Wind shear presents a significant hazard to aviation, particularly during critical flight phases such as takeoff and landing. On 12 February 2025, six aircraft at Soekarno–Hatta International Airport conducted go-arounds due to wind shear events between 07:35 UTC and 08:37 UTC. This study employs meteorological observations (AWOS and METAR), remote sensing data (Doppler radar and wind profiler), and pilot reports (PIREPs) to investigate the underlying meteorological mechanisms and assess the effectiveness of issued warnings.
Doppler radar detected a Sea Breeze Front (SBF) moving inland, which created sharp thermal and pressure gradients, further intensifying wind shear along the final approach path. Wind profiler data revealed substantial vertical wind variations of up to 3,000 m, while surface observations recorded gusts reaching 28 knots, indicating significant near-surface turbulence. METAR reports issued "WS ALL RWY" (wind shear affecting all runways) warnings at 07:30 UTC and 08:00 UTC, coinciding with pilot-reported wind shear encounters. Additionally, Wind Shear Warnings and an Aerodrome Warning for strong winds were disseminated via the Aeronautical Message Handling System (AMHS) and other communication channels, ensuring timely and effective communication with relevant stakeholders. This proactive approach enabled air traffic controllers and flight crews to implement risk mitigation measures, minimizing operational disruptions.
These findings offer crucial insights into the operational impacts of wind shear and underscore the need for continuous advancements in meteorological support for aviation. Strengthening early warning systems and fostering interdisciplinary collaboration between meteorologists, air traffic controllers, and pilots are essential for enhancing aviation safety, particularly in tropical regions prone to localized wind shear phenomena.

As global temperatures continue to rise, understanding the relationship between climate and vegetation is crucial for agriculture and for mitigating and adapting to environmental changes. The complex interaction between vegetation and climate becomes even more significant as temperatures increase, making it essential to comprehend these changes in the environment. This study examines how vegetation dynamics in the Sylhet Region have been affected by long-term temperature changes over the past four decades. This study aims to uncover changes in temperature trends and vegetation growth over recent decades and how it has impacted the climate of the Sylhet region. This research utilizes the Normalized Difference Vegetation Index (NDVI) data from satellites, and statistical analysis techniques are used to examine the relationship between vegetation and temperature in Sylhet. This research shows how changes in temperature affect vegetation health and density. Satellite imagery shows a steady decrease in vegetation density in the Sylhet region. Local climatic variables such as temperature, precipitation, and humidity, as well as anthropogenic factors like urbanization and deforestation, are identified as primary factors behind the loss of vegetation. This study shows a decreasing effect of climate-driven changes on vegetation over time by closely examining the relationship between NDVI and regional climatic parameters. This implies that in recent years, the direct impact of climatic variability has been less significant than the involvement of human-induced variables, such as urbanization, land-use changes, and deforestation, in shaping vegetation patterns.

The state of Zacatecas, Mexico, has a territorial extension of 75,275.3 km² and is located in the center-north of the country. The climate of Zacatecas is classified as arid and semi-arid. However, in recent decades, the climate in the region has changed, making it necessary to observe, predict, and determine which renewable energy source would be ideal for supporting the metereological sector, depending on the region. Agriculture and livestock farming are also affected. For this reason, the description of a portable prototype that is designed, developed, and implemented to measure meteorological variables and obtain indirect variables is presented. A microcontroller from the Arduino family was used for this purpose. The variables of temperature, humidity, and atmospheric pressure were measured. The variables of the UV index, thermal sensation, dew point, altitude above sea level, and air density were measured indirectly. An interface was created to check the data in real time via the Internet. The information can be checked from a cell phone, an electronic tablet, or a computer using a program developed in the HTML language. The information can also stored on a micro-SD memory device. The first results were collected over 45 days. The sampling of the data that were read by the system took 10 seconds. The data were compared with those obtained from a commercial meteorological station, which produced similar results. The design of the meteorological station will be further improved by adding new measurement variables and setting up a series of portable stations in different regions of the state.

This study investigates the intricate dynamics of typhoon-like vortex track deflections over complex terrain, explicitly focusing on Taiwan Island. We develop a novel dynamic model based on the conservation of potential vorticity (PV) that incorporates a topographic adjusting parameter (α) and a meridional adjusting velocity (MAV) to capture the vortex's response to terrain variations. We elucidate the fundamental mechanisms driving track sensitivity and predictability using idealized simulations and real-case scenarios that use Taiwan's topography. Our results show that steeper terrain gradients consistently deflect tracks, with this topographic steering effect amplified for stronger vortices due to their more significant α value, leading to an enhanced MAV and more pronounced deflections. Shallower impinging angles, resulting in prolonged interactions with steep terrain, further enhance these deflections. We identify distinct Track Diverging Zones (TDZs) and Track Converging Zones (TCZs) associated with Taiwan's Central Mountain Range (CMR), highlighting the significant impact of the initial position of a vortex and terrain resolution on forecast reliability. The model successfully captures the key features of vortex–topography interactions, providing a physical basis for understanding the observed variability in typhoon tracks near Taiwan. This work demonstrates the practical value of a dynamic modeling perspective and PV analysis in improving typhoon track forecasting and risk assessments in regions with complex topography. It suggests that future research should focus on refining numerical models.

