Search Results (94)

In this study, a magnet-integrated fabric phase sorptive extraction (MI-FPSE) protocol was developed in combination with high pressure liquid chromatography—diode array detection (HPLC-DAD) for the simultaneous determination of five phenylurea pesticides (i.e., chlorbromuron, diuron, linuron, metoxuron, monuron) in environmental water samples. To produce the MI-FPSE device, two individual sol-gel coated carbowax 20 M (CW 20 M) cellulose membranes were fabricated and stitched to each other, while a magnetic rod was inserted between them to give the resulting device the ability to spin and serve as a stand-alone microextraction platform. The adsorption and desorption step of the MI-FPSE protocol was optimized to achieve high extraction efficiency and the MI-FPSE-HPLC-DAD method was validated in terms of linearity, sensitivity, selectivity, accuracy, and precision. The limits of detection (LODs) were found to be 0.3 μg L⁻¹. The relative recoveries were 85.2–110.0% for the intra-day and 87.7–103.2% for the inter-day study. The relative standard deviations were better than 13% in all cases. The green character and the practicality of the developed procedure were assessed using ComplexGAPI and Blue Analytical Grade Index metric tools, showing good method performance. Finally, the developed method was successfully used for the analysis of tap, river, and lake water samples. Full article

While the existing research has extensively explored the impact of urban green spaces on residents’ well-being, studies specifically focusing on waterfront sports buildings remain scarce. This study examines how the combined effects of built environment characteristics in waterfront sports facilities enhance user satisfaction through psychological mechanisms. Based on survey data from 721 users across nine major waterfront sports complexes in China, we find that (1) four social function dimensions (social interaction, accessibility, safety, and multifunctionality) show significant positive correlations with satisfaction; (2) place attachment mediates these relationships. These findings validate the importance of integrating water-oriented design principles with community needs, offering both theoretical contributions to human–water interaction studies and practical implications for urban blue space (defined as visible water features including rivers, lakes, and coastal areas) development. Full article

Extreme weather events and rising temperatures pose significant risks, not only in urban areas but also in metropolitan forests, that affect the well-being of the people who visit them. City forests are considered one of the best bets for mitigating high temperatures within civic areas. Such areas modulate microclimates in contemporary cities, offering environmental, social, and economic advantages. Therefore, comprehending the intricate relationships between municipal forests and the climatic changes of various destinations is crucial for attaining healthier and more sustainable city environments for people. In this research, the thermal comfort index (Modified Temperature–Humidity Index (MTHI)) has been analysed using Landsat images of six urban forests in London during July 2022, when the area first experienced record-breaking temperatures of over 40 °C. Our results show a significant growth in the MTHI that goes from 2.5 (slightly hot) under normal conditions to 3.4 (hot) during the heat wave period. This situation intensifies the environmental discomfort for visitors and highlights the necessity to enhance their adaptability to future temperature increases. In turn, it was found that the places most affected by heat waves are those that have grass cover or that have small associated buildings. Conversely, forested regions or those with lakes and/or ponds exhibit lower temperatures, which results in enhanced resilience. These findings are noteworthy in their concentration on one of the UK’s most severe heat waves and illustrate the efficacy of integrating spectral measurements with statistical analyses to formulate customized regional initiatives. Therefore, the results reported will allow the implementation of new planning and adaptation policies such as incorporating thermal comfort into planning processes, improving green and blue amenities, increasing tree densities that are resilient to rising temperatures, and increasing environmental comfort conditions in metropolitan forests. Finally, the applicability of this approach in similar urban contexts is highlighted. Full article

