Exposure to particulate matter (PM), specifically PM2.5 and PM10, poses significant health risks, particularly for workers in industries such as rice processing, where dust and particulate emissions are prevalent. The study aims to investigate the exposure of PM2.5 and PM10 among rice processing industry workers and associated health risks in the Kushtia District of Bangladesh. A total of 180 indoor and outdoor PM samples were purposively collected from three types of rice processing industries- automated, semi-automated, and non-automated, using a widely used Handheld Particle Counter device (Model: HT 9600). The study found the concentration of indoor PM2.5: 43–181µg/m³; indoor PM10: 57–207µg/m³; outdoor PM2.5: 47–160µg/m³; and outdoor PM10: 61–203µg/m³. All rice processing industries exhibited higher levels of PM2.5 and PM10, exceeding World Health Organization (WHO), the National Ambient Air Quality Standards (NAAQS), and Bangladesh Standards. Hazard quotient (HQ) for PM2.5 exceeded 1, signaling significant health risks across automated, non-automated, and some semi-automated industries, where automated and non-automated rice processing industries were the highest concern. The results highlighted that the PM levels of the indoor and outdoor air of the study area were significantly high. This study concluded that both PM2.5 and PM10 exposure increased health risks among the workers. Future research should identify the PM sources in the workplace and explore mitigation strategies among the workers.

Weed invasion poses a significant threat to global rice production, causing substantial yield losses and environmental degradation from excessive herbicide use. Unmanned Aerial Vehicles (UAVs), combined with advanced remote sensing and deep learning techniques, offer a transformative approach for precise weed and rice classification, supporting site-specific weed management. This review not only synthesizes recent advancements in deep learning methods using UAV-acquired data, diverse vegetation indices, and multiple sensor modalities (RGB, multispectral, hyperspectral, thermal, and LiDAR) but also provides a critical perspective on the evolution of model architectures, highlighting key trends and challenges in real-world agricultural applications. We discuss persistent issues, including data scarcity, limited model generalizability across varying environmental conditions, and the computational demands for real-time deployment. Furthermore, we propose future research directions informed by our perspective on the field’s development, emphasizing synthetic data generation via generative adversarial networks, advanced attention mechanisms, and the integration of UAVs with ground-based robotic platforms to enable more autonomous, efficient, and sustainable agricultural practices. This review thus offers both a comprehensive synthesis and a forward-looking viewpoint on advancing UAV-based precision weed management in rice cultivation. By integrating these insights, we provide a roadmap for translating UAV-based weed detection from experimental research to scalable, field-ready solutions.

Timely detection of crop stress is important for precision agriculture and agroecosystem resilience, particularly in environments where ground calibration data are limited or environmental conditions change rapidly. Traditional stress detection approaches often rely on single-date imagery or external calibration, which can limit temporal interpretation and transferability across heterogeneous fields. This study presents the Dynamic Temporal Stress Method (DTSM), a data-driven framework for monitoring and quantifying vegetation stress patterns in rice fields using multi-temporal UAV observations. DTSM integrates four vegetation indices, namely NDVI, NDRE, GNDVI, and MSAVI2, derived from UAV imagery acquired at three phenological stages (July 17, August 1, and August 12, 2024). The framework combines binary thresholding for initial stress mapping, temporal differencing to track stress change, and a Stress Persistence Index (SPX) to quantify repeated stress occurrence over time. Spatial patterns of stress were further evaluated using zonal statistics and Getis-Ord Gi* hotspot analysis to identify persistent stress clusters. Results showed clear temporal variation in crop stress, with stress intensity peaking on August 1, when NDRE-based mapping indicated 41.4% of the field under stress. Approximately 15.7% of the study area exhibited persistent stress across all observation dates. Spatial analysis identified Zones Z3, Z4, and Z8 as recurring stress hotspots. These findings indicate that integrating temporal change detection, multi-index analysis, and spatial persistence assessment can improve the interpretation of crop stress dynamics and support precision management in UAV-based agricultural monitoring.

[This corrects the article DOI: 10.1016/j.heliyon.2023.e14505.].

