Effective Mitigation Strategies Research Articles

Ransomware Detection Using Machine Learning Techniques

The proliferation of ransomware attacks is a critical cybersecurity threat that organizations globally face. This situation necessitates effective prevention and mitigation strategies. These malicious programs encrypt data and extort payments, impacting various industries. They highlight the urgent need for robust defense mechanisms. Despite advancements in machine learning for ransomware detection, there is a notable gap in the comparative analysis of individual algorithms such as Decision Tree (DT), Support Vector Machine (SVM), and Multi-Layer Perceptron (MLP). This study aims to fill the gap by providing a comparative analysis of these algorithms. It focuses on using the UG Ransome dataset and key metrics including accuracy, precision, recall, and F-measure. The experiments were conducted using Python. The results demonstrate that the Decision Tree outperforms SVM and MLP across all metrics. It achieves an accuracy of 98.83%, precision of 99.41%, recall of 99.41%, and F-measure of 99.41%. SVM and MLP, on the other hand, achieved lower scores. These results highlight the Decision Tree's superior performance in capturing non-linear data relationships, which is crucial for ransomware detection. The major contribution of this study is the identification of the Decision Tree as a highly effective model for ransomware detection. It significantly outperforms other models. The findings suggest that the Decision Tree's ability to model complexities within the data makes it a robust and reliable tool for safeguarding systems against ransomware attacks. Future research should explore the Decision Tree's performance across diverse ransomware datasets, integrate ensemble learning, investigate adversarial machine learning techniques, and enhance real-time detection methods. These efforts will improve the robustness and applicability of machine learning-based ransomware detection systems.

Multifactor Mathematical Modeling and Analysis of the Impact of Extreme Climate on Geological Disasters

Objective: To investigate the impact of extreme climate on geological disasters in Shanxi and propose effective disaster prevention and mitigation strategies. Methods: Using daily temperature and precipitation data from 27 meteorological stations in Shanxi Province from 1975 to 2020, 32 extreme climate indices were calculated. Combined with geological disaster site data, the distribution characteristics of extreme climates and their relationship with geological disasters were analyzed, and a regression model for geological disaster risk zones was constructed. Results: Sixteen extreme climate indices in Shanxi Province showed significant changes, especially TMAXmean (100% significant). Indices related to negative precipitation effects showed a declining trend, with 77.78% being significant, while 96.3% of positive temperature effect indices showed an increasing trend, with 73.6% being significant. Geological disaster hotspots were concentrated in the mid-altitude (500–1500 m) hilly and low mountain areas along the central north–south axis and on Q and Pz strata. Extreme high-temperature indices were significantly positively correlated with geological disaster hotspots, while extreme low-temperature indices were negatively correlated. Indices related to extreme heavy precipitation (e.g., R99p.Slope, RX5day.Slope) were associated with an increase in geological disaster hotspots, whereas higher total precipitation and frequent heavy precipitation events were associated with a decrease in disaster hotspots. The grey relational degree between the Z-score and TXn.Slope, TXx.Slope, GSL.Slope, and TX90P.Slope was greater than 0.8. The random forest model performed best in evaluation metrics such as MAE, RMSE, and R2. Conclusions: Shanxi is likely to experience more extreme high-temperature and precipitation events in the future. The low-altitude hilly and terraced areas in Zones III and VII are key regions for geological disaster prevention and control. High temperatures and extreme rainfall events generally increase the disaster risk, while higher total precipitation reduces it. The random forest model is the optimal tool for predicting geological disaster risks in Shanxi Province.

A comprehensive approach to evaluate risk mitigation strategies in offshore wind farms using spherical fuzzy decision making analysis

The escalating impact of global warming, primarily exacerbated by carbon emissions from conventional energy sources, has prompted significant advancements in offshore wind energy as a pivotal avenue for sustainable development. Amid the surging interest in renewable energy, offshore wind-farms have emerged as a promising solution. To ensure their efficient and effective operation, it becomes imperative to systematically identify and mitigate potential risks. This study addresses the critical need to systematically prioritize risk reduction strategies for offshore wind farms, a problem that has not been comprehensively explored in the literature. A multi-criteria analysis has been adopted to simultaneously evaluate the contradictory criteria in the evaluation process. The novelty of this study lies in its integration of spherical fuzzy sets with multi-criteria decision-making (MCDM) techniques to handle uncertainties and evaluate contradictory criteria simultaneously. As a result of all this analysis, the most critical risks for offshore wind-farms and risk mitigation strategies that can be adopted within the determined risks have been revealed. The main findings reveal that turbine underperformance, disruption of habitats, and grid connection issues are the most critical risks. Furthermore, a combination of robust design and engineering and collaboration and partnerships emerged as the most effective risk mitigation strategies.