Lake Hume, a critical reservoir within the Murray River system, Australia, has been identified as a potential source of cyanobacteria in downstream rivers during past mega-blooms. This study aims to evaluate the impact of lake-level fluctuations on cyanobacterial abundance at the dam outlets, with the goal of mitigating the risk of cyanobacteria intake from hydropower and irrigation outlets during periods of low dam levels. Utilising a one-dimensional vertical hydrodynamic model (LAKEoneD), the study simulated time series data on water temperature and stratification within Lake Hume. These outputs were then incorporated into a cyanobacteria growth model driven by water temperature, mixing dynamics and light. Despite inherent uncertainties in the models, the simulated cell counts effectively mirrored bloom occurrences. Consequently, a series of simulations across varying water levels in the lake revealed a consistent risk of significant cyanobacteria intake through both the hydropower and irrigation outlets when water levels dropped below specific thresholds. Notably, water levels below 20 m and 10 m posed heightened risks of releases of seed populations of cyanobacteria from the hydropower and irrigation outlets, respectively. Full article

S-wave seismic data are unaffected by natural gas trapped in strata, making them a valuable tool to study evaporite facies comparing to P-wave data. S-wave seismic data were utilized to construct an isochronous framework and analyze evaporite facies by seismic sedimentology methods in the Quaternary biogenic gas-bearing Taidong area, Sanhu Depression, Qaidam Basin, NW China, with calibration from wireline logs, geochemical evidences, and modern analogs. Techniques of phase rotation, frequency decomposition, R (Red), G (Green), B (Blue) fusion, and stratal slices were integrated to reconstruct seismic geomorphological features. Linear and sub-circular morphologies, resembling those observed in modern saline pans such as Lake Chad, were identified. Observations from Upper Pleistocene outcrops of anhydrite and halite at Yanshan (east of the Taidong area), along with lithological and paleo-environmental records from boreholes SG-5, SG-1, and SG-1b (northwest of the Taidong area), support the seismic findings. The slices generated from the S-wave seismic data indicate a progressive increase in the occurrence of evaporite features from the K2 standard zone upwards. The vertical occurrence of evaporite facies in the Taidong area increases, which coincides with the contemporary regional and global arid paleo-environmental changes. The interpretation of Quaternary stratal slices reveals a transition from a freshwater lake to brackish, saline, and finally, a dry saline pan, overlaid by silt. This analysis provides valuable insights into locating evaporites as cap rocks for biogenic gas accumulation and also into mining the evaporite mineral resources in shallow layers of the Taidong area. Full article

The aquatic plant species Eichhornia crassipes, commonly known as water hyacinth, is indigenous to South America and is considered an invasive species. The invasive water hyacinth has caused significant economic and ecological damage by preventing sunlight from penetrating the surface of the water, resulting in the loss of aquatic life. To quantify the invasiveness and address the issue of accurately identifying plant species, water hyacinths have prompted numerous researchers to propose approaches to detect regions occupied by water hyacinths. One such solution involves the utilization of multispectral imaging which obtain detailed information about plant species based on the surface reflectance index. This is achieved by analyzing the intensity of light spectra at different wavelengths emitted by each plant. However, the use of multispectral imagery presents a potential challenge since there are various spectral indices that can be used to capture different information. Despite the high accuracy of these multispectral images, there remains a possibility that plants similar to water hyacinths may be misclassified if the right spectral index is not chosen. Considering this challenge, the objective of this research is to develop a low-cost multispectral camera capable of capturing multispectral images. The camera will be equipped with two infrared light spectrum filters with wavelengths of 720 and 850 nanometers, respectively, as well as red, blue, and green light spectrum filters. Additionally, the implementation of the U-Net architecture is proposed for semantic segmentation to accurately identify water hyacinths, as well as other classes such as lakes and land. An accuracy rate of 96% was obtained for the identification of water hyacinths using data captured by an autonomous drone constructed in the laboratory flying at an altitude of 10 m. We also analyzed the contribution each of the infrared layers to the camera’s spectrum setup. Full article