Groundwater is a vital resource that supports human health, ecosystems, and agriculture, and its quality varies across Bangladesh due to differing geology, land use, and environmental pressures. This study aims to conduct a comparative hydrogeochemical characterization and assess the environmental controls affecting groundwater quality, irrigation suitability, and human health risks across northern (Dinajpur) and coastal (Barisal) hydrological settings in Bangladesh. Groundwater data were obtained from the Bangladesh Water Development Board (BWDB), covering 27 monitoring wells in Dinajpur and 24 in Barisal. Multivariate statistical analyses revealed that groundwater quality in Barisal is primarily influenced by salinity-related factors (EC, TDS, and Major Ions like Na+, Cl-), reflecting coastal saline intrusion. In contrast, Dinajpur samples were associated with parameters like SAR, Si+, PO43-, ORP, I⁻, CaCO3, and B, indicating geogenic influences from silicate weathering, carbonate dissolution, and fertilizer inputs from agriculture. The Water Quality Index (WQI) results showed that in Barisal, 20.83 % of sites were excellent and 18 % unsuitable for drinking, whereas in Dinajpur, 64.29 % were excellent with no unfit sites. For irrigation suitability, Barisal had 75 % excellent and 4.17 % severely affected areas, while 44.44 % excellent and 14.81 % severely affected sites were observed in Dinajpur. Health risk assessment revealed significantly higher Hazard Index (HI) values in Barishal across all age groups compared to Dinajpur, indicating elevated potential health risks in the coastal region. Children were identified as the most vulnerable group, exhibiting higher HI values than males and females in both Barishal (HI = 0.38–57.29) and Dinajpur (HI = 0.34–4.41). The Pearson Correlation analysis indicated that in Dinajpur, environmental variables demonstrated negligible correlations with groundwater quality (WQI: r = –0.18–0.19; IWQI: NDVI r = 0.25, LST r = –0.20). Similarly, Barishal had negligible correlations, with the Water Quality Index (WQI) revealing a little link with NDVI and NDWI, respectively (r = 0.17–0.24), whilst the Integrated Water Quality Index (IWQI) indicated minimal impact across all indices (r = –0.03–0.19). Overall, the study highlights regional variations in groundwater quality and health risks, emphasizing the necessity for location-specific water resource management strategies.

The study put forward a data fusion approach for urban remote sensing that combines SAR (Synthetic Aperture Radar) and optical satellite data. By integrating datasets from different sensors and spatial–temporal scales, the technique aims to extract more accurate information. The fusion approach utilizes two methods: feature-based fusion, where relevant features are extracted and fused, and simple layer stacking (SLS), where the original datasets are directly stacked as multiple layers. This study extracted features using SAR textures (using Sentinel-1) and modified indices (using Landsat-8), and then classified these features using an XGBoost algorithm implemented in Python and Google Earth Engine. Researchers examined five cities, each representing a distinct climatic zone and urban dynamic: Cape Town, Guangzhou, Los Angeles, Mumbai, and Osaka. An accuracy assessment was conducted using random validation points, achieving an overall accuracy of 89.5 https://github.com/mnasarahmad/sls .

Effective land use practices and sustainable land management require a thorough assessment of the spatial variability of soil properties across distinct landuse zones. The research analyzed the spatial variability of key soil physicochemical parameters across agro-industrial (Diversified farming, specialized farming, and industrial area) zones using geostatistical methods and examined the relationship with environmental variables. A total of 123 soil samples were collected at 0–15 cm depth using systematic sampling techniques, and semivariogram modelling was used to identify soil property distribution patterns, with nugget-to-sill ratios calculated to assess spatial structure. The findings of the one-way ANOVA test revealed significant differences (p < 0.05) in soil parameters, except moisture, across the landuse zones. Pearson correlation analysis revealed strong positive correlations between Soil Organic Matter (SOM), Soil Organic Carbon (SOC), and Soil Total Nitrogen (STN), with Normalize Difference Built-up Index (NDBI) being the most associated environmental factor, while PCA analysis highlighted SOM, SOC, and STN as the most influential soil variables, while Land Surface Temperature (LST), Normalize Difference Built-up Index (NDBI), and Normalize Difference Vegetation Index (NDVI) as the most dominant environmental factors influencing the soil properties. This study revealed varying distribution patterns of environmental-anthropogenic and soil parameters across landuse zones and their impact on this variation. Nugget-to-sill ratios indicated weak to moderate spatial structure for most soil properties, except for STN (12.28 %) in industrial area, moisture (3.36 %) in the diversified farming land, pH in specialized (0 %) and industrial area (0 %), and C:N ratio in the industrial area (0 %), which showed strong spatial dependence. In industrial areas, soil properties exhibited moderate to strong spatial dependence, except for SOM and SOC. This study highlights the role of land use, environmental, and anthropogenic factors in soil property distribution, supporting precision agriculture and conservation.