Forecasting infection fatality rate of COVID-19: measuring the efficiency of several hybrid models

The main goal of this paper is to delve into a crucial epidemiological metric the daily infection fatality rate in the context of the ongoing COVID-19 pandemic. The significance of understanding this metric lies in its potential to provide insights into the severity and impact of the virus on a daily basis. Methods: To achieve this overarching objective, we employ a comprehensive approach by applying various hybrid models that hybridize both machine learning and statistical techniques. In our pursuit of a deeper understanding, we leverage advanced machine learning algorithms, including Support Vector Machine and Random Forest. These techniques allow us to capture intricate patterns and relationships within the data, contributing to a more nuanced analysis of the infection fatality rate. The application of machine-learning models in epidemiological studies has gained prominence due to their ability to adapt to complex and evolving patterns inherent in infectious disease dynamics. Complementing our machine-learning arsenal, we integrate traditional statistical models such as ARIMA (AutoRegressive Integrated Moving Average), fractional ARIMA, and BATS (Bayesian Structural Time Series). Results. To assess the performance of these models, we employ key evaluation metrics, including Root Mean Squared Error (RMSE), Mean Squared Error (MSE), and Mean Absolute Error (MAE). These metrics serve as critical benchmarks, allowing us to quantify the accuracy and reliability of our models in predicting the daily infection fatality rate. A meticulous evaluation of model performance is crucial for ensuring the validity and of our findings. According to these measures, we see that hybrid models performed well especially ARIMA-RF model RMSE: 0.29, MSE: 0.084, MAE: 0.215 for the horizon 60 and for horizon 120 ARIMA-RF still the best performance, RMSE: 0.268, MSE: 0.071, MAE: 0.183, we get these results due to the capacity of this approach to handle complex patterns contrarily to other model ARIMA, BATS, RF and SVM. Conclusion. This work adopted this approach in order to build a model to predict infection fatality rate, we aspire to provide a nuanced understanding of the factors influencing the severity of the virus, ultimately contributing to the ongoing discourse on effective public health interventions and mitigation strategies.

Noise pollution assessment and management in rare earth mining areas: a case study of Kollam, Kerala, India.

Noise pollution is an unintentional consequence of mining activities, needing rigorous assessment, monitoring, and mitigation techniques to reduce its impact on local residents and ecosystems. The study specifically examines the noise pollution from rare earth mining activities in the Neendakara-Kayamkulam (NK) coastal belt, Kollam, Kerala, India, a region rich in ilmenite, rutile, sillimanite, zircon, and monazite. Despite the known environmental and health impacts of noise pollution, there is limited specific data on its magnitude and sources in this region, as well as a lack of effective mitigation strategies tailored to rare earth mining operations. Studies have indicated that mining operations, such as the movement of heavy mineral sands, considerably elevate noise levels, which have an effect on the environment's quality and public health. This study seeks to fill the gap by geospatial mapping and assessing the noise levels and recommend measures to effectively mitigate noise pollution. Systematic noise measurements were conducted at 48 suitable locations within the NK coastal belt, including residential, commercial, industrial, coastal, and silence zones. The noise levels vary from 49.1dB(A) near a religious place to 82.4dB(A) near the local industry. The study employs geospatial noise mapping and land cover superimposition to implement class-specific mitigation measures for noise pollution in a coastal vicinity mixed land use area, including natural and vegetative barriers, operational scheduling, zoning, and land use planning.

