LEVY FLIGHT-ENHANCED SOFT COMPUTING APPROACHES FOR PREDICTING POLLUTION DYNAMICS AND OPTIMIZING BIOLOGICAL REMEDIATION UNDER CLIMATE CHANGE SCENARIOS

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This work aims to answer if post-processing climate model outputs will improve the accuracy of climate change impact assessment and adaptation evaluation. To achieve this aim, the daily outputs of CSIRO Conformal Cubic Atmospheric Model for periods 1960–1979, 1980–1999 and 2046–2065, and observed daily climate data (1960–1979, 1980–1999) were used by a stochastic weather generator, the Long Ashton Research Station-Weather Generator to construct long time series of local climate scenarios (CSs). The direct outputs of climate models (DOCM) and CSs were then fed into the Agricultural Production System sIMulator—Wheat model to calculate seasonal climate variables and production components at three locations spanning northern, central and southern wheat production areas in New South Wales (NSW), Australia. This study firstly compared the differences in climate variables and production components derived from DOCM and CSs against those from observed climate for period 1960–1979. The impact difference arising from the use of DOCM and CSs for the future period 2046–2065 was then quantified. Simulation results show that (1) both the median/mean and distribution/variation of climate variables and production components associated with CSs were closer to those derived from observed climate when compared to those from DOCM in most of the cases (median/mean, distribution/variation, climate variables, production components and locations); (2) the difference in the mean and distribution of climate variables and production components derived from DOCM and observed climate was significant in most of the cases; (3) longer dry spells in both winter and spring were found from CSs across the three locations considered in comparison with those from DOCM; (4) the median growing season (GS) rainfall total, GS average maximum temperature, GS average solar radiation, GS length and final wheat yield were lower from DOCM than those from CSs and vice versa for GS rainfall frequency and GS average minimum temperature in 2055; (5) the mean and distribution of these climate variables and production components arising from the use of DOCM and CSs are significantly different in most of the cases. This implied that using the direct outputs of spatially downscaled general circulation model without implementing post-processing procedures may lead to significant errors in projected climate impact and the identified effort in tackling climate change risk. It is therefore highly recommended that post-processing procedures be used in developing robust CSs for climate change impact assessment and adaptation evaluation.

Societal Impact StatementFleshy fruits provide humans with many flavorful and nutritious crops. Understanding the diversity of these plants is fundamental to managing agriculture and food security in a changing world. This study surveyed fruit trait variation across species of tomato wild relatives and explored associations among color, size, shape, sugars, and acids. These wild tomato species native to South America can be interbred with the economically important cultivated tomato. Beyond its application to tomatoes, deepening our knowledge of how fruit traits evolve together is valuable to crop improvement efforts aimed at breeding more nutritious and appealing varieties of fruits.Summary Fleshy fruits display a striking diversity of traits, many of which are important for agriculture. The evolutionary drivers of this variation are not well understood, and most studies have relied on variation found in the wild. Few studies have explored this question on a fine‐grained scale with a group of recently diverged species while controlling for environmental effects. We developed the tomato clade as a novel system for fruit trait evolution research by presenting the first common garden‐based systematic survey of variation and phylogenetic signal in color, nutrition, and morphology traits across all 13 species of tomato wild relatives (Solanum sect. Lycopersicon). We laid the groundwork for further testing of potential evolutionary drivers by assessing patterns of clustering and correlation among disperser‐relevant fruit traits as well as historical climate variables. We found evidence of two distinct clusters of associated fruit traits defined by color, sugar type, and malic acid concentration. We also observed correlations between a fruit's external appearance and internal nutrient content that could function as honest signals to dispersers. Analyses of historical climate and soil variables revealed an association between red/orange/yellow fruits and high annual average temperature. Our results establish the tomato clade as a promising system for testing hypotheses on the drivers of divergence behind early‐stage fleshy fruit evolution, particularly selective pressure from frugivores.