Mapping urban pluvial flooding (UPF) in data-scarce regions poses significant challenges, particularly when drainage systems are inadequate or outdated. These limitations hinder effective flood mitigation and risk assessment. This study proposes an innovative approach to address these challenges by integrating deep learning (DL) models with traditional methods. First, deep convolutional generative adversarial networks (DCGANs) were employed to enhance drainage network data generation. Second, deep recurrent neural networks (DRNNs) and multi-criteria decision analysis (MCDA) methods were implemented to assess UPF. The study compared the performance of these approaches, highlighting the potential of DL models in providing more accurate and robust flood mapping outcomes. The methodology was applied to Lahore, Pakistan—a rapidly urbanizing and data-scarce region frequently impacted by UPF during monsoons. High-resolution ALOS PALSAR DEM data were utilized to extract natural drainage networks, while synthetic datasets generated by GANs addressed the lack of historical flood data. Results demonstrated the superiority of DL-based approaches over traditional MCDA methods, showcasing their potential for broader applicability in similar regions worldwide. This research emphasizes the role of DL models in advancing urban flood mapping, providing valuable insights for urban planners and policymakers to mitigate flooding risks and improve resilience in vulnerable regions.

This study proposes a fusion approach to enhancing urban remote sensing applications by integrating SAR (Sentinel-1) and optical (Landsat-8) satellite datasets. The fusion technique combines feature-based fusion and simple layer stacking (SLS) to improve the accuracy of urban impervious surface (UIS) extraction. SAR textures and modified indices are used for feature extraction, and classification is performed using the XGBoost machine learning algorithm in Python and Google Earth Engine. The study focuses on four global cities (New York, Paris, Tokyo, and London) with heterogeneous climatic zones and urban dynamics. The proposed method showed significant results. The accuracy assessment using random validation points shows an overall accuracy of 86% for UIS classification with the SLS method, outperforming single-data classification. The proposed approach achieves higher accuracy (86%) compared to three global products (ESA, ESRI, and Dynamic World). New York exhibits the highest overall accuracy at 88%. This fusion approach with the XGBoost classifier holds potential for new applications and insights into UIS mapping, with implications for environmental factors such as land surface temperature, the urban heat island effect, and urban pluvial flooding.

The integration of optical and SAR datasets through ensemble machine learning models shows promising results in urban remote sensing applications. The integration of multi-sensor datasets enhances the accuracy of information extraction. This research presents a comparison of two ensemble machine learning classifiers (random forest and extreme gradient boost (XGBoost)) classifiers using an integration of optical and SAR features and simple layer stacking (SLS) techniques. Therefore, Sentinel-1 (SAR) and Landsat 8 (optical) datasets were used with SAR textures and enhanced modified indices to extract features for the year 2023. The classification process utilized two machine learning algorithms, random forest and XGBoost, for urban impervious surface extraction. The study focused on three significant East Asian cities with diverse urban dynamics: Jakarta, Manila, and Seoul. This research proposed a novel index called the Normalized Blue Water Index (NBWI), which distinguishes water from other features and was utilized as an optical feature. Results showed an overall accuracy of 81% for UIS classification using XGBoost and 77% with RF while classifying land use land cover into four major classes (water, vegetation, bare soil, and urban impervious). However, the proposed framework with the XGBoost classifier outperformed the RF algorithm and Dynamic World (DW) data product and comparatively showed higher classification accuracy. Still, all three results show poor separability with bare soil class compared to ground truth data. XGBoost outperformed random forest and Dynamic World in classification accuracy, highlighting its potential use in urban remote sensing applications.

