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Air pollution is a serious public health issue in Pakistan’s metropolitan cities, including Lahore, Karachi, Faisalabad, Islamabad, and Rawalpindi. Pakistan’s urban areas are vulnerable due to air pollution drivers such as industrial activities, vehicular emissions, burning processes, emissions from brick kilns, urbanization, and other human activities that have resulted in significant human health issues. The purpose of this study was to examine the impact of air pollutants and smog, as well as their causes and effects on human health. The PRISMA technique was used to assess the impact of environmental contaminants on human health. This study looked at air pollution sources and pollutants such as PM_2.5, PM₁₀, CO₂, CO, SO_X, and NO_x from waste combustion and agriculture. The population included people of all ages and sexes from both urban and rural areas of Pakistan. Data were retrieved and analyzed using SRDR⁺ software and Microsoft Excel spreadsheets. The data suggested that Karachi and Lahore had the highest levels of air pollution and disease prevalence, which were attributed to heavy industrial activity and traffic emissions. Smog was a serious concern in Lahore during winter, contributing to the spread of several diseases. Other cities, including Islamabad, Rawalpindi, Jhang, Sialkot, Faisalabad, and Kallar Kahar, were impacted by agricultural operations, industrial pollutants, brick kilns, and urbanization. Due to these drivers of air pollution, some diseases such as respiratory and cardiovascular diseases had notably higher incidences in these cities. Other diseases were connected with air pollution exposure, asthma, eye and throat problems, allergies, lung cancer, morbidities, and mortalities. To reduce air pollution’s health effects, policies should focus on reducing emissions, supporting cleaner technologies, and increasing air quality monitoring. Full article

Data4HumanRights’ training materials have been developed as open-source and tailored to limited-resource settings, where community data collectors often live and work. Access to training on data collection, analysis, and visualisation to support the advocacy of vulnerable groups is essential, particularly in the context of increasing human rights challenges such as land rights, adequate housing, conflicts, and climate justice. This paper provides an overview of how the training materials were co-developed with community data collectors in Nigeria and Kenya, offering insights into the fundamental principles (i.e., inclusiveness, adaptive, limited resources, and being gender- and incentive-sensitive) and the structure of the open-access training materials. The development process resulted in 28 modules, each designed to be delivered in a face-to-face format in less than one day by a local trainer. To maximize adaptivity, the training modules can be mixed and matched (e.g., as individual modules or a learning path of several modules around a specific training need). The individual modules cover a range of methods and tools that are useful to human rights work and community advocacy, e.g., documenting evictions, performing rapid needs assessments after acute crises, community profiling, and monitoring community development indicators. The training materials contain instructions for the training facilitator(s) and all necessary training materials. To ensure inclusivity, the training covers both basic and advanced topics, with most modules designed to address basic needs that can be followed using a mobile phone, thereby avoiding the need for computers or printed handouts. The training results in Nigeria and Kenya showcase applications, including mapping waste problems and addressing forced evictions. Trained community groups produced maps of waste piles to prioritize community actions, such as finding space for urban agriculture, and conducted rapid needs assessments during a massive eviction. This approach helps reduce power imbalances and empowers community groups to effectively manage and utilise their own data. Full article

The pastoral areas of Ethiopia are facing a recurrent drought crisis that significantly affects the availability of water resources for communities dependent on livestock. Despite the urgent need for effective drought early warning systems, Ethiopia’s pastoral areas have limited capacities to monitor variations in the intensity–duration–frequency of droughts. This study intends to drive drought intensity–duration–frequency (IDF) curves that account for climate-model uncertainty and spatial variability, with the goal of enhancing water resources management in Borana, Ethiopia. To achieve this, the study employed quantile delta mapping to bias-correct outputs from five climate models. A novel multi-model ensemble approach, known as spatiotemporal reliability ensemble averaging, was utilized to combine climate-model outputs, exploiting the strengths of each model while discounting their weaknesses. The Standardized Precipitation Evaporation Index (SPEI) was used to quantify meteorological (3-month SPEI), agricultural (6-month SPEI), and hydrological (12-month SPEI) droughts. Overall, the analysis of historical (1990–2014) and projected (2025–2049, 2050–2074, and 2075–2099) periods revealed that climate change significantly exacerbates drought conditions across all three systems, with changes in drought being more pronounced than changes in mean precipitation. A prevailing rise in droughts’ IDF features is linked to an anticipated decline in precipitation and an increase in temperature. From the derived drought IDF curves, projections for 2025–2049 and 2050–2074 indicate a significant rise in hydrological drought occurrences, while the historical and 2075–2099 periods demonstrate greater vulnerability in meteorological and agricultural systems. While the frequency of hydrological droughts is projected to decrease between 2075 and 2099 as their duration increases, the periods from 2025 to 2049 and from 2050 to 2074 are expected to experience more intense hydrological droughts. Generally, the findings underscore the critical need for timely interventions to mitigate the vulnerabilities associated with drought, particularly in areas like Borana that depend heavily on water resources availability. Full article