Fractional vegetation cover (FVC) plays a key role in ecological and environmental status assessment because it directly reflects the extent of vegetation cover and its status, yet vegetation is an important component of ecosystems. FVC estimation methods have evolved from traditional manual interpretation to advanced remote sensing technologies, such as satellite data analysis and unmanned aerial vehicle (UAV) image processing. Extraction methods based on high-resolution UAV data are being increasingly studied in the fields of ecology and remote sensing. However, research on UAV-based FVC extraction against the backdrop of the high soil reflectance in arid regions remains scarce. In this paper, based on 12 UAV visible light images in differentiated scenarios in the Ebinur Lake basin, Xinjiang, China, various methods are used for high-precision FVC estimation: Otsu’s thresholding method combined with 12 Visible Vegetation Indices (abbreviated as Otsu-VVIs) (excess green index, excess red index, excess red minus green index, normalized green–red difference index, normalized green–blue difference index, red–green ratio index, color index of vegetation extraction, visible-band-modified soil-adjusted vegetation index, excess green minus red index, modified green–red vegetation index, red–green–blue vegetation index, visible-band difference vegetation index), color space method (red, green, blue, hue, saturation, value, lightness, ‘a’ (Green–Red component), and ‘b’ (Blue–Yellow component)), linear mixing model (LMM), and two machine learning algorithms (a support vector machine and a neural network). The results show that the following methods exhibit high accuracy in FVC extraction across differentiated scenarios: Otsu–CIVE, color space method (‘a’: Green–Red component), LMM, and SVM (Accuracy > 0.75, Precision > 0.8, kappa coefficient > 0.6). Nonetheless, higher scene complexity and image entropy reduce the applicability of precise FVC extraction methods. This study facilitates accurate, efficient extraction of vegetation information in differentiated scenarios within arid and semiarid regions, providing key technical references for FVC estimation in similar arid areas. Full article

Cyanobacteria (blue-green algae) are a diverse group of prokaryotic microorganisms that impact global biogeochemical cycles. Under eutrophic conditions, cyanobacterial species can produce cyanotoxins, resulting in harmful algal blooms (cHABs) that degrade water quality and result in economic and recreational losses. The Laurentian Great Lakes, a key global freshwater source, are increasingly affected by these blooms. To understand the underlying mechanisms in cHAB formation, we investigated microcystin levels, cyanotoxin genes/transcripts, and taxonomic/microcystin metabarcoding across three sampling locations in the Canadian Great Lakes region, including Hamilton Harbour, Bay of Quinte, and Three Mile Lake (Muskoka), to observe the regional and longitudinal cyanobacterial dynamics. The results revealed a positive correlation between microcystin levels, the occurrence of cyanobacterial taxonomic/cyanotoxin molecular markers, and the relative widespread abundance of specific dominant cyanobacterial taxa, including Planktothrix, Microcystis, and Dolichospermum. The Cyanobium genus was not observed in Hamilton Harbor samples during late summer (August to September), while it was consistently observed in the Three Mile Lake and Bay of Quinte samples. Notably, Dolichospermum and saxitoxin genes were predominantly higher in Three Mile Lake (an inland lake), suggesting site-specific characteristics influencing saxitoxin production. Additionally, among the potential microcystin producers, in addition to Microcystis, Hamilton Harbour and Bay of Quinte samples showed consistent presence of less dominant microcystin-producing taxa, including Phormidium and Dolichospermum. This study highlights the complexity of cHAB formation and the variability in cyanotoxin production in specific environments. The findings highlight regional and site-specific factors that can influence cyanobacterial taxonomic and molecular profiles, necessitating the integration of advanced molecular technologies for effective monitoring and targeted management strategies. Full article

The detection of complex formations, initially suspected to be oil spills, is investigated using atmospherically corrected multispectral satellite images and deep learning techniques. Several formations have been detected in an inland lake in Northern Greece. Four atmospheric corrections (ACOLITE, iCOR, Polymer, and C2RCC) that are specifically designed for water applications are examined and implemented on Sentinel-2 multispectral satellite images to eliminate the influence of the atmosphere. Out of the four algorithms, iCOR and ACOLITE are able to depict the formations sufficiently; however, the latter is chosen for further processing due to fewer uncertainties in the depiction of these formations as anomalies across the multispectral range. Furthermore, a number of formations are annotated at the pixel level for the 10 m bands (red, green, blue, and NIR), and a deep neural network (DNN) is trained and validated. Our results show that the four-band configuration provides the best model for the detection of these complex formations. Despite not being necessarily related to oil spills, studying these formations is crucial for environmental monitoring, pollution detection, and the advancement of remote sensing techniques. Full article