Assessment of heavy metal contamination in the surface sediments, seawater and organisms of the Pearl River Estuary, South China Sea

Coastal heavy-metal contamination poses significant risks to marine ecosystems and human health, necessitating comprehensive research for effective mitigation strategies. This study assessed heavy-metal pollution in sediments, seawater, and organisms in the Pearl River Estuary (PRE), with a focus on Cd, Cu, Pb, Zn, As, Hg, and Cr. A notable reduction in heavy metal concentrations in surface sediments was observed in 2020 compared to 2017 and 2018, likely due to improved pollution management and COVID-19 pandemic restrictions. Spatial analysis revealed a positive correlation between elevated heavy-metal concentrations (Cu, Pb, Zn, Cd, and As) and areas with significant human activity. Source analysis indicated that anthropogenic activities accounted for 63 % of the heavy metals in sediments, originating from industrial effluents, metal processing, vehicular activities, and fossil fuel combustion. Cd presented a high ecological risk due to its significant enrichment in surface sediments. Organisms in the PRE were found to be relatively enriched with Hg and Cu, with average As concentrations slightly exceeding the Chinese food-health criterion. This study identified high-risk ecological zones and highlighted Cd as the primary pollutant in the PRE. The findings demonstrate the effectiveness of recent pollution control measures and emphasize the need for ongoing monitoring and mitigation to safeguard marine ecosystems and human health.

Risk analysis of petroleum storage tank based on uncertain data incorporated into mapped Bow-tie to Bayesian network

The petroleum industry has an important impact on the environment, which is why risk analysis for a petroleum storage tank is required in the process industry. However, conducting an exhaustive study is frequently complicated due to the incomplete data based on historical system information, feedback, or test results. The main goal of this study is to develop an innovative framework that integrates the Bow-tie method with MIMAH (Methodology for the Identification of Major Accident Hazards). This approach specifically aims to identify the most critical accident scenario and comprehensively list all realistic causes (basic events) associated with petroleum storage tanks. Acquiring historical data on basic events poses challenges due to the lack, insufficiency, and imprecision of failure data. To address this obstacle and predict the system’s evolution, a combination of established methods for handling uncertainty and multi-criteria decision-making has been proposed. This combination incorporates a modified Fuzzy Analytical Hierarchy Process with Chang’s Extent Analysis Method (FAHP-EAM), fuzzy set theory, and expert elicitation, contributing to an extensive approach for managing uncertainty and making informed decisions in the absence of comprehensive historical data. Furthermore, the Bow-tie model is converted into a dynamic Bayesian network to improve risk analysis by integrating probabilistic reasoning and capturing the dynamic interactions among multiple risk factors. This integrated approach is validated on real scenarios. The utilization of dynamic Bayesian networks further facilitates the suggestion of scenarios to assess the contribution of basic events to the critical event and evaluate the resulting environmental impacts. Finally, the prediction of threat zones for various hazardous effects, such as toxicity, thermal radiation, and overpressure linked to chemical releases following the occurrence of the critical event, is carried out. The risk analysis outcomes from this study offer valuable data for assessment, enabling managers to improve the quality of risk management and formulate effective risk mitigation strategies.

Understanding climate change awareness and adaptation measures in urban settings: A study in Surakarta, Indonesia

Backgorund: The effects of climate change in the atmosphere are starting to manifest, impacting not only the natural world but also human cultures. In Indonesia, these impacts are expected to intensify over the coming decades, with rising temperatures, declining rainfall, and sea-level rise affecting coastal areas. Extreme weather events, such as flooding and droughts, are becoming more common, posing significant challenges to human existence, economic growth, and the natural environment. This research aims to provide a comprehensive understanding of climate change awareness and adaptation measures among adolescents and local people in Surakarta, Indonesia. The study seeks to assess how the local population perceives and responds to climate change, as well as to identify factors that make communities vulnerable to severe weather events. By exploring these factors, the study aims to shed light on the underlying causes of vulnerability and inform the development of effective adaptation and mitigation strategies. Method: The research methodology involves the use of structured questionnaires and surveys to collect data from 100 respondents in Surakarta. Statistical analysis, including regression analysis, is employed to assess the relationship between various demographic factors and climate change awareness. Demographic factors such as age, education level, and income are evaluated to understand their impact on climate change perception and responsiveness. Findings: The findings of the study highlight the importance of enhancing climate change knowledge among adolescents and local residents and underscore the need for targeted adaptation measures to address the unique challenges faced by urban communities in Surakarta. Conclusion: Overall, this study contributes to the broader understanding of climate change adaptation and resilience-building efforts in urban settings and provides valuable insights for policymakers, researchers, and community stakeholders working in the field of climate change mitigation and adaptation. Novelty/Originality of this article: Using a structured survey, this study assessed climate change awareness and adaptation measures among adolescents and residents. The findings highlight the importance of improving knowledge about climate change and developing appropriate adaptation strategies for urban communities.

Activating Biocake Communities Retards Jumps of Transmembrane Pressure in Membrane Bioreactors.