This study investigates the dynamic changes in wheat canopy spectral characteristics across seven critical growth stages (Tillering, Pre-Jointing, Jointing, Post-Jointing, Booting, Flowering, and Ripening) using UAV-based multispectral remote sensing. By analyzing four key spectral bands—green (G), red (R), red-edge (RE), and near-infrared (NIR)—and their combinations, we identify spectral features that reflect changes in canopy activity, health, and structure. Results show that the green band is highly sensitive to chlorophyll activity and low canopy coverage during the Tillering stage, while the NIR band captures structural complexity and canopy density during the Jointing and Booting stages. The combination of G and NIR bands reveals increased canopy density and spectral concentration during the Booting stage, while the RE band effectively detects plant senescence and reduced spectral uniformity during the ripening stage. Time-series analysis of spectral data across growth stages improves the accuracy of growth stage identification, with dynamic spectral changes offering insights into growth inflection points. Spatially, the study demonstrates the potential for identifying field-level anomalies, such as water stress or disease, providing actionable data for targeted interventions. This comprehensive spatio-temporal monitoring framework improves crop management and offers a cost-effective, precise solution for disease prediction, yield forecasting, and resource optimization. The study paves the way for integrating UAV remote sensing into precision agriculture practices, with future research focusing on hyperspectral data integration to enhance monitoring models. Full article

Biological invasions threaten biodiversity and agroecosystems, and early warning systems can minimise the spread of invasive alien species with limited resources. This study documents the presence of the alien plant Asclepias speciosa Torr., native to North America, that was first discovered in 2022 on Mount Vrontou, Central Macedonia, Northern Greece. This is the second European record of this alien species, after Lithuania, confirming its adaptability to contrasting European biogeographical regions. To enable future monitoring, this study provided new data on morphological traits of the species (above-ground parts), climatic tolerance (precipitation and temperature regimes), habitats with co-occurring species, pollinators, current reproductive potential, and seed germination at controlled temperatures (10 °C, 15 °C, and 20 °C). The high probability of misidentification with the highly invasive A. syriaca in European inventories supports the theory that A. speciosa may have been present in Europe long before it was officially reported. The lack of an EU-mandated reassessment of A. syriaca monitoring raises concerns regarding the potential invasion risk of A. speciosa in European natural and semi-natural areas or agricultural lands. Inspection mechanisms, early warning systems, and preventive measures are therefore essential to protect local biodiversity and agriculture from potential A. speciosa invasion, a risk that may be exacerbated by climate change. Full article

This study on soil salinity inversion in coastal tidal flats based on Sentinel-2 remote sensing imagery is significant for improving saline–alkali soils and advancing tidal flat agriculture. This study proposes an improved approach for soil salinity inversion in coastal tidal flats using Sentinel-2 imagery and a new enhanced chaotic mapping adaptive whale optimization neural network (CIWOABP) algorithm. Novel spectral indices were developed to enhance correlations with salinity, significantly outperforming traditional indexes. The CIWOABP model achieved superior validation accuracy (R² = 0.815) and reduced root mean square error (RMSE) and mean absolute error (MAE) compared to other machine learning models. The results enable the precise mapping of salinity levels, aiding salt-tolerant crop cultivation and sustainable agricultural management. This method offers a reliable framework for rapid salinity monitoring and precision farming in coastal regions. Full article