Expanding urban impervious surface area (ISA) mapping is crucial to sustainable development, urban planning, and environmental studies. Multispectral ISA mapping is challenging because of the mixed-pixel problems with bare soil. This study presents a novel approach using spectral and temporal information to develop a Soil-Suppressed Impervious Surface Area Index (SISAI) using the Landsat Operational Land Imager (OLI) data set, which reduces the soil but enhances the ISA signature. This study mapped the top 12 populated megacities using SISAI and achieved an over-all accuracy of 0.87 with an F1-score of 0.85. It also achieved a higher Spatial Dissimilarity Index between the ISA and bare soil. However, it is limited by bare gray soil and shadows of clouds and hills. SISAI encourages urban dynamics and inter-urban compari- son studies owing to its automatic and unsupervised methodology.

The study put forward a data fusion approach for urban remote sensing that combines SAR (Synthetic Aperture Radar) and optical satellite data. By integrating datasets from different sensors and spatial-temporal scales, the technique aims to extract more accurate information. The fusion approach utilizes two methods: feature-based fusion, where relevant features are extracted and fused, and simple layer stacking (SLS), where the original datasets are directly stacked as multiple layers. This study extracted features using SAR textures (using Sentinel-1) and modified indices (using Landsat-8), and then classified these features using an XGBoost algorithm implemented in Python and Google Earth Engine. Researchers examined five cities, each representing a distinct climatic zone and urban dynamic: Cape Town, Guangzhou, Los Angeles, Mumbai, and Osaka. An accuracy assessment was conducted using random validation points, achieving an overall accuracy of 89.5% using the proposed MSFI method. A comparison was also performed with three well-known global products. The proposed approach, outperformed all three global products achived 89% accuracy while ESA (84%), ESRI (81%) and Dynamic World (82%). Additionally, Land surface temperature analysis was accomplished to investigate the relationship between extracted UIS and Land Surface Temperature (LST) across selected cities to show the practical use of proposed MSFI method. Los Angeles, a warm temperate city, showed the highest LST among all five cities. The datasets, along with the GEE and Python codes, are available at https://github.com/mnasarahmad/sls.

Accurate urban impervious surface (UIS) extraction from open-source remote sensing data remains challenging, especially for cities with heterogeneous climatic backgrounds. Contemporary, state-of-the-art techniques achieve promising results at a global scale, but accuracy is compromised at the city level. Therefore, a ensemble machine learning approach using open-source Optical-SAR remote sensing datasets was implemented to enhance the accuracy of UIS mapping. Initially, we integrated optical and radar datasets with modified urban indices to generate input features. Then, we applied four ensemble machine learning algorithms, including AdaBoost, Gradient Boost (GB), Extreme Gradient Boosting (XGBoost), and Random Forest (RF), and fine-tuned them via a soft voting ensemble approach. The optimized UISEM approach showed a model accuracy of 98%. The UISEM method achieved a classification accuracy of 92% and consistently performed across 32 cities globally with heterogeneous climatic zones. Regarding accuracy and predictive power, the XGB ensemble classifier outperformed other ML classifiers in mapping UIS. Furthermore, a comparative analysis against three well-known datasets (ESA World Cover, ESRI Land Cover, and Dynamic World) was also performed. The proposed UISEM model outperformed renowned global datasets with a 92% classification accuracy, followed by DW with 83%, ESA with 86%, and ESRI with 82%. In the future, developing a spatial–temporal version of UISEM can support diverse urban applications globally. The datasets and (GEE and Python) codes are available at https://github.com/mnasarahmad/UISEM.