Sudden jump of transmembrane pressure (TMP) in membrane bioreactors (MBRs), associated with abrupt aggravation of membrane fouling, limits practical applications of MBRs and calls for effective mitigation strategies. While the TMP jump is generally related to the bacterial activity of biocakes, the mechanisms underlying the TMP jump remain unclear. Herein, we conducted various backwash protocols with different nutrient (e.g., nitrate and sodium acetate) loadings on fouled membranes in MBRs to reveal the critical role of bacterial activity of biocakes for the TMP jump. The filtration tests showed a lower TMP jump rate for the membrane backwashed with a nutrient solution (a mixture of 180 mg/L NaNO3 and 200 mg/L NaAc, averaged at 1.40 kPa/d) than that backwashed with tap water (averaged at 3.56 kPa/d), implying that TMP jump could be efficiently mitigated by providing sufficient nutrients to biocake bacteria. The characterization of biocakes showed that high-nutrient solution backwash considerably increased bacterial viability and activity, while considerably reducing biomolecule accumulation on membranes. The keystone taxa (e.g., g_Aeromonas and o_Chitinophagaceae) in the network of nutrient-enriched biocake communities were involved in nitrate reduction and biomolecule degradation. Ecological null model analyses revealed that the deterministic manner mainly shaped biocake communities with high-nutrient availability. Overall, this study highlights the significance of the bacterial activity of biocakes for TMP development and provides potential alternatives for controlling membrane fouling.

Quantitative assessment of benefits and losses from the influence of atmospheric nitrogen deposition on cropland ecosystems in China

China is facing severe atmospheric nitrogen (N) deposition. Assessing the economic benefits and damage costs induced by N deposition can help to develop effective mitigation strategies for N emissions. A net economic benefit method was used to assess the economic impact of N deposition in cropland ecosystems in China in 2020. The results showed that atmospheric N deposition gained an economic benefit of $4896.0 million through increased yields of major grain crops and a climate benefit of $1259.3 million through cooling effects. On the other hand, N deposition induced economic losses of $6257.1 and $1063.4 million, respectively to human health and ecosystem health; excessive N deposition induced damages of $137.8 million due to reduced crop yields and $168.4 million due to the increased greenhouse gas emissions. In general, the net economic benefit was -$1471.5 million (−$5324.7 ∼ $921.4 million), indicating that China is suffering economic losses due to N deposition in cropland ecosystems. These results would provide scientific data for the government to enact efficient measures to reduce N pollution.

Drought trend and its association with land surface temperature (LST) over homogeneous drought regions of India (2001–2019)

Droughts pose significant challenges to food security, affecting millions of people and hectares of land in India. Despite their widespread impact, assessing drought patterns at a high spatial resolution remains a challenge, particularly in regions with poor data availability. Standardised Precipitation Evapotranspiration Index (SPEI) is one of the most widely accepted drought indices. The publicly available SPEI reanalysis dataset has a poor spatial resolution for regional drought studies. Land Surface Temperature (LST) has the potential to act as an effective proxy for SPEI and can be used for high-resolution drought studies. The study considered six homogeneous drought regions (NWH, CPR, NEI, NCP, NWI, and SPR) over India, and the recent trend in LST and SPEI over these regions was estimated using modified Mann-Kendell (MMK) trend and Sen’s slope estimator at monthly, seasonal, and annual time scales. The correlation between SPEI and LST was also checked at a 95% confidence level. Results indicate statistically significant decreasing annual LST trends in NWH (slope = −0.10), CPR (slope = −0.09), and NWI (slope = −0.11) regions, while increasing SPEI trends were observed in NWI (slope = 0.06) and NCP (slope = 0.01) regions during 2001–2019. Additionally, NCP (p = 0.025), CPR (p = 0.002), and NWI (p = 0.020) exhibited significantly decreasing LST trends during winter, with a notable post-monsoon decrease observed in NWI. Significant correlations between annual LST and SPEI were observed only in CPR (r =−0.527) and NWI (r = −0.601), with seasonal associations found predominantly in winter across CPR, NEI, NCP, and NWI regions. Notably, a significant correlation was observed in all months except July over NWI, with consistent correlations during most of the winter months in NWH, CPR, NCP, and NWI regions. The observed trends and correlations can provide valuable insights for policymakers and stakeholders in formulating effective drought mitigation strategies.