Karst aquifers in the Mediterranean region are crucial for water supply and agriculture but are increasingly threatened by climate change and overexploitation. The Jebel Zaghouan aquifer, historically significant for supplying Carthage and Tunis, serves as the focus of this study, which aims to evaluate its availability, quality, and vulnerability to ensure its long-term sustainability. To achieve this, various methods were employed, including APLIS and COP for recharge assessment and vulnerability mapping, SPEI and SGI drought indices, and stable and radioactive isotope analysis. The findings revealed severe groundwater depletion, primarily caused by overexploitation linked to urban expansion. Minimal recharge was observed, even during wet periods. APLIS analysis indicated moderate infiltration rates, consistent with prior reservoir models and the MEDKAM map. Isotopic analysis highlighted recharge from the Atlantic and mixed rainfall, while Tritium and Carbon-14 dating showed a mix of ancient and recent water, emphasizing the aquifer’s complex hydrodynamics. COP mapping classified 80% of the area as moderately vulnerable. Monitoring of nitrate levels indicated fluctuations, with peaks during wet years at Sidi Medien Spring, necessitating control measures to safeguard water quality amid agricultural activities. This study provides valuable insights into the aquifer’s dynamics, guiding sustainable management and preservation efforts. Full article

With the continuous advancement of 6G technology, SAGINs provide seamless coverage and efficient connectivity for future communications by integrating terrestrial, aerial, and satellite networks. Unmanned aerial vehicles (UAVs), owing to their high maneuverability and flexibility, have emerged as a critical component of the aerial layer in SAGINs. In this paper, we systematically review the key technologies, applications, and challenges of UAV-assisted SAGINs. First, the hierarchical architecture of SAGINs and their dynamic heterogeneous characteristics are elaborated on, and this is followed by an in-depth discussion of UAV communication. Subsequently, the core technologies of UAV-assisted SAGINs are comprehensively analyzed across five dimensions—routing protocols, security control, path planning, resource management, and UAV deployment—highlighting the progress and limitations of existing research. In terms of applications, UAV-assisted SAGINs demonstrate significant potential in disaster recovery, remote network coverage, smart cities, and agricultural monitoring. However, their practical deployment still faces challenges such as dynamic topology management, cross-layer protocol adaptation, energy-efficiency optimization, and security threats. Finally, we summarize the applications and challenges of UAV-assisted SAGINs and provide prospects for future research directions. Full article

Cropland is fundamental to food security, and monitoring cropland non-agriculturalization through satellite enforcement can effectively manage and protect cropland. However, existing research primarily focuses on optical imagery, and there are problems such as low data processing efficiency and long updating cycles, which make it difficult to meet the needs of large-scale rapid monitoring. To comprehensively and accurately obtain cropland change information, this paper proposes a method based on the Google Earth Engine (GEE) cloud platform, combining optical imagery and synthetic aperture radar (SAR) data for quick and accurate detection of cropland non-agriculturalization. The method uses existing land-use/land cover (LULC) products to quickly update cropland mapping, employs change vector analysis (CVA) for detecting non-agricultural changes in cropland, and introduces vegetation indices to remove pseudo-changes. Using Shanwei City, Guangdong Province, as a case study, the results show that (1) the cropland map generated in this study aligns well with the actual distribution of cropland, achieving an accuracy of 90.8%; (2) compared to using optical imagery alone, the combined optical and SAR data improves monitoring accuracy by 22.7%, with an overall accuracy of 73.65%; (3) in the past five years, cropland changes in Shanwei followed a pattern of an initial increase followed by a decrease. The research in this paper can provide technical reference for the rapid monitoring of cropland non-agriculturalization on a large scale, so as to promote cropland protection and rational utilization of cropland. Full article