Chlorophyll-a (Chl-a), a proxy for phytoplankton biomass, is one of the few biological water quality indices detectable using satellite observations. However, models for estimating Chl-a from satellite signals are currently unavailable for many lakes. The application of Chl-a prediction algorithms may be affected by the variance in optical complexity within lakes. Using Lake Winnipeg in Canada as a case study, we demonstrated that separating models by the lake’s basins [north basin (NB) and south basin (SB)] can improve Chl-a predictions. By calibrating more than 40 commonly used Chl-a estimation models using Landsat data for Lake Winnipeg, we achieved higher correlations between in situ and predicted Chl-a when building models with separate Landsat-to-in situ matchups from NB and SB (R² = 0.85 and 0.76, respectively; p < 0.05), compared to using matchups from the entire lake (R² = 0.38, p < 0.05). In the deeper, more transparent waters of the NB, a green-to-blue band ratio provided better Chl-a predictions, while in the shallower, highly turbid SB, a red-to-green band ratio was more effective. Our approach can be used for rapid Chl-a modeling in large lakes using cloud-based platforms like Google Earth Engine with any available satellite or time series length. Full article

Pollution is the biggest environmental and health threat in the world. Conventional treatments of polluted habitats require the removal of pollutants contaminating the environment, but removal methods are costly and involve high power consumption. This research aims to investigate the potential for bioremediation and proposes an alternative source for implementing it that is cheaper and more environmentally friendly. The phycobioremediation experiment used hydrocarbon- and heavy-metal-polluted water from Al Asfar Lake, AlAhsa, KSA. The isolation and characterization of the lake’s predominant microalgae and associated bacteria were carried out. Monoalgal cultures of the dominant genera of algae were employed for the treatment of contaminated water and soil samples. The concentrations of the heavy metals and hydrocarbons in these samples were determined before and after the treatments by using atomic absorption spectroscopy (for heavy metals) and gas chromatography (for hydrocarbons). From the initial assessments, the levels of manganese, copper, and chromium were high, with chromium being the highest. Three microalgal isolates were identified: two coccoid, with one being blue-green and the other green, and one filamentous cyanobacterium. These species were the most efficient in removing heavy metals and dangerous hydrocarbons. Molecular characterization revealed Chlorella sp. and Geitlarianema sp. to be the most promising for bioremediation. The present work sheds light on the prospect of using algal and bacterial consortia for optimized, safe, and eco-friendly pollution amelioration. Full article

Under the background of global change, the lake water environment is facing a huge threat from eutrophication. The rapid increase in curly-leaf pondweed (Potamogeton crispus L.) in recent years has seriously threatened the ecological balance and the water diversion safety of the eastern route of China’s South-to-North Water Diversion Project. The monitoring and control of curly-leaf pondweed is imperative in shallow lakes of northern China. Unmanned Aerial Vehicles (UAVs) have great potential for monitoring aquatic vegetation. However, merely using satellite remote sensing to detect submerged vegetation is not sufficient, and the monitoring of UAVs on aquatic vegetation is rarely systematically evaluated. In this study, taking Nansi Lake as a case, we employed Red–Green–Blue (RGB) UAV and satellite datasets to evaluate the monitoring of RGB Vegetation Indices (VIs) in pondweed and mapped the dynamic patterns of the pondweed Fractional Vegetation Coverage (FVC) in Nansi Lake. The pondweed FVC values were extracted using the RGB VIs and the machine learning method. The extraction of the UAV RGB images was evaluated by correlations, accuracy assessments and separability. The correlation between VIs and FVC was used to invert the pondweed FVC in Nansi Lake. The RGB VIs were also calculated using Gaofen-2 (GF-2) and were compared with UAV and Sentinel-2 data. Our results showed the following: (1) The RGB UAV could effectively monitor the FVC of pondweed, especially when using Support Vector Machine that (SVM) has a high ability to recognize pondweed in UAV RGB images. Two RGB VIs, RCC and RGRI, appeared best suited for monitoring aquatic plants. The correlations between four RGB VIs based on GF-2, i.e., GCC, BRI, VDVI, and RGBVI and FVC_SVM calculated by the UAV (p < 0.01) were better than those obtained with other RGB VIs. Thus, the RGB VIs of GF-2 were not as effective as those of the UAV in pondweed monitoring. (2) The binomial estimation model constructed by the Normalized Difference Water Index (NDWI) of Sentinel-2 showed a high accuracy (R² = 0.7505, RMSE = 0.169) for pondweed FVC and can be used for mapping the FVC of pondweed in Nansi Lake. (3) Combined with the Sentinel-2 time-series data, we mapped the dynamic patterns of pondweed FVC in Nansi Lake. It was determined that the flooding of pondweed in Nansi Lake has been alleviated in recent years, but the rapid increase in pondweed in part of Nansi Lake remains a challenging management issue. This study provides practical tools and methodology for the innovative remote sensing monitoring of submerged vegetation. Full article