Agricultural best management practices (BMPs) reduce non-point source pollution from cropland. Goals for BMP adoption and expected pollutant load reductions are often specified in water quality management plans to protect and restore waterbodies; however, estimates of needed load reductions and pollutant removal performance of BMPs are generally based on historic climate. Increasing air temperatures and changes in precipitation patterns and intensity are anticipated throughout the U.S. over the 21st century. The effects of such changes on agricultural pollutant loads have been addressed by several authors, but how these changes will affect the performance of widely promoted BMPs has received limited attention. We use the Soil and Water Assessment Tool (SWAT) to investigate potential changes in the effectiveness of conservation tillage, no-till, vegetated filter strips, grassed waterways, nutrient management, winter cover crops, and drainage water management practices under potential future temperature and precipitation patterns. We simulate two agricultural watersheds in the Minnesota Corn Belt and the Georgia Coastal Plain with different hydro-climatic settings, under recent conditions (1950-2005) and multiple potential future mid-century (2030-2059) and late-century (2070-2099) climate scenarios. Results suggest future increases in agricultural source loads of sediment, nitrogen and phosphorous. Most BMPs continue to reduce loads, but removal efficiencies generally decline due to more intense runoff events, biological responses to changes in soil moisture and temperature, and exacerbated upland loading. The coupled effects of higher upland loading and reduced BMP efficiencies suggest that wider adoption, resizing, and/or combining practices may be needed in the future to meet water quality goals for agricultural lands.

The assessment of future discharge impacts on the Gandak River Basin is crucial for understanding potential climate change effects and planning effective water resource management. This study employs the Soil and Water Assessment Tool (SWAT) model integrated with machine learning techniques to evaluate and predict the future discharge patterns in the basin. The Gandak River Basin, a significant tributary of the Ganges, plays a vital role in regional agriculture, hydropower, and ecosystem services, making it imperative to understand the potential changes in its hydrological dynamics. The SWAT model, a comprehensive, semi-distributed hydrological model, simulates the effects of land management practices, climate variability, and water management strategies on water, sediment, and agricultural chemical yields in large complex watersheds. SWAT’s capability to incorporate various climatic inputs, land use, soil properties, and topography enables it to simulate hydrological processes with high accuracy. However, the complexity and non-linearity of hydrological processes often necessitate the incorporation of advanced data-driven techniques to enhance prediction accuracy and robustness. In this study, machine learning algorithms, including Random Forest, Support Vector Machines, and Neural Networks, are integrated with SWAT to improve the model’s predictive performance. These algorithms are trained on historical discharge data, climate variables, and SWAT-simulated outputs to capture the non-linear relationships and complex interactions within the hydrological system. The hybrid model leverages the strengths of both physically-based and data-driven approaches, providing a more comprehensive understanding of the future discharge scenarios under various climate change projections. The research involves hbias-correcting climate projections from General Circulation Models (GCMs) to derive high-resolution climate inputs for the SWAT model. Scenarios based on Shared Socio-Economic Pathways (SSPs) are employed to simulate future climatic conditions. The SWAT model is calibrated and validated using observed discharge data from the Gandak River Basin, ensuring the reliability of the simulations. Subsequently, the machine learning models are trained on the SWAT outputs and historical data, creating an ensemble approach to predict future discharge. Results indicate significant variability in future discharge patterns under different climate scenarios. The integrated SWAT and machine learning model captures the seasonal and inter-annual variability in discharge more accurately than the standalone SWAT model. The findings suggest potential increases in peak discharge events during the monsoon season, with implications for flood risk management. Conversely, reduced discharge during the dry season could impact water availability for agriculture and domestic use, necessitating adaptive water management strategies. The study highlights the importance of combining physically-based hydrological models with machine learning techniques to enhance the prediction of hydrological responses to climate change. The integrated approach provides valuable insights for policymakers and stakeholders in the Gandak River Basin, aiding in the development of sustainable water resource management plans to mitigate the adverse impacts of future climate variability. This research underscores the need for continuous monitoring, adaptive management, and the incorporation of advanced modeling techniques to address the complexities of climate change impacts on river basins.

This study investigates the effects of climate change on ecosystems through the application of fuzzy logic, a mathematical approach that handles uncertainty and imprecision in environmental data. Climate change presents complex, dynamic challenges for ecosystems, where various factors such as temperature fluctuations, precipitation patterns, and extreme weather events interact in ways that are difficult to model using traditional methods. By utilizing fuzzy logic systems, this research aims to provide a more nuanced understanding of how these environmental variables affect biodiversity, ecosystem services, and overall ecosystem health. Through the development of fuzzy inference models, we assess the interactions between climate variables and ecological responses, incorporating both qualitative and quantitative data. The results of the study highlight the potential for fuzzy logic to improve predictions and inform adaptive management strategies in response to the unpredictable impacts of climate change on ecosystems. This approach offers valuable insights for policymakers, environmental managers, and researchers seeking to mitigate the effects of climate change on natural habitats and biodiversity.