In recent years, surface ozone (O₃) pollution has emerged as a significant barrier to the continued improvement of air quality in China, making O₃ risk assessment a critical research priority. Using nationwide O₃ monitoring data, this research investigated the spatial characteristics of O₃ pollution and assessed its potential impacts on human health and crop yields. The results showed that the maximum daily 8 h average O₃ (MDA8 O₃) exhibited higher concentrations in eastern and northern regions, and lower concentrations in the western and southern regions of China. Long-term O₃ exposure was associated with an estimated 175,154 all-cause deaths nationwide, with the highest health risks observed in Shandong, Henan, and Jiangsu provinces. The AOT40 values for the winter wheat and single-rice growing seasons in China were 9.30 × 10³ ppb·h and 1.29 × 10⁴ ppb·h, respectively. Moreover, O₃ exposure led to relative yield losses of 22.1% for winter wheat and 9.3% for single rice, corresponding to crop yield losses (CPLs) of 63 million metric tons and 14 million metric tons, respectively. Higher winter wheat CPL values were primarily concentrated in Henan, Shandong, and Hebei, while higher single rice CPL values were observed in Jiangsu, Hubei, and Anhui. This study presents a novel coupling of O₃ pollution exposure with human health and agricultural risk assessments across China, emphasizing the need for region-specific O₃ management strategies to protect public health and ensure agricultural sustainability. In conclusion, this study highlights the importance of targeted O₃ control in densely populated and major crop-producing areas to mitigate health risks and yield losses, thus safeguarding ecosystem health and food security. Full article

The United States Department of Agriculture—Invasive Plant Research Laboratory started limited production of a biological control mite, Floracarus perrepae, in 2008 for release against the invasive fern Lygodium microphyllum. Mass-rearing and release of the biological control agent was initiated in 2014 as part of the Comprehensive Everglades Restoration Plan to address the challenge of low establishment rates observed from 2008 to 2010. In late 2021, we critically analyzed our rearing protocols, focusing on aging galls and increasing plant vigor. These adjustments resulted in an exponential increase in colony productivity. We implemented bi-weekly monitoring of mite numbers within galls and identified the gall age class with the highest mite density. Based on this information, we developed a systematic method involving weekly plant readiness criteria and a predefined sequence of stages to select plants for release, ensuring that galls are correctly aged to maximize mite numbers. These changes have resulted in substantial improvements in gall abundance (165.3%), F. perrepae density per gall (86.0%), and estimated F. perrepae per plant (453.2%). The increase in F. perrepae released throughout the landscape improved the rates of establishment, abundance, and impact of the agent throughout the invaded range of L. microphyllum in Florida. Full article

The agricultural water–soil matching coefficient is a key factor for reflecting regional grain production status, which can be used to evaluate the reasonableness of water–soil allocation in certain areas. Taking the North China Plain (NCP) as the study area, in this study, we constructed a framework from a “physical water–water footprint” standpoint. The binary matching characteristics of “water–soil–grain” were then analyzed, and the water–soil matching coefficient method was employed to evaluate the pattern of water–soil matching for the years 1984, 1998, 2003, and 2022. Through the perspective of physical water–water footprint coupling, field trials of grain were utilized to calculate the range of water–soil matching coefficients under high yields. The results showed the following: ① From 1949 to 2022, the grain yield and planting areas increased. Wheat, the dominant crop, required substantial irrigation. Precipitation, cultivated land, and irrigation water exhibited spatial mismatches over the last ten years. ② The total water footprint showed an increasing trend, and the blue water footprint accounted for 19.47%. The spatial distribution of the water and land footprints of grain crops largely overlapped, and their values were higher in the central and southern regions, and lower in the north. ③ The current water–soil matching coefficient was in the range of [0.28, 1.75], which fell outside the optimal range of [0.534, 0.724]. The soil–water matching coefficients of wheat and rice were overall higher than those of other crops. We found higher values in the southwestern region and lower values in the northern areas, which aligns with the boundary of the groundwater funnel area. To address the identified challenges, we recommend implementing a tiered regulatory zone system based on the matching coefficient. The government should encourage a reduction in water-intensive crops like wheat and rice in high-value regions by providing subsidies. Additionally, a monitoring mechanism for water and soil compatibility should be established, considering the specific growth requirements of various crops. Full article