Introduction

Since 2016, smog has been an annual health and environmental crisis in Pakistan, commonly known as the “fifth season.” This hazardous phenomenon results from a combination of vehicle emissions, industrial pollution, crop residue burning, and fossil fuel consumption. Smog season intensifies in October, dropping visibility considerably while increasing respiratory problems and antimicrobial resistance. We seek to analyze the correlation between deteriorating air quality and rising public health concerns, specifically antimicrobial resistance.

Methods

This study was supported by multiple sources, including the existing literature, environmental data, air quality indices (AQIs), and public health reports, to assess the impact of smog on respiratory ailments and antimicrobial resistance. The global study on particulate matter proved to be resourceful. Additionally, we reviewed the data from the U.S. Environmental Protection Agency (EPA) and NASA’s research statistics regarding cross-border crop burning. The relation between airborne pollution levels and antimicrobial resistance rates was interpreted based on the direct correlation found between them.

Results

In 2024, Pakistan’s urban cities reported toxic AQI levels, with Multan reaching an unprecedented 1,392, followed by Lahore (826), Peshawar (575), and Rawalpindi (271). A 10% rise in PM 2.5 levels caused a 2.6% increase in antimicrobial resistance in Pakistan. This led to premature deaths and difficulty in disease management. The government was forced to take temporary measures such as school closures, enforcing work-from-home policies, and limiting commercial activities. However, these short-term actions failed to deal with the root causes of pollution.

Conclusion

The smog crisis in Pakistan is causing serious environmental and public health concerns, particularly by accelerating antimicrobial resistance. Without resolute action, it will continue to jeopardize public health and strain the health care system. Policy recommendations involve stricter emissions standards, large-scale afforestation, investment in clean energy, and global collaboration to reduce crop burning. While emergency measures provide momentary relief, long-term sustainable solutions are necessary to mitigate the problem.

Numerical Weather Prediction (NWP) models are essential tools for forecasting atmospheric weather conditions and mitigating weather-related hazards. This research investigates the integration of certain atmospheric and weather parameters (rainfall, temperature, relative humidity, cloud cover, geomagnetic storms, and solar radiation) into NWP models to enhance the forecasting accuracy to a high degree. This research evaluates different NWP approaches, including deterministic and ensemble models, focusing on the parameterization schemes and data assimilation techniques. The NWP model was selected due to its flexibility in regional forecasting. Simulations were conducted using different parameterization schemes to ascertain the impact of geomagnetic and solar influences on weather conditions. This research employed data assimilation techniques, including 4D-Var, to integrate real-time observations into the model. The results demonstrate forecast reliability that was improved by a reasonable percentage by the addition of solar and geomagnetic influences, contributing to improved disaster preparedness and climate risk management and control. A stronger correlation (R = 0.85) between the modeled and observed cloud cover was obtained when both solar and geomagnetic influences were incorporated. The outcomes of this research are expected to offer valuable insights for improving weather forecasting accuracy, strengthening early warning systems, and enhancing preparedness for potential weather-related disasters, thereby providing more robust and effective risk and hazard management strategies. This study underscores the role of comprehensive atmospheric modeling techniques in strengthening early warning systems and reducing the uncertainties in weather forecasts.

The nature of the activities carried out in open-pit mines necessitates appropriate and efficient management of the dispersion of pollutants generated and of local air quality levels. The blasting, excavation, and transportation of minerals are some of the main mining activities that can cause the release of particles into the atmosphere. These particles may contain heavy metals and other chemical species that can affect the respiratory health of people living near mines.

In this contribution, innovative techniques related to air quality modelling and remote sensing have been evaluated. These three techniques aim to address previously unanswered questions and sources of uncertainty identified based on the authors’ experience, with areas such as the following: (1) recommended emission factors for blasting activity in copper mines do not exist; (2) to adapt environmental management and ensure compliance with legislation, the concentration of particulate matter for the next few hours, depending on meteorological conditions and the mine operation plan, should be known; and (3) methods of generating a heat map of the particulate matter levels in the mine and nearby populations.

To respond to these questions, we have tested innovative techniques: (a) a semi-empirical approach based on real data and Gaussian dispersion modeling has been used to accurately estimate the emission factors of particulate matter in the atmosphere related to blasting activity; (b) a data-science model has been prepared to generate a nowcasting of the levels of particulate matter considering, mainly, the evolution of the meteorological conditions and a large amount of historical data; and (c) an air quality monitoring service has been used that derives particulate matter properties from space by transforming public satellite data, and other public sources has been tested. These techniques have been evaluated over one of the most relevant open-pit mines in southern Europe: the Riotinto mine, Huelva (Spain).

