses detectable only at 75 percent root damage and as late as 14 or 30 days following root damage. Responses varied vastly among species, with carob being the most sensitive and pine being the least. Changes in leaf temperature and PRI indicated that leaf transpiration and photosynthesis were impaired by root damage. Although trees build roots in excess, mechanical damage will eventually decrease transpiration and photosynthesis across tree species.

Abstract The spectral-based photochemical reflectance index (PRI) and leaf surface temperature (Tleaf) derived from thermal imaging are two indicative metrics of plant functioning. The relationship of PRI with radiation-use efficiency (RUE) and Tleaf with leaf transpiration could be leveraged to monitor crop photosynthesis and water use from space. Yet, it is unclear how such relationships will change under future high carbon dioxide concentrations ([CO2]) and drought. Here we established an [CO2] enrichment experiment in which three wheat genotypes were grown at ambient (400?ppm) and elevated (550?ppm) [CO2] and exposed to well-watered and drought conditions in two glasshouse rooms in two replicates. Leaf transpiration (Tr) and latent heat flux (LE) were derived to assess evaporative cooling, and RUE was calculated from assimilation and radiation measurements on several dates along the season. Simultaneous hyperspectral and thermal images were taken at ~ $\unicode{x0007E}$1.5?m from the plants to derive PRI and the temperature difference between the leaf and its surrounding air (? $\unicode{x02206}$Tleaf?air). We found significant PRI and RUE and ? $\unicode{x02206}$Tleaf?air and Tr correlations, with no significant differences among the genotypes. A PRI?RUE decoupling was observed under drought at ambient [CO2] but not at elevated [CO2], likely due to changes in photorespiration. For a LE range of 350?W?m?2, the ?Tleaf?air range was ~ $\unicode{x0007E}$10°C at ambient [CO2] and only ~ $\unicode{x0007E}$4°C at elevated [CO2]. Thicker leaves in plants grown at elevated [CO2] suggest higher leaf water content and consequently more efficient thermoregulation at high [CO2] conditions. In general, Tleaf was maintained closer to the ambient temperature at elevated [CO2], even under drought. PRI, RUE, ?Tleaf?air, and Tr decreased linearly with canopy depth, displaying a single PRI-RUE and ?Tleaf?air Tr model through the canopy layers. Our study shows the utility of these sensing metrics in detecting wheat responses to future environmental changes.

SUMMARY Late blight caused by the oomycete Phytophthora infestans is a most devastating disease of potatoes (Solanum tuberosum). Its early detection is crucial for suppressing disease spread. Necrotic lesions are normally seen in leaves at 4 dpi (days post inoculation) when colonized cells are dead, but early detection of the initial biotrophic growth stage, when the pathogen feeds on living cells, is challenging. Here, the biotrophic growth phase of P. infestans was detected by whole-plant redox imaging of potato plants expressing chloroplast-targeted reduction-oxidation sensitive green fluorescent protein (chl-roGFP2). Clear spots on potato leaves with a lower chl-roGFP2 oxidation state were detected as early as 2 dpi, before any visual symptoms were recorded. These spots were particularly evident during light-to-dark transitions and reflected the mislocalization of chl-roGFP2 outside the chloroplasts. Image analysis based on machine learning enabled systematic identification and quantification of spots and unbiased classification of infected and uninfected leaves in inoculated plants. Comparing redox to chlorophyll fluorescence imaging showed that infected leaf areas which exhibit mislocalized chl-roGFP2 also showed reduced non-photochemical quenching (NPQ) and enhanced quantum PSII yield (ΦPSII) compared to the surrounding leaf areas. The data suggest that mislocalization of chloroplast-targeted proteins is an efficient marker of late blight infection and demonstrate how it can be utilized for nondestructive monitoring of the disease biotrophic stage using whole-plant redox imaging.