Environmental contamination due to industrial effluents, agricultural runoff, and urbanization has become a critical global concern. Accurate prediction of pollutant levels and assessment of biological remediation potential are essential for sustainable environmental management and public health protection. Traditional modeling approaches often struggle with complex, nonlinear interactions between contaminants and biological remediation agents. Conventional computational models frequently exhibit limitations in capturing the dynamic and stochastic nature of environmental systems. Moreover, existing prediction techniques may fail to optimize bioremediation strategies effectively, leading to inefficiencies in pollutant removal and prolonged environmental recovery times. In this study, we propose a hybrid soft computing framework integrating a novel meta-heuristic optimization algorithm with fuzzy logic and artificial neural networks. The meta-heuristic component efficiently tunes the parameters of the predictive models, while the fuzzy logic handles uncertainties inherent in environmental data. The framework was trained and validated using multi-source datasets comprising heavy metals, organic pollutants, and microbial remediation efficiency metrics. Comparative analysis with conventional machine learning models and standalone soft computing techniques was conducted to evaluate predictive accuracy and optimization performance. The proposed hybrid model showd superior predictive performance, achieving a mean absolute error (MAE) reduction of 18–25% compared to traditional models. Biological remediation efficiency predictions exhibited a 92% correlation with experimental observations, outperforming standalone neural networks and fuzzy inference models by 12–15%. The meta-heuristic optimization successfully identified optimal remediation strategies, reducing predicted contaminant levels by up to 35% under simulated intervention scenarios.

Petroleum-contaminated soil is considered among the most important potential anthropogenic atmospheric methane sources. Additionally, various rhizoremediation factors can affect methane emissions by altering soil ecosystem carbon cycles. Nonetheless, greenhouse gas emissions from soil have not been given due importance as a potentially relevant parameter in rhizoremediation techniques. Therefore, in this study we sought to investigate the effects of different plant and soil amendments on both remediation efficiencies and methane emission characteristics in dieselcontaminated soil. An indoor pot experiment consisting of three plant treatments (control, maize, tall fescue) and two soil amendments (chemical nutrient, compost) was performed for 95 days. Total petroleum hydrocarbon (TPH) removal efficiency, dehydrogenase activity, and alkB (i.e., an alkane compound-degrading enzyme) gene abundance were the highest in the tall fescue and maize soil system amended with compost. Compost addition enhanced both the overall remediation efficiencies, as well as pmoA (i.e., a methane-oxidizing enzyme) gene abundance in soils. Moreover, the potential methane emission of diesel-contaminated soil was relatively low when maize was introduced to the soil system. After microbial community analysis, various TPH-degrading microorganisms (Nocardioides, Marinobacter, Immitisolibacter, Acinetobacter, Kocuria, Mycobacterium, Pseudomonas, Alcanivorax) and methane-oxidizing microorganisms (Methylocapsa, Methylosarcina) were observed in the rhizosphere soil. The effects of major rhizoremediation factors on soil remediation efficiency and greenhouse gas emissions discussed herein are expected to contribute to the development of sustainable biological remediation technologies in response to global climate change.

Abstract. We present time series of equilibrium-line altitude (ELA) measured from the end-of-summer snow line altitude computed using satellite images, for 43 glaciers in the western Alps over the 1984–2010 period. More than 120 satellite images acquired by Landsat, SPOT and ASTER were used. In parallel, changes in climate variables, summer cumulative positive degree days (CPDD) and winter precipitation, were analyzed over the same time period using 22 weather stations located inside and around the study area. Assuming a continuous linear trend over the study period: (1) the average ELA of the 43 glaciers increased by about 170 m; (2) summer CPDD increased by about 150 PDD at 3000 m a.s.l.; and (3) winter precipitation remained rather stationary. Summer CPDD showed homogeneous spatial and temporal variability; winter precipitation showed homogeneous temporal variability, but some stations showed a slightly different spatial pattern. Regarding ELAs, temporal variability between the 43 glaciers was also homogeneous, but spatially, glaciers in the southern part of the study area differed from glaciers in the northern part, mainly due to a different precipitation pattern. A sensitivity analysis of the ELAs to climate and morpho-topographic variables (elevation, aspect, latitude) highlighted the following: (1) the average ELA over the study period of each glacier is strongly controlled by morpho-topographic variables; and (2) the interannual variability of the ELA is strongly controlled by climate variables, with the observed increasing trend mainly driven by increasing temperatures, even if significant nonlinear, low-frequency fluctuations appear to be driven by winter precipitation anomalies. Finally, we used an expansion of Lliboutry's approach to reconstruct fluctuations in the ELA of any glacier of the study area with respect to morpho-topographic and climate variables, by quantifying their respective weight and the related uncertainties in a consistent manner within a hierarchical Bayesian framework. This method was tested and validated using the ELA measured on the satellite images.

