Refugee settlements are highly exposed to extreme weather conditions.

Involuntary displacement from conflict and other causes leads to clustering of refugees and internally displaced people, often in long-term settlements. Within refugee-hosting countries, refugee settlements are frequently located in isolated and remote areas, characterized by poor-quality land and harsh climatic conditions. Yet, the exposure of refugee settlements to climatic events is underresearched. In this article, we study the exposure of the 20 largest refugee settlements worldwide to extreme variations in climatic conditions. The analysis integrates exposure of camp locations compared to the national trends for both slow- and rapid-onset events and includes descriptive statistics, signal-to-noise analyses, and trend analyses. Our findings show that most refugee settlements included face relatively high exposure to slow-onset events, including high temperatures (for settlements in Kenya, Ethiopia, Rwanda, Sudan, and Uganda), low temperatures (in the case of Jordan and Pakistan), and low levels of rainfall (in Ethiopia, Rwanda, Kenya, and Uganda) compared to national averages. Our findings for rapid-onset events-heatwaves, coldwaves, and extreme rainfall-are less conclusive compared to country trends, although we find relatively high exposure to extreme rainfall in Cox's Bazar, Bangladesh. Our analyses confirm that refugee populations are exposed to extreme weather conditions postdisplacement, which, in combination with their sociopolitical exclusion, poses a threat to well-being and increased marginalization. Our findings call for an inclusive and integrated approach, including refugees and their host communities, in designing climate adaptation and sustainable development policies, in order to promote equitable sustainable development pathways in refugee-hosting countries.

Clouded judgments? The role of virtual weather in word valence evaluations.

Exploring the dynamic interface of environmental psychology and psycholinguistics, this investigation delves into how simulated weather conditions - sunny versus rainy - affect emotional perceptions of linguistic stimuli within a Virtual Reality (VR) framework. Participants assessed words' emotional valence being exposed to these distinct environmental simulations. Contrary to expectations, we found that while rainy conditions modestly prolonged response times, they did not significantly alter the emotional valence attributed to words. Our study shows that weather can affect emotional cognition, but intrinsic emotional word properties are resilient to environmental influences. This highlights the complex interplay between environmental factors and linguistic processing and reaffirms the importance of environmental contexts in cognitive and emotional evaluations, especially in the face of climate change. By integrating VR technology with environmental psychology and linguistics, our research offers novel insights into the subtle yet significant ways in which virtual and real-world environments converge to shape human emotional and cognitive responses.

Perception Methods for Adverse Weather Based on Vehicle Infrastructure Cooperation System: A Review.

Environment perception plays a crucial role in autonomous driving technology. However, various factors such as adverse weather conditions and limitations in sensing equipment contribute to low perception accuracy and a restricted field of view. As a result, intelligent connected vehicles (ICVs) are currently only capable of achieving autonomous driving in specific scenarios. This paper conducts an analysis of the current studies on image or point cloud processing and cooperative perception, and summarizes three key aspects: data pre-processing methods, multi-sensor data fusion methods, and vehicle-infrastructure cooperative perception methods. Data pre-processing methods summarize the processing of point cloud data and image data in snow, rain and fog. Multi-sensor data fusion methods analyze the studies on image fusion, point cloud fusion and image-point cloud fusion. Because communication channel resources are limited, the vehicle-infrastructure cooperative perception methods discuss the fusion and sharing strategies for cooperative perception information to expand the range of perception for ICVs and achieve an optimal distribution of perception information. Finally, according to the analysis of the existing studies, the paper proposes future research directions for cooperative perception in adverse weather conditions.

Object Detection and Classification Framework for Analysis of Video Data Acquired from Indian Roads.