The research aimed to quantify the lake area dynamics, evaluate the changes in distance and rate of lake shorelines quantitatively and spatially and investigate the key factors influencing the Hongjiannao Lake (HL) area shrinkage. The study used remote sensing (RS) data from Landsat TM/ETM+ and OLI images and Google Earth Engine (GEE), a cloud platform for obtaining the lake surface area and island information from 1987 to 2023. A modified normalized difference water index (MNDWI) was applied to water area extraction. Digital Shoreline Analysis System (DSAS) was employed to assess net shoreline movement (NSM) and depict the lake shoreline length and rate changes. Furthermore, the water level was derived by ASTER GDEM V2 using the waterline method and lake boundaries. Six climatic features (temperature, precipitation, potential and actual evaporation, aridity index, and actual water difference) were investigated to find the driving factors of lake area shrinkage by correlation and factor analysis. The results reveal that during 1987-2023, the HL area has undergone four separate phases: stable (1987-1997), shrinkage (1998-2015), growth (2016-2019), and reduction (2020-2023). The most substantial negative change (-7.45%) in the HL area was observed in 1998. NSM analysis demonstrates that the lake has experienced both expansion and shrinkage at various times and locations. According to Water Balance Method, the water volume of HL exhibited variations, ranging from-0.1895 to-0.009 km(3). The average yearly change in lake volume, water level, and area displayed similar characteristics with high inconstancy. Correlation and factor analysis of lake area and climatic factors demonstrate that higher precipitation, low temperatures, less potential evaporation level, lower actual evaporation rates, and more minor differences in water levels are associated with an increase in lake area. In contrast, the opposite conditions lead to a reduction in lake size.

Impervious surfaces are an essential component of our environment and are mainly triggered by human developments. Rapid urbanization and population expansion have increased Lahore's urban impervious surface area. This research is based on estimating the urban imper-vious surface area (UISA) growth from 1993 to 2022. Therefore, we aimed to generate an accurate urban impervious surfaces area map based on Landsat time series data on Google Earth Engine (GEE). We have used a novel global impervious surface area index (GISAI) for impervious surface area (UISA) extraction. The GISAI accomplished significant results, with an average overall accuracy of 90.93% and an average kappa coefficient of 0.78. We also compared the results of GISAI with Global Human Settlement Layer-Built and harmonized nighttime light (NTL) ISA data products. The accuracy assessment and cross-validation of UISA results were performed using ground truth data on ArcGIS and GEE. Our research findings revealed that the spatial extent of UISA increased by 198.69 km2 from 1993 to 2022 in Lahore. Additionally, the UISA has increased at an average growth rate of 39.74 km2. The GISAI index was highly accurate at extract-ing UISA and can be used for other cities to map impervious surface area growth. This research can help urban planners and policymak-ers to delineate urban development boundaries. Also, there should be controlled urban expansion policies for sustainable metropolis and should use less impermeable materials for future city developments.

Rapid urbanization has become an immense problem in Lahore city, causing various socio-economic and environmental problems. Therefore, it is noteworthy to monitor land use/land cover (LULC) change detection and future LULC patterns in Lahore. The present study focuses on evaluating the current extent and modeling the future LULC developments in Lahore, Pakistan. Therefore, the semi-automatic classification model has been applied for the classification of Landsat satellite imagery from 2000 to 2020. And the Modules of Land Use Change Evaluation (MOLUSCE) cellular automata (CA-ANN) model was implemented to simulate future land use trends for the years 2030 and 2040. This study project made use of Landsat, Shuttle Radar Topography Mission Digital Elevation Model, and vector data. The research methodology includes three main steps: (i) semi-automatic land use classification using Landsat data from 2000 to 2020; (ii) future land use prediction using the CA-ANN (MOLUSCE) model; and (iii) monitoring change detection and interpretation of results. The research findings indicated that there was a rise in urban areas and a decline in vegetation, barren land, and water bodies for both the past and future projections. The results also revealed that about 27.41% of the urban area has been increased from 2000 to 2020 with a decrease of 42.13% in vegetation, 2.3% in barren land, and 6.51% in water bodies, respectively. The urban area is also expected to grow by 23.15% between 2020 and 2040, whereas vegetation, barren land, and water bodies will all decline by 28.05%, 1.8%, and 12.31%, respectively. Results can also aid in the long-term, sustainable planning of the city. It was also observed that the majority of the city's urban area expansion was found to have occurred in the city's eastern and southern regions. This research also suggests that decision-makers and municipal Government should reconsider city expansion strategies. Moreover, the future city master plans of 2050 must emphasize the relevance of rooftop urban planting and natural resource conservation.