<p>High mountain regions, like the Andes, face various risks due to climate change. In the Santa River catchment in Peru which includes the glaciated Cordillera Blanca, water availability is threatened by many climatic and non-climatic impacts. The water resources in the catchment heavily rely on seasonal precipitation and during the dry season glacier melt water plays an important role. However, both, precipitation patterns and glacier extent are affected by climate change impacts. Additionally, socio-economic changes put further pressure on water resources and hence on water availability.</p> <p>Within the AguaFuturo Project we established a conceptual integrated water balance model based on a semi-distributed HBV model for the data scarce Santa River catchment. The hydrological model processes are extended by feedback loops for agricultural and domestic water use. The model runs on daily time scale and includes two hydrological response units. One includes the irrigated agricultural areas which are predominately located in the valley of the catchment; the other includes non-irrigated areas and domestic water use.</p> <p>To assess future water balance challenges we downscaled and disaggregated monthly CORDEX scenarios for 2020-2050 using information from the new Peruvian precipitation dataset PISCO (Peruvian Interpolated data of the SENAMHI’s Climatological and hydrological Observations) for simulations of future changes in hydro-climatology. In the model, these climate scenarios are combined with possible socio-economic scenarios which are translated into time series for domestic and agricultural water demand. The socio-economic scenarios are developed by using the Cross-Impact-Balance-Analysis (CIB), a method used for analyzing impact networks. Using CIB, the interrelations between 15 social, economic and policy descriptors were analyzed and as a result a total of 29 possible consistent scenarios were determined. For further analysis and validation of these scenarios a participatory process was included, involving local experts and stakeholders of the study region.</p> <p>The climate and socio-economic scenarios are independent and can be combined randomly. The uncertainties of the climatic and socio-economic scenarios are quantified by Monte Carlo simulations.</p> <p>The output of the model runs is an ensemble of possible future discharges of the Santa River, which can be further analyzed statistically to assess the range of the possible discharges. This evaluation provides an estimate of the probability of water shortages, especially with regard to conflict potential with hydropower production and the large scale irrigated agriculture areas in the adjacent coastal desert which also rely on water from the Santa River.</p>

This study examines the influence of large-scale climatic phenomena—sunspot activity (SSN) and the El Niño-Southern oscillation (ENSO), represented by the Southern oscillation index (SOI) on precipitation, temperature, and dew point patterns in Gilan province, Iran. Using wavelet signal analysis, including continuous wavelet transforms (CWT), cross-wavelet transforms (XWT), wavelet coherence (WTC), and time-lag correlation analysis (TLCA), the research investigates temporal and frequency-domain relationships between these factors and regional climatic variables. A 73-year dataset (1951–2023) reveals cyclical patterns in SSN and SOI, correlating with significant 10-year periodicities in precipitation trends. The study further reveals ENSO and SSN’s influence on long-term temperature and dew point fluctuations. While sunspot activity and ENSO both modulate climate variability, the study’s scope is limited to Gilan province and excludes other atmospheric drivers or anthropogenic influences, which may also affect local hydrology. The findings suggest integrating solar and climatic indices into predictive models could improve long-term climate forecasts, supporting water resource management in this climate-sensitive region. This research provides novel insights for policymakers to address water-related challenges amid climatic variability by applying advanced wavelet methods to analyze multi-decadal interactions.

Global climate change is significantly impacting dust emission patterns in arid and semi‐arid regions, posing challenges to environmental quality and human health. However, the impacts of future climate change on dust emissions in China remain insufficiently understood. This study employed the Weather Research and Forecasting coupled with Chemistry (WRF‐Chem) model to project future dust emissions in China under two climate scenarios (SSP2‐4.5 and SSP5‐8.5) for the years 2030, 2060, and 2090. Results indicated that in the near term (2030 and 2060), dust emissions were projected to be higher under the high‐emission SSP5‐8.5 scenario compared to the moderate‐emission SSP2‐4.5 scenario. This suggests that increased greenhouse gas concentrations and associated climatic changes may enhance conditions favorable for dust generation, such as elevated temperatures and reduced soil moisture. By 2090, however, this trend may reverse, with SSP2‐4.5 exhibiting higher dust emissions than SSP5‐8.5. This reversal highlights the complex, non‐linear interactions between long‐term climate variables and dust emission processes, potentially due to changes in precipitation patterns, atmospheric circulation, and vegetation cover. The spatial distribution of dust emissions consistently remains concentrated in northwestern China and southern Mongolia across all scenarios and time periods, emphasizing the persistent role of major dust source regions like the Taklamakan Desert and the Gobi Desert. These findings underscore the need for targeted mitigation and adaptation strategies to manage the environmental and health impacts associated with dust emissions in the context of climate change.