Object detection and classification in autonomous vehicles are crucial for ensuring safe and efficient navigation through complex environments. This paper addresses the need for robust detection and classification algorithms tailored specifically for Indian roads, which present unique challenges such as diverse traffic patterns, erratic driving behaviors, and varied weather conditions. Despite significant progress in object detection and classification for autonomous vehicles, existing methods often struggle to generalize effectively to the conditions encountered on Indian roads. This paper proposes a novel approach utilizing the YOLOv8 deep learning model, designed to be lightweight, scalable, and efficient for real-time implementation using onboard cameras. Experimental evaluations were conducted using real-life scenarios encompassing diverse weather and traffic conditions. Videos captured in various environments were utilized to assess the model's performance, with particular emphasis on its accuracy and precision across 35 distinct object classes. The experiments demonstrate a precision of 0.65 for the detection of multiple classes, indicating the model's efficacy in handling a wide range of objects. Moreover, real-time testing revealed an average accuracy exceeding 70% across all scenarios, with a peak accuracy of 95% achieved in optimal conditions. The parameters considered in the evaluation process encompassed not only traditional metrics but also factors pertinent to Indian road conditions, such as low lighting, occlusions, and unpredictable traffic patterns. The proposed method exhibits superiority over existing approaches by offering a balanced trade-off between model complexity and performance. By leveraging the YOLOv8 architecture, this solution achieved high accuracy while minimizing computational resources, making it well suited for deployment in autonomous vehicles operating on Indian roads.

Climate Change and Diabetes Mellitus - Emerging Global Public Health Crisis: Observational Analysis.

Climate change is the most pressing challenge of the 21st century. It's immediate impacts on the environment are extreme weather conditions such as heatwaves, storms, rains, floods, sealevel rise, the disruption of crops, agricultural systems, water, vector-borne diseases, and ecosystems. The weather-related disasters disturbed the natural biological environment and dislocated millions of people from their homes. The extreme weather conditions caused the deaths of about two million people and $4.3 trillion in economic loss over the past half a century, and 90% of deaths were reported from developing countries. It has also been predicted that between 2030 and 2050, climate change is presumed to cause about 250,000 additional deaths per annum. The rapid rise in temperatures, frequencies of heat waves, wildfires, storms, and other weather extremes conditions could affect human health in many ways. The one-degree Celsius rise in outdoor temperature causes over 100,000 new cases of diabetes mellitus per annum. Climate change compromised body metabolism, vasodilation, sweating, insulin resistance and cause Type-2 diabetes mellitus and gestational diabetes Mellitus.

[Forensic assessment of meteorological conditions as risk factors of sudden death from arterial hypertension]. / Sudebno-meditsinskaya otsenka meteouslovii v kachestve faktorov riska vnezapnoi smerti pri gipertonicheskoi bolezni.

Arterial hypertension is a disease that significantly increases the risk of sudden death in different age groups. It is of high scientific interest to study the relationship of arterial hypertension manifestations with different weather conditions. The article provides a review of literature data on the variability of arterial hypertension course depending on meteorological conditions as a risk factor for sudden death.

Multiparametric Monitoring System of Mt. Melbourne Volcano (Victoria Land, Antarctica).

Volcano monitoring is the key approach in mitigating the risks associated with volcanic phenomena. Although Antarctic volcanoes are characterized by remoteness, the 2010 Eyjafjallajökull eruption and the 2022 Hunga eruption have reminded us that even the farthest and/or least-known volcanoes can pose significant hazards to large and distant communities. Hence, it is important to also develop monitoring systems in the Antarctic volcanoes, which involves installing and maintaining multiparametric instrument networks. These tasks are particularly challenging in polar regions as the instruments have to face the most extreme climate on the Earth, characterized by very low temperatures and strong winds. In this work, we describe the multiparametric monitoring system recently deployed on the Melbourne volcano (Victoria Land, Antarctica), consisting of seismic, geochemical and thermal sensors together with powering, transmission and acquisition systems. Particular strategies have been applied to make the monitoring stations efficient despite the extreme weather conditions. Fumarolic ice caves, located on the summit area of the Melbourne volcano, were chosen as installation sites as they are protected places where no storm can damage the instruments and temperatures are close to 0 °C all year round. In addition, the choice of instruments and their operating mode has also been driven by the necessity to reduce energy consumption. Indeed, one of the most complicated tasks in Antarctica is powering a remote instrument year-round. The technological solutions found to implement the monitoring system of the Melbourne volcano and described in this work can help create volcano monitoring infrastructures in other polar environments.