Prevalence of plant pathogenic nematodes in banana fields of Sylhet district, Bangladesh

Plant pathogenic nematodes pose a significant threat to banana production, causing symptoms such as root lesions, stunted growth, and reduced yield. A comprehensive study in Bangladesh’s Sylhet district aimed to assess nematode infestations in banana fields, involving interviews with farmers, soil sample collection, and microscopic examination. Results revealed the presence of multiple nematode species, with Helicotylenchus spp. being the most abundant. PCR analysis confirmed R. similis infestation in 35% of fields, highlighting its prevalence. Monoculture practices were observed among farmers, exacerbating nematode infestation risks. Limited communication between farmers and agricultural offices contributes to unawareness of nematode infestations. Country-wide studies and improved communication are crucial for implementing effective nematode mitigation strategies and safeguarding banana yield.

Using artificial neural networks and citizen science data to assess jellyfish presence along coastal areas

Abstract Jellyfish blooms along coastal areas can pose significant challenges for beach users and local authorities. Understanding the factors influencing jellyfish presence is crucial for effective management and mitigation strategies. In this study, citizen science data from the Andalusian coast (232 beaches, in 40 different localities) and machine learning techniques are used to investigate if the presence and absence of jellyfish along coastal areas can be predicted. A multi‐layer perceptron (MLP) neural network was employed to classify user comments regarding jellyfish presence or absence, achieving an accuracy of approximately 96%. The MLP model demonstrated robustness in handling non‐linear classification problems and noise, although it showed lower precision for predicting jellyfish presence, likely due to an imbalance in the dataset. Environmental data were also incorporated to characterise the influence of sea surface temperature, wind direction and wind speed on jellyfish distribution. The results align with previous studies, suggesting these environmental factors significantly impact jellyfish presence. Synthesis and applications. This research provides actionable recommendations for beach management. The implementation of continuous monitoring of sea surface temperature and wind conditions will enable more accurate predictions of jellyfish distribution. Adaptive management strategies that respond dynamically to environmental data will help mitigate the impact of jellyfish blooms on coastal tourism and public health.

Optimizing land use for climate mitigation using nature based solution (NBS) strategy: a study on afforestation potential and carbon sequestration in Rajasthan, India

Rajasthan faces significant environmental challenges, including the pressing need for effective climate change mitigation strategies. Recognizing afforestation as a vital tool in this endeavor, this study leverages the latest in remote sensing and geospatial analysis to map out the state's afforestation potential. The goal is to assess land suitability across Rajasthan for afforestation projects and evaluate the potential carbon sequestration capabilities of different tree species. This aims to inform sustainable land management strategies that can contribute to climate change mitigation. By integrating satellite imagery, land cover data, terrain analysis, and vegetation indices, the study evaluates environmental factors such as slope, soil moisture, and vegetation health to identify areas optimal for tree planting. A carbon sequestration model was also developed to estimate sequestration rates based on species-specific growth patterns. Findings indicate that approximately 40% of Rajasthan is suitable for afforestation, with potential carbon sequestration rates ranging from 2 to 8 tons per hectare per year. Species such as Azadirachta indica (Neem) and Prosopis cineraria (Khejri) are identified as particularly effective for short-term sequestration, while Phyllanthus emblica (Amla) and Ziziphus mauritiana (Ber) are better suited for long-term carbon capture. This research highlights the importance of targeted afforestation using suitable tree species as a nature-based solution (NBS) for climate change mitigation in Rajasthan. The study offers a data-driven approach to enhancing ecosystem resilience and supports decision-making for climate adaptation in arid regions, highlighting the significant potential of Rajasthan's land for carbon sequestration through afforestation.

Prioritising urban heat island mitigation interventions: Mapping a heat risk index