Domestic violence against women is a global issue that encompasses physical, sexual, and psychological abuse within intimate relationships. It is a significant public justice concern in Pakistan, yet formal reporting channels and statistics databases are lacking. This study aimed to explore the socioeconomic causes and prevalence of domestic violence against women in a rural community of Lahore. A quantitative descriptive cross-sectional study was conducted among 150 married women residing in the Lakhodair rural community of Lahore. A close-ended questionnaire, including demographic information and a domestic violence questionnaire, was used for data collection. Data was analyzed using SPSS version 23, and prevalence rates of domestic violence were determined. Most participants were uneducated (93.3%), and prevalence rates of domestic violence were assessed across ten questions related to various forms of abuse. The prevalence of domestic violence is high in our study. Notably, 66% of respondents reported experiencing physical injury from their partners, and 65% had encountered physical violence, such as being struck, pushed, grabbed, thrown, or choked. Additionally, 55% of women reported experiencing sexual violence, while 51% faced forced sexual activity. Emotional abuse was also prevalent, with 52% feeling regularly belittled by their partners. This study revealed that more than 50% of women experience domestic violence after marriage in rural areas. It underscores the urgent need for awareness programs, women's education initiatives, and empowerment programs to address this grave concern. Future research should consider qualitative approaches to gain deeper insight into participants' experiences and feelings. Furthermore, focusing on domestic violence against males is essential to comprehensively address this complex issue.

Tobacco farming in Bangladesh has significant and far-reaching environmental impacts, affecting the land, water, and air. While the country has implemented tobacco control measures, the lack of monitoring and enforcement has resulted in environmental degradation and public health concerns. This study aims to document the environmental impact of tobacco farming in Bangladesh, adopting a qualitative approach to collect and analyze data. The study used focus group discussions, key informant interviews, and a structured questionnaire survey to gather data, assessing the impact of tobacco farming on the environment, socioeconomic conditions, and human health using a five-point impact assessment scale. Results illustrated that tobacco cultivation contributes to the ecosystem and natural resource degradation, leading to a loss of habitat diversity and domestic animal death. Soil erosion, water pollution, and air pollution from excessive plowing and pesticide usage have also been observed, causing skin diseases and other health issues. Despite some economic benefits, social conditions have worsened due to drug addiction and conflicts among tobacco workers. The study will help policymakers and environmentalists by highlighting the need to take action in reducing the environmental and social impacts of tobacco farming in Bangladesh. It also informs the public about the potential tobacco production and consumption risks. This study provides important insights into the adverse effects of tobacco farming in Bangladesh and emphasizes the importance of implementing appropriate measures to reduce environmental and public health impacts.

Expansion of urban impervious surface (UIA) and increased urban pluvial flooding (UPF) have an impact on urban dynamics, socioeconomic activities, and our environment. Therefore, monitoring the increase in UIS and its effect on UPF is essential. The notion of this research is based on the mapping of impervious surface area increase in three major cities of Pakistan. There were two key objectives: (i) Mapping impervious surface area growth using the global impervious surface area index (GISAI) on Google Earth Engine from 1992 to 2022 and (ii) mapping the pluvial flood extent in selected urban areas using Sentinel-1 Ground Range Detected (GRD) data. Thus, we have utilized the GISAI for mapping urban impervious surface area (UISA) using Landsat time-series data on GEE. Our research findings revealed that about 16.8%, 23.5%, and 16.4% of the impervious surface have been increased in Islamabad, Lahore, and Karachi, respectively. Also, Lahore city has the highest overall accuracy, aiming at the GISAI of 93%, followed by Karachi and Islamabad with an overall accuracy of 86% and 85%, respectively. The results indicated that urban flooding has occurred in those areas where the ISA has grown during the last three decades. It shows significant changes in the impervious surface area that cause enhanced urban pluvial flooding in major cities of Pakistan. Also, Sentinel-1 data and the SNAP tool significantly mapped flooded areas in the selected zones. So, providing cities and local governments with increased quick flood detection capabilities is essential. It can also provide feasible policy recommendations for Pakistan decision-makers in city management. Therefore, we suggest a modeling-based solution to identify high-risk locations in major cities for upcoming UPF events.