Land suitability analyses are crucial for identifying sustainable areas for agricultural crops and developing appropriate land use strategies. Thus, the present study aims to analyze the current and future land suitability for wheat (Triticum aestivum L.) cultivation in Ethiopia. Twelve variables including soil properties, climate variables, and topographic characteristics were used in the evaluation of land suitability. Statistical methods such as Rotated Empirical Orthogonal Functions (REOF), Coefficient of Variation (CV), correlation, and parametric and non-parametric trend analyses were used to analyze the spatiotemporal variability in current and future climate data and identified significant patterns of variability. For future projections of land suitability and climate, this study employed climate models from the Coupled Model Intercomparison Project Phase 6 (CMIP6) framework, downscaled using regional climate model version 4.7 (RegCM4.7) under two different Shared Socioeconomic Pathway (SSP) climate scenarios: SSP1 (a lower emission scenario) and SSP5 (a higher emission scenario). Under the current condition, during March, April, and May (MAM), 53.4% of the country was suitable for wheat cultivation while 44.4% was not suitable. In 2050, non-suitable areas for wheat cultivation are expected to increase by 1% and 6.9% during MAM under SSP1 and SSP5 climate scenarios, respectively. Our findings highlight that areas currently suitable for wheat may face challenges in the future due to altered temperature and precipitation patterns, potentially leading to shifts in suitable areas or reduced productivity. This study also found that the suitability of land for wheat cultivation was determined by rainfall amount, temperature, soil type, soil pH, soil organic carbon content, soil nitrogen content, and elevation. This research underscores the critical importance of integrating spatiotemporal climate variability with future projections to comprehensively assess wheat suitability. By elucidating the implications of climate change on wheat cultivation, this study lays the groundwork for developing effective adaptation strategies and actionable recommendations to enhance management practices. The findings support the county’s commitment to refining agricultural land use strategies, increasing wheat production through suitability predictions, and advancing self-sufficiency in wheat production. Additionally, these insights can empower Ethiopia’s agricultural extension services to guide farmers in cultivating wheat in areas identified as highly and moderately suitable, thereby bolstering production in a changing climate.

It is projected that, on average, annual temperature will increase between 2 °C to 6 °C under high emission scenarios by the end of the 21st century, with serious consequences in food and nutrition security, especially within semi-arid regions of sub-Saharan Africa. This study aimed to investigate the impact of historical long-term climate (temperature and rainfall) variables on the yield of five major crops viz., sorghum, sesame, cotton, sunflower, and millet in Gedaref state, Sudan over the last 35 years. Mann–Kendall trend analysis was used to determine the existing positive or negative trends in temperature and rainfall, while simple linear regression was used to assess trends in crop yield over time. The first difference approach was used to remove the effect of non-climatic factors on crop yield. On the other hand, the standardized anomaly index was calculated to assess the variability in both rainfall and temperature over the study period (i.e., 35 years). Correlation and multiple linear regression (MLR) analyses were employed to determine the relationships between climatic variables and crops yield. Similarly, a simple linear regression was used to determine the relationship between the length of the rainy season and crop yield. The results showed that the annual maximum temperature (Tmax) increased by 0.03 °C per year between the years 1984 and 2018, while the minimum temperature (Tmin) increased by 0.05 °C per year, leading to a narrow range in diurnal temperature (DTR). In contrast, annual rainfall fluctuated with no evidence of a significant (p > 0.05) increasing or decreasing trend. The yields for all selected crops were negatively correlated with Tmin, Tmax (r ranged between −0.09 and −0.76), and DTR (r ranged between −0.10 and −0.70). However, the annual rainfall had a strong positive correlation with yield of sorghum (r = 0.64), sesame (r = 0.58), and sunflower (r = 0.75). Furthermore, the results showed that a longer rainy season had significant (p < 0.05) direct relationships with the yield of most crops, while Tmax, Tmin, DTR, and amount of rainfall explained more than 50% of the variability in the yield of sorghum (R2 = 0.70), sunflower (R2 = 0.61), and millet (R2 = 0.54). Our results call for increased awareness among different stakeholders and policymakers on the impact of climate change on crop yield, and the need to upscale adaptation measures to mitigate the negative impacts of climate variability and change.

Effects of injection directions and boundary exchange times on adaptive pumping in heterogeneous porous media: Pore-scale simulation