Vision Transformers in Image Restoration: A Survey.

The Vision Transformer (ViT) architecture has been remarkably successful in image restoration. For a while, Convolutional Neural Networks (CNN) predominated in most computer vision tasks. Now, both CNN and ViT are efficient approaches that demonstrate powerful capabilities to restore a better version of an image given in a low-quality format. In this study, the efficiency of ViT in image restoration is studied extensively. The ViT architectures are classified for every task of image restoration. Seven image restoration tasks are considered: Image Super-Resolution, Image Denoising, General Image Enhancement, JPEG Compression Artifact Reduction, Image Deblurring, Removing Adverse Weather Conditions, and Image Dehazing. The outcomes, the advantages, the limitations, and the possible areas for future research are detailed. Overall, it is noted that incorporating ViT in the new architectures for image restoration is becoming a rule. This is due to some advantages compared to CNN, such as better efficiency, especially when more data are fed to the network, robustness in feature extraction, and a better feature learning approach that sees better the variances and characteristics of the input. Nevertheless, some drawbacks exist, such as the need for more data to show the benefits of ViT over CNN, the increased computational cost due to the complexity of the self-attention block, a more challenging training process, and the lack of interpretability. These drawbacks represent the future research direction that should be targeted to increase the efficiency of ViT in the image restoration domain.

Electrocoagulation flotation as a municipal wastewater (pre-)treatment technology: Effect of weather conditions and current density.

Electrocoagulation (EC) is a promising compact alternative technology, despite its viability in municipal wastewater treatment (MWWT) is currently challenged by its energy-intensive and batch-mode operation. This study introduces an innovative continuous electrocoagulation flotation (ECF) design for MWWT. ECF shows promising pollutant removal efficiencies, with identical results using both iron (Fe) and aluminum (Al) anodes. At a current density (CD) of 120 A/m2, it achieved significant removals: 90% tCOD, 98% TP, 94% TSS, 60% BOD5, and 40% TN. Designed ECF is proposed as a pre-treatment step due to limited TN removal. The study investigated optimal ECF performance under varying weather conditions using CD ranges of 40, 80, and 120 A/m2. Both Fe and Al ECF outperformed in treating rainy weather (RW) and dry weather (DW) municipal wastewater (MWW). However, Al anode's super-faradaic behavior resulted in higher residual concentrations in effluent, (i.e., an average of 6.53-33.7 mg/L), and operational costs compared to Fe ECF. Optimized Fe ECF setting needs to be changed depending in the weather variation. Fe ECF achieved high removal rates for tCOD (94%) and TP (95%) in RW MWW at a low CD of 40 A/m2. Comparative to this, the optimum CD for treated DW MWW was between 40 and 80 A/m2, removing tCOD (71-73%) and TP (85-95%). Specifically, at these conditions, the operational expenses were respectively 0.47 ± 0.03 €/m3 (RW MWW), and 0.37 ± 0.02 €/m3 to 0.81 ± 0.04 €/m3 (DW MWW). Moreover, ECF enables resource recovery and a circular economy through anaerobic sludge digestion, with Fe ECF generating more biogas than Al.

What determines how green crop farming can get? Spatial factors or green awareness spillovers.