Cairo is one of the most populous cities in the world. The city’s unplanned urbanization has led to significant challenges, including an increase in Land Surface Temperature (LST), which is defined as the skin temperature of the land. LST is crucial for studies on radiative temperature in this densely populated and rapidly developing metropolitan area. The aim of this research is to analyze the seasonal variations in LST using MODIS Aqua satellite data, which offer a spatial resolution of 1 km and a temporal resolution of twice daily over an extended period from 2003 to 2022. This analysis involves calculating longitudinal and latitudinal temperature differences during both day and night to assess LST variations across different seasons, as well as annually, between the central area defined by coordinates (29Ëš.89´ to 30Ëš.15´ N and 31Ëš.17´ to 32Ëš E) and the surrounding regions extending from south to north (29Ëš.69´ to 30Ëš.35´ N) and from west to east (30Ëš.65´ to 31Ëš.75´ E). Additionally, this study examines the daytime and nighttime averages of the spatial and temporal distributions of LST for each season. The results indicate that, compared to daytime measurements, both the city and its suburbs exhibit a closer alignment with the annual average during nighttime across south-to-north and west-to-east charts. During nighttime, temperature differences suggest that central areas experience higher temperatures than surrounding suburbs; however, conditions differ during daytime measurements when temperature differences indicate that central areas have cooler temperatures compared to neighboring suburbs. The warm seasons—summer, autumn, and spring—show higher temperatures in central Cairo compared to the cold season. The number of LST values peaks in summer rather than in other seasons in Cairo. This research contributes to a broader understanding of LST dynamics and provides valuable insights for policymakers, urban planners, and researchers alike.

Assam, one of India’s richest regions in terms of biodiversity, faces significant environmental threats due to regional climate variability and anthropogenic pressures. This study explores a GIS and machine learning (ML)-based approach to assess the impact of climate change on forest and biodiversity vulnerability across the state. This analysis involves eight parameters: the Biological Richness Index (BRI), Disturbance Index (DI), Forest Canopy Density (FCD), Fire Point Intensity (FPI), Biomass Extraction Intensity (BEI), Slope, Standardized Precipitation Index (SPI), and Flood Vulnerability Index (FVI). Using the Analytical Hierarchy Process (AHP), these indicators were weighted and synthesized to develop a composite Forest and Biodiversity Vulnerability Index (FBVI). The major focus of this study is the delineation of climate change hotspots and their correlation with forest and biodiversity vulnerability zones. The assessment identified 19 grids as very highly vulnerable and 68 grids as highly vulnerable in terms of their forests and biodiversity. Climate change hotspot mapping revealed a further 38 grids experiencing very high climate exposure and 47 grids with high climate exposure which significantly overlap with vulnerable forested regions. This correlation suggests that regions with high climate sensitivity are also at increased risk of forest degradation, biodiversity loss, and ecosystem instability. An analysis of the trend over 22 years indicates a 1.56% decline in total forest cover and a 4.06% reduction in very dense forest cover, driven by factors such as a 9.3% increase in cropland area and a 1% expansion in settlements. The FPI analysis highlights six districts as very highly prone to fire incidents, while nine districts are highly prone to such events. The Standardized Precipitation Index (SPI) trends classify seven districts as very highly drought-prone zones, further exacerbating forest vulnerability. The Flood Vulnerability Index (FVI) identifies regions highly susceptible to flooding, stressing the compounded impacts of hydrological changes on Assam’s forest ecosystems. The results emphasize the urgent need for climate-adaptive forest management strategies, including remote sensing-based monitoring, participatory conservation approaches, and policy-driven interventions.

The petrochemical industry emits large amounts of nitrogen oxides (NOx). It is the second source of volatile organic compounds (VOCs), which, through photochemical reactions, can form tropospheric ozone (O₃) and, together with geographic and meteorological conditions, determine pollution's spatial and temporal behavior. The objective of this study is to assess the influence of air pollutants NOx, NO₂, and NO, as well as meteorological factors, on O₃ concentration levels in the city of Cadereyta, Nuevo Leon, which is characterized by its petrochemical industry as part of the metropolitan area of ​​Monterrey, Mexico. The data were analyzed using the Spearman correlation coefficient, identifying a weak to moderate negative association between NOx and NO₂ with O₃ in the spring season and a null relationship in the summer. However, the fall and winter seasons observed a moderate to strong negative relationship. Subsequently, a multiple linear regression analysis determined the influence of air pollutants NOx, NO₂, and NO, as well as meteorological factors, on O₃ concentration levels. In this sense, when the concentration levels of NOx and NO₂ decrease, the concentration of O₃ will increase proportionally according to the year's season. The prediction model obtains a coefficient of determination (R²) of 0.61 and a value in the root-mean-square error (RMSE) metric of 0.0107 ppm. In the prediction model, all variables presented a significant effect on the interpretation of the dependent variable, and the independent variables that provided the most significance in the variation in the concentration levels of O₃ were NOx and NO₂.