The global climate is under threat from increasing extreme heat, evidenced by rising temperatures and a surge in hot days. Heat waves are intensifying worldwide, impacting cities and residents, as demonstrated by the record-breaking heat experienced in the UK in 2022, which resulted in over 4500 deaths. Urban heat islands (UHIs) exacerbate these heat waves, making city residents more vulnerable to heat-related deaths. UHIs occur when temperatures in urban areas exceed those in surrounding rural areas due to the heat-absorbing properties of urban structures. Implementing mitigation strategies, such as green infrastructure, is crucial for enhancing urban resilience and reducing vulnerability to UHIs.Effectively addressing UHIs requires a systematic approach, including developing risk maps to prioritise areas for UHI mitigation strategies. Using remote sensing, GIS, and SPSS correlational analysis, the research aims to develop and assess a Heat Risk Index (HRI). This index integrates UHI spatial intensity, current green cover, and population density at the district level to develop the risk index. This study stands out for its novel approach to developing the HRI, focusing on the localised impact of the UHI in Manchester City, identifying high-risk heat-vulnerable districts, and prioritising implementing effective UHI mitigation strategies.The findings highlight the importance of this approach, revealing that approximately 30 % of Manchester City is affected by UHI effects, with areas near the city centre, characterised by higher population density and reduced green cover, being particularly vulnerable. Furthermore, the study suggests that applying HRIs at a more localised level, such as the neighbourhood level rather than the district level, would provide more relevant and targeted insights for mitigating UHI. A more localised index would offer tailored insights into the unique conditions of each neighbourhood within the districts, enabling more effective mitigation strategies. The HRI developed in this paper serves as a test for a more nuanced and comprehensive index, considering additional variables related to population vulnerability and city urban structure.

Mitigating biases in big mobility data: a case study of monitoring large-scale transit systems

ABSTRACT Big mobility data (BMD) have shown many advantages in studying human mobility and evaluating the performance of transportation systems. However, the quality of BMD remains poorly understood. This study evaluates biases in BMD and develops mitigation methods. Using Google and Apple mobility data as examples, this study compares them with benchmark data from governmental agencies. Spatio-temporal discrepancies between BMD and benchmark are observed and their impacts on transportation applications are investigated, emphasizing the urgent need to address these biases to prevent misguided policymaking. This study further proposes and tests a bias mitigation method. It is shown that the mitigated BMD could generate valuable insights into large-scale public transit systems across 100+ US counties, revealing regional disparities of the recovery of transit systems from the COVID-19. This study underscores the importance of caution when using BMD in transportation research and presents effective mitigation strategies that would benefit practitioners.

Microplastics with different functional groups modulate cellular and molecular mechanisms of reduced graphene oxide toxicity on the green microalga, Scenedesmus obliquus

Even though microplastics (MPs) and graphene nanomaterials (GNMs) have demonstrated individual toxicity towards aquatic organisms, the knowledge gap lies in the lack of understanding regarding their combined toxicity. The difference between the combined toxicity of MPs and GNMs, in contrast to their individual toxicities, and furthermore, the elucidation of the mechanism of this combined toxicity are scientific questions that remain to be addressed. In this study, we examined the individual and combined toxicity of three polystyrene microplastics (MPs) with different functional groups—unmodified, carboxyl-modified (COOH-), and amino-modified (NH2-) MPs—in combination with reduced graphene oxide (RGO) on the freshwater microalga Scenedesmus obliquus. More importantly, we explored the cellular and molecular mechanisms responsible for the observed toxicity. The results indicated that the growth inhibition toxicity of RGO, either alone or in combination with the three MPs, against S. obliquus increased gradually with higher particle concentrations. The mitigating effect of MPs-NH2 on RGO-induced toxicity was most significant at a higher concentration, surpassing the effect of unmodified MPs. However, the MPs-COOH did not exhibit a substantial impact on the toxicity of RGO. Unmodified MPs and MPs-COOH aggravated the inhibition effects of RGO on the cell membrane integrity and oxidative stress-related biomarkers. Additionally, MPs-COOH exhibited a stronger inhibition effect on RGO-induced biomarkers compared to unmodified MPs. In contrast, the MPs-NH2 alleviated the inhibition effect of RGO on the biomarkers. Furthermore, the presence of differently functionalized MPs did not significantly affect RGO-induced oxidative stress and photosynthesis-related gene expression in S. obliquus, indicating a limited ability to modulate RGO genotoxicity at the molecular level. These findings can offer a more accurate understanding of the combined risks posed by these micro- and nano-materials and assist in designing more effective mitigation strategies.