This study intends to examine if traditional local factors (seasonal weather conditions) and/or green awareness spillovers contribute to the spatial dependency of farmland allocated to organic farming after its uptake in Taiwan. To investigate the push and pull factors to improve the policy targeting on environmentally-friendly farming practices, we assess spatial autocorrelation of the adoption intensity of organic farming with exploratory analysis, and expand that by exploring how explanatory factors affect the adoption intensity using a spatial Tobit regression analysis, taking into consideration that the adoption intensity is a typical example of censored data. Based on township-level data of 323 townships constructed from 213,534 rice farm households drawn from the 2015 Agriculture Census, we find high-high clusters (hot spots) are mostly in the northern and the eastern parts of Taiwan, whereas the majority of low-low clusters (cold spots) locate in central and southern Taiwan. Such spatial aspects of organic adoption intensity suggest that a spatially targeted program in promoting environmental awareness is pertinent to fostering the development of organic agriculture. The results from the spatial lag Tobit regression estimation provide empirical evidence supporting the role of local weather conditions and green awareness spillovers in explaining the spatial patterns of organic agriculture in Taiwan. In light of the stylized fact that the majority of the rice farm households in Taiwan are small with 84% having farmland areas less than 1 ha, the findings provide practical references to policy practitioners in tailoring farm programs or policies in line with the notion of inclusive and sustainable development.

Variation of Chemical Composition and Antioxidant Properties of Kiwiberry (Actinidia arguta) in a Three-Year Study.

The quality of fruit as a source of bioactive ingredients is related to the genetic characteristics of plants, but it can also be modified by growing conditions. Therefore, long-term research can be extremely valuable in evaluating various crop plants, especially novel ones. The aim of the research was to test four popular European kiwiberry (Actinidia arguta) cultivars ('Geneva', 'Bingo', 'Weiki', 'Anna') in terms of selected morphological features, yield, and chemical composition as well as their variability over 3 years. It can be concluded that the studied genotypes were very diverse in terms of the biochemical compounds' concentration in individual seasons. The cultivars 'Anna' and 'Weiki' were the most similar ones with respect to each other in terms of morphology and chemical composition. The cultivars 'Bingo' and 'Geneva' were definitely different. 'Bingo' was characterized by the largest and most uniform fruits in each season and had the highest concentration of vitamin C but the lowest carotenoid concentration. In turn, 'Geneva' produced the smallest fruit in each season with the highest concentration of polyphenols and a high concentration of carotenoids and displayed the highest antioxidant capacity regardless of the determination method. The research was performed with the application of computer-supported statistical analysis.

Vertical distribution of microplastics in a river water column using an innovative sampling method.

Due to limitations of sampling methods, subsurface water is usually a less well-investigated compartment of the water column when scientists assess microplastic contamination. In this study, microplastic (MP) contamination was assessed in a freshwater river both in surface and subsurface using an innovative sampling method. Microplastic contamination in the lower part of the water column, i.e., near-bottom water and in sediments, was also studied. Three sampling campaigns were carried out during different weather conditions: stormy, rainy, and dry in order to observe their influence on the microplastics vertical distribution. No significant difference was observed between the abundance and types of MPs in surface and subsurface water. The proportion of polymer with theoretical density < 1 (polypropylene d = 0.9, polyethylene d = 0.91-0.95) and polystyrene (d = 0.1-1.06) in the surface and subsurface samples was 73.5%, and this proportion drops to 40.8% for the samples located in the near-bottom water and the sediments. Our results indicate that the MP concentration of the different compartments analyzed can be significantly influenced by rainfall during and prior to the sampling day. This study highlights that in shallow rivers, surface water sampling is representative of the water column MP contamination, but that sampling without taking environmental conditions into account may lead to erroneous estimation of MPs concentration and flux entering the marine environment.

Early-life seasonal, weather and social effects on telomere length in a wild mammal.