Nitrogen serves as a critical nutrient influencing the yield and quality of processed tomatoes; however, traditional methods for assessing its levels are both labor-intensive and costly. This study aimed to explore an efficient monitoring approach by analyzing the relationship between leaf nitrogen content (LNC) and canopy spectral reflectance characteristics throughout the growth stages of processed tomatoes at the Laolong River Tomato Base in Changji City, Xinjiang. The experimental design incorporated nine treatments, each with three replicates. LNC data were obtained using a dedicated leaf nitrogen content analyzer, while drones were utilized to capture multispectral images for the extraction of vegetation indices. Through Pearson correlation analysis, the optimal spectral variables were identified, and the relationships between LNC and spectral variables were established using models based on backpropagation (BP), multiple linear regression (MLR), and random forests (RFs). The findings revealed that the manually measured LNC data exhibited two peak values, which occurred during the onset of flowering and fruit setting stages, displaying a bimodal pattern. Among the twelve selected vegetation indices, ten demonstrated spectral sensitivity, passing the highly significant 0.01 threshold, with the Normalized Difference Chlorophyll Index (NDCI) showing the highest correlation during the full bloom stage. The combination of the NDCI and RF model achieved a prediction accuracy exceeding 0.8 during the full bloom stage; similarly, models incorporating multiple vegetation indices, such as RF, MLR, and BP, also reached prediction accuracies exceeding 0.8. Consequently, during the seedling establishment and initial flowering stages (vegetation coverage of <60%), the RF model with multiple vegetation indices was suitable for monitoring LNC; during the full bloom stage (vegetation coverage of 60–80%), both the RF model with the NDCI and the MLR model with multiple indices proved effective; and during the fruit setting and maturation stages (vegetation coverage of >80%), the BP model was more appropriate. This research provides a scientific basis for the cultivation management of processed tomatoes and the optimization of nitrogen fertilization within precision agriculture. It advances the application of precision agriculture technologies, contributing to improved agricultural efficiency and resource utilization. Full article

Climate change is profoundly impacting snow-dependent regions, altering hydrological cycles and threatening water security. This study examines the relationships between snow water equivalent (SWE), snow cover, temperature, and wind speed in Jammu and Kashmir, India, over five decades (1974–2024). Using ERA5 reanalysis and Indian Meteorological Department (IMD) datasets, we reveal significant declines in SWE and snow cover, particularly in high-altitude regions such as Kupwara and Bandipora. A Sen’s slope of 0.0016 °C per year for temperature highlights a steady warming trend that accelerates snowmelt, shortens snow cover duration, and reduces streamflow during critical agricultural periods. Strong negative correlations between SWE and temperature (r = −0.7 to −0.9) emphasize the dominant role of rising temperatures in SWE decline. Wind speed trends exhibit weaker correlations with SWE (r = −0.2 to −0.4), although localized effects on snow redistribution and evaporation are evident. Temporal snow cover analyses reveal declining winter peaks and diminished summer runoff contributions, exacerbating water scarcity. These findings highlight the cascading impacts of climate variability on snow hydrology, water availability, and regional ecosystems. Adaptive strategies, including real-time snow monitoring, sustainable water management, and climate-resilient agricultural practices, are imperative for mitigating these challenges in this sensitive Himalayan region. Full article

Due to the increasing demand for agricultural production and the depletion of natural resources, the rational and efficient use of resources in agriculture becomes essential. Thus, Agriculture 4.0 or precision agriculture (PA) was born, which leverages advanced technologies such as Geographic Information Systems (GIS), Artificial Intelligence (AI), sensors and remote sensing techniques to optimize agricultural practices. This study focuses on an innovative approach integrating data from different sources, within a GIS platform, including data from an experimental atmospheric simulator and from a wireless sensor network, to identify the most suitable areas for future crops. In addition, we also calculate the optimal path of a drone for crop monitoring and for a farm machine for agricultural operations, improving efficiency and sustainability in relation to agricultural practices and applications. Expected and obtained results of the conducted study in a specific area of Reggio Calabria (Italy) include increased accuracy in agricultural planning, reduced resource and pesticide use, as well as increased yields and more sustainable management of natural resources. Full article