Vertically resolved meteorological adjustments of aerosols and trace gases in Beijing, Taiyuan, and Hefei by using RF model

Air pollution represents a complex phenomenon defined by the presence of various gases and particulate matter, leading to intricate spatio-temporal fluctuations. This study aims to enhance our understanding of how meteorological factors influence trace gases and aerosols, exacerbating air pollution in various geographical locations, specifically in Beijing's Fengtai (BJFT), Taiyuan City (SXTY), and Hefei's Science Island (HFDP). The study employs 2D-MAX-DOAS observations and utilizes the Random Forest (RF) model to decouple the influence of meteorological conditions from pollutant data. The vertical profile of nitrogen dioxide (NO2), sulfur dioxide (SO2), formaldehyde (HCHO), and aerosols at each study site was classified into four distinct layers, followed by conducting a meteorological decoupling analysis on each layer. This decoupling analysis demonstrates that meteorology significantly influences aerosols across all sites, with reductions ranging from 75 % to 95 % after de-weathering. SO2 shows minimal susceptibility, with the changes ranging from ±20 % to ±60 % after de-weathering. Among all sites, BJFT's pollutants exhibit less susceptibility overall, while pollutants at HFDP are more susceptible. The findings further reveal significant meteorological interventions in pollutants in surface layers (0.05 km and 0.2–0.4 km) at BJFT, with some exceptions at SXTY. However, pollutants, particularly NO2 and aerosols in higher layers (0.6–0.8 km and 1.0–1.2 km) at HFDP, also experience significant meteorological interferences. The findings at HFDP and SXTY reveal that removing meteorological influence also adjusts the profile shape of pollutants. For instance, the NO2 profile at HFDP during the winter season shifted from a bimodal to an exponential shape after de-weathering. Overall, this study sheds light on the complex interplay between meteorological factors and trace gases at various altitudes across different geographic locations, offering insights crucial for holistic and effective pollution mitigation strategies.

Pemetaan Daerah Rawan Tanah Longsor di Daerah Desa Sanggang, Kecamatan Bulu, Kabupaten Sukoharjo

<p>Landslides are one of the frequent natural disasters occurring in various regions of Indonesia, including the Village of Sanggang in Bulu District, Sukoharjo Regency, Central Java. This area boasts diverse topography with numerous steep slopes, rendering it susceptible to landslides. The Bulu District is located in a hilly region with a high vulnerability to landslides, where from 2019 to 2020, there were recorded 11 landslide disaster events, with 10 of them occurring in the Bulu District. This study aims to map landslide-prone areas in the Village of Sanggang using a spatial analysis approach and relevant risk factors. The analytical methods include the use of topographical data, soil types, rainfall patterns, and historical landslide occurrences. The mapping results will provide a deeper understanding of locations vulnerable to landslides in this area. The outcomes of this research are expected to serve as a foundation for the development of more effective mitigation strategies, including recommendations for prevention measures and early warnings. A better understanding of these susceptible areas will assist local government authorities and stakeholders in safeguarding their communities and assets from the potential landslide hazards in the Village of Sanggang, Bulu District, Sukoharjo Regency.</p>

Assessing the dynamics and trigger factors of drought propagation in the China-Pakistan economic corridor: A three-dimensional perspective

Effectively managing drought in the China-Pakistan Economic Corridor (CPEC) region requires a precise understanding of the three-dimensional characteristics of meteorological drought (MD) and agricultural drought (AD), as well as the factors that trigger their propagation. This study employed non-stationary drought indices (NSPEI and SSMI) to develop a cutting-edge 3-dimensional drought identification model. This model was used to detect MD and AD patterns from 1981 to 2022 in the CPEC region and was integrated with binomial logistic regression to identify the critical factors that drive drought propagation. This study's key findings include: 1) Between 1981 and 2022, droughts in Xinjiang, China, exhibited a discernible southward migration trend, while in Pakistan, droughts showed a northward migration pattern. Drought frequency and extent have increased over time, with affected regions becoming more widespread in CPEC. Notably, drought events with higher preceding drought contagion indices (DCI) were more likely to evolve into extreme, long-term droughts. 2) Drought area emerged as a significant positive triggering factor for drought propagation in the CPEC region. Conversely, snowmelt in Xinjiang and the leaf area index for low vegetation in Pakistan acted as triggering elements affecting negatively. 3) Various factors played a pivotal role during drought propagation process, including geographical coordinates of drought centroids, DCI, and temperature variations. Additionally, snowmelt and snow evaporation significantly impacted drought propagation in Xinjiang, while vegetation cover in Pakistan played a crucial role during the drought propagation process. By utilizing four regression models and conducting comprehensive attribution analysis, this study sheds light on the characteristics of drought propagation and the factors influencing it. These findings are valuable for enhancing early warning systems and implementing effective drought mitigation strategies in the CPEC region.