Early-life environmental conditions can provide a source of individual variation in life-history strategies and senescence patterns. Conditions experienced in early life can be quantified by measuring telomere length, which can act as a biomarker of survival probability in some species. Here, we investigate whether seasonal changes, weather conditions and group size are associated with early-life and/or early-adulthood telomere length in a wild population of European badgers (Meles meles). We found substantial intra-annual changes in telomere length during the first 3 years of life, where within-individual effects showed shorter telomere lengths in the winter following the first spring and a trend for longer telomere lengths in the second spring compared to the first winter. In terms of weather conditions, cubs born in warmer, wetter springs with low rainfall variability had longer early-life (3-12 months old) telomeres. Additionally, cubs born in groups with more cubs had marginally longer early-life telomeres, providing no evidence of resource constraint from cub competition. We also found that the positive association between early-life telomere length and cub survival probability remained when social and weather variables were included. Finally, after sexual maturity, in early adulthood (i.e., 12-36 months) we found no significant association between same-sex adult group size and telomere length (i.e., no effect of intrasexual competition). Overall, we show that controlling for seasonal effects, which are linked to food availability, is important in telomere length analyses, and that variation in telomere length in badgers reflects early-life conditions and also predicts first year cub survival.

The interactive effect of ambient temperature and brood size manipulation on nestling body mass in blue tits: an exploratory analysis of a long-term study.

BACKGROUND: Relatively few studies have examined the interactive effects of ecological factors on physiological responses in wild animals. Nearly all of them have been short-term investigations that did not include experimental manipulations, limiting our ability to understand how climate change will affect natural populations. Using a 10-year brood size manipulation experiment in wild blue tits (Cyanistes caeruleus), we quantified the impact of weather conditions and brood competition on the body mass and structural size (tarsus length) of nestlings just prior to leaving the nest. RESULTS: We found that variation in nestling body mass on day 14 after hatching was explained by an interactive effect between average ambient temperature experienced during nestling period and brood size treatment. Specifically, in control broods nestling body mass was correlated with temperature in a non-linear manner (concave) with the vertex point (maximum body mass) at ca. 13 °C. In contrast, in enlarged broods nestling body mass permanently increased (also non-linearly) as temperature advanced. CONCLUSIONS: Our results highlight the importance of considering the effects of brood rearing conditions alongside other environmental factors experienced during growth while investigating early-life environmental effects on body condition.

Seasonal weather effects on offspring survival differ between reproductive stages in a long-lived neotropical seabird.

Weather conditions can profoundly affect avian reproduction. It is known that weather conditions prior to and after the onset of reproduction can affect the breeding success of birds. However, little is known about how seasonal weather variability can affect birds' breeding performance, particularly for species with a slow pace of life. Long-term studies are key to understanding how weather variability can affect a population's dynamics, especially when extreme weather events are expected to increase with climate change. Using a 32-year population study of the Blue-footed booby (Sula nebouxii) in Mexico, we show that seasonal variation in weather conditions, predominantly during the incubation stage, affects offspring survival and body condition at independence. During most of the incubation period, warm sea surface temperatures were correlated with low hatching success, while rainfall in the middle of the incubation stage was correlated with high fledging success. In addition, chicks from nests that experienced warm sea surface temperatures from the pre-laying stage to near-fledging had lower body condition at 70 days of age. Finally, we show that variable annual SST conditions before and during the incubation stage can impair breeding performance. Our results provide insight into how seasonal and interannual weather variation during key reproductive stages can affect hatching success, fledging success, and fledgling body condition in a long-lived neotropical seabird.

The influence of weather conditions on the COVID-19 epidemic: Evidence from 279 prefecture-level panel data in China.

Studying the influence of weather conditions on the COVID-19 epidemic is an emerging field. However, existing studies in this area tend to utilize time-series data, which have certain limitations and fail to consider individual, social, and economic factors. Therefore, this study aimed to fill this gap. In this paper, we explored the influence of weather conditions on the COVID-19 epidemic using COVID-19-related prefecture-daily panel data collected in mainland China between January 1, 2020, and February 19, 2020. A two-way fixed effect model was applied taking into account factors including public health measures, effective distance to Wuhan, population density, economic development level, health, and medical conditions. We also used a piecewise linear regression to determine the relationship in detail. We found that there is a conditional negative relationship between weather conditions and the epidemic. Each 1 °C rise in mean temperature led to a 0.49% increase in the confirmed cases growth rate when mean temperature was above -7 °C. Similarly, when the relative humidity was greater than 46%, it was negatively correlated with the epidemic, where a 1% increase in relative humidity decreased the rate of confirmed cases by 0.19%. Furthermore, prefecture-level administrative regions, such as Chifeng (included as "warning cities") have more days of "dangerous weather", which is favorable for outbreaks. In addition, we found that the impact of mean temperature is greatest in the east, the influence of relative humidity is most pronounced in the central region, and the significance of weather conditions is more important in the coastal region. Finally, we found that rising diurnal temperatures decreased the negative impact of weather conditions on the spread of COVID-19. We also observed that strict public health measures and high social concern can mitigate the adverse effects of cold and dry weather on the spread of the epidemic. To the best of our knowledge, this is the first study which applies the two-way fixed effect model to investigate the influence of weather conditions on the COVID-19 epidemic, takes into account socio-economic factors and draws new conclusions.

Embedding Weather Simulation in Auto-Labelling Pipelines Improves Vehicle Detection in Adverse Conditions.

The performance of deep learning-based detection methods has made them an attractive option for robotic perception. However, their training typically requires large volumes of data containing all the various situations the robots may potentially encounter during their routine operation. Thus, the workforce required for data collection and annotation is a significant bottleneck when deploying robots in the real world. This applies especially to outdoor deployments, where robots have to face various adverse weather conditions. We present a method that allows an independent car tansporter to train its neural networks for vehicle detection without human supervision or annotation. We provide the robot with a hand-coded algorithm for detecting cars in LiDAR scans in favourable weather conditions and complement this algorithm with a tracking method and a weather simulator. As the robot traverses its environment, it can collect data samples, which can be subsequently processed into training samples for the neural networks. As the tracking method is applied offline, it can exploit the detections made both before the currently processed scan and any subsequent future detections of the current scene, meaning the quality of annotations is in excess of those of the raw detections. Along with the acquisition of the labels, the weather simulator is able to alter the raw sensory data, which are then fed into the neural network together with the labels. We show how this pipeline, being run in an offline fashion, can exploit off-the-shelf weather simulation for the auto-labelling training scheme in a simulator-in-the-loop manner. We show how such a framework produces an effective detector and how the weather simulator-in-the-loop is beneficial for the robustness of the detector. Thus, our automatic data annotation pipeline significantly reduces not only the data annotation but also the data collection effort. This allows the integration of deep learning algorithms into existing robotic systems without the need for tedious data annotation and collection in all possible situations. Moreover, the method provides annotated datasets that can be used to develop other methods. To promote the reproducibility of our research, we provide our datasets, codes and models online.

Leveraging Artificial Intelligence and Fleet Sensor Data towards a Higher Resolution Road Weather Model.

Road weather conditions such as ice, snow, or heavy rain can have a significant impact on driver safety. In this paper, we present an approach to continuously monitor the road conditions in real time by equipping a fleet of vehicles with sensors. Based on the observed conditions, a physical road weather model is used to forecast the conditions for the following hours. This can be used to deliver timely warnings to drivers about potentially dangerous road conditions. To optimally process the large data volumes, we show how artificial intelligence is used to (1) calibrate the sensor measurements and (2) to retrieve relevant weather information from camera images. The output of the road weather model is compared to forecasts at road weather station locations to validate the approach.

Modelling soil salinity effects on salt water uptake and crop growth using a modified denitrification-decomposition model: A phytoremediation approach.

Soil salinization is a widespread problem affecting global food production. Phytoremediation is emerging as a viable and cost-effective technology to reclaim salt-affected soil. However, its efficiency is not clear due to the uncertainty of plant responses in saline soils. The main objective of this paper is to propose a phytoremediation dynamic model (PDM) for salt-affected soil within the process-based biogeochemical denitrification-decomposition (DNDC) model. The PDM represents two salinity processes of phytoremediation: plant salt uptake and salt-affected biomass growth. The salt-soil-plant interaction is simulated as a coupled mass balance equation of water and salt plant uptake. The salt extraction ability by plant is a combination of salt uptake efficiency (F) and transpiration rate. For water filled pore space (WFPS), the statistical measures RMSE, MAE, and R2 during the calibration period are 2.57, 2.14, and 0.49, and they are 2.67, 2.34, and 0.56 during the validation period, respectively. For soil salinity, RMSE, MAE, and R2 during the calibration period are 0.02, 0.02, and 0.92, and 0.06, 0.04, and 0.68 during the validation period, respectively, which are reasonably good for further scenario analysis. Over the four years, cumulative salt uptake varied based on weather conditions. At the optimal salt uptake efficiency (F = 20), cumulative salt uptake from soil was 16-90% for alfalfa, 11-70% for barley, and 10-80% for spring wheat. While at the lowest salt uptake efficiency (F = 40), cumulative salt uptake was nearly zero for all crops. Although barley has the highest peak transpiration flux, alfalfa and spring wheat have greater cumulative salt uptake because their peak transpiration fluxes occurred more frequently than in barley. For salt-tolerant crops biomass growth depends on their threshold soil salinity which determines their ability to take up salt without affecting biomass growth. In order to phytoremediate salt-affected soil, salt-tolerant crops having longer duration of crop physiological stages should be used, but their phytoremediation effectiveness will depend on weather conditions and the soil environment.

Monitoring water transparency, total suspended matter and the beam attenuation coefficient in inland water using innovative ground-based proximal sensing technology.

Long-term and high-frequency observations are vital to reveal water quality dynamics and responses to climate change and human activities. However, the datasets collected from traditional in situ and satellite observations may miss the rapid dynamics of water quality in the short term due to low temporal-spatial monitoring frequency and cloudy or rainy weather. To address this shortage, innovative ground-based proximal sensing (GBPS) technology was proposed to monitor water quality and identify emergencies with a wavelength range of 400-1000 nm, a spectral resolution of 1 nm and a minimal observation interval of 30 s. The GBPS was equipped with a hyperspectral imager placed 4-5 m above the water surface to minimize the impacts of the atmosphere and clouds. In this study, combined with 583 water samples obtained from four field samplings, GBPS datasets were first applied to estimate the total suspended matter (TSM), Secchi disk depth (SDD) and beam attenuation coefficient at 550 nm (C(550)) in Taihu Lake (TL), Liangxi River (LR) and Funchunjiang Reservoir (FR). The results demonstrated good performance with the TSM (R2 = 0.83, RMSE = 8.35 mg/L, MAPE = 24.0%), SDD (R2 = 0.88, RMSE = 0.09 m, MAPE = 14.7%), and C(550) (R2 = 0.79, RMSE = 3.55 m-1, MAPE = 35.8%). The time series of TSM and C(550) at the second-minute level showed consistent changes, but they were opposite to those of SDD. Taking TSM as an example, the datasets captured two mutations in TL with an 853.6% increase in 65 min and a rapid change from 40.3 mg/L to 256.9 mg/L and then to 51.0 mg/L in 224 min on November 1 and 3, respectively. Meanwhile, a significant decreasing trend (r = -0.83, p < 0.01) in LR from November 7 to 9 and a periodic diurnal increasing trend of TSM in FR during November 11 to 13 (0.46 ≤ R2 ≤ 0.70, p < 0.01) were observed. GBPS, with the advantages of high-frequency observations and the applicability of complex weather conditions, compensates for the in situ, aircraft and satellite observation deficiencies. Therefore, GBPS allows us to capture more detailed water quality information and episodic events, which is an important part of an integrated air-space-ground monitoring system in the future.