Rapid urbanization leads to fragmentation and reduced connectivity of urban landscapes, endangering regional biodiversity conservation and sustainable development. Constructing a red, green, and blue spatial ecological network is an effective way to alleviate ecological pressure and promote economic development. Using circuit theory, hydrological analysis, and suitability analysis, this study constructs a composite ecological network under urban–rural integration. The results show the following: (1) A total of 22 ecological corridors with a length of 349.20 km, 22 ecological pinch points, and 22 ecological barrier points are identified in the municipal area, mainly distributed in Haidong Town. There are 504 stormwater corridors, which are more evenly distributed, 502 riverfront landscape corridors, and 130 slow-moving landscape corridors. (2) A total of 20 ecological corridors, with a length of 99.23 km, 19 ecological pinch points, and 25 barrier points were identified in the main urban area, and most of them are located in the ecological corridors. There are 71 stormwater corridors, mainly located in the northwestern forest area, 71 riverfront recreation corridors, and 50 slow-moving recreation corridors. (3) Two scales of superimposed ecological source area of 3.65 km², and eleven ecological corridors, are primarily distributed between Erhai Lake and Xiaguan Town. There are two superimposed stormwater corridors and fourteen recreational corridors. The eco-nodes are mostly distributed in the east and south of Dali City; wetland nodes are mainly situated in the eighteen streams of Cangshan Mountain; and landscape nodes are more balanced in spatial distribution. The study results can provide a reference for composite ecological network construction. Full article

Water conservation efforts and studies receive special attention, versatile and constantly developing remote sensing methods especially so. The quality and quantity of algae fundamentally influence the ecosystems of water bodies. Inland lakes are less-frequently studied despite their essential ecological role compared to ocean and sea waters. One of the reasons for this is the small-scale surface extension, which poses challenges during satellite remote sensing. In this study, we investigated the correlations between remote-sensing- (via Seninel-2 satellite) and laboratory-based results in different chlorophyll-a concentration ranges. In the case of low chlorophyll-a concentrations, the measured values were between 15 µg L⁻¹ and 35 µg L⁻¹. In the case of medium chlorophyll-a concentrations, the measured values ranged between 35 and 80 µg L⁻¹. During high chlorophyll-a concentrations, the results were higher than 80 µg L⁻¹. Finally, under extreme environmental conditions (algal bloom), the values were higher than 180 µg L⁻¹. We also studied the accuracy and correlation and the different algorithms applied through the Acolite (20231023.0) processing software. The chl_re_mishra algorithm of the Acolite software gave the highest correlation. The strong positive correlations prove the applicability of the Sentinel-2 images and the Acolite software in the indication of chlorophyll-a. Because of the high CDOM concentration of Lake Naplás, the blue–green band ratio underestimated the concentration of chlorophyll-a. In summer, higher chlorophyll-a was detected in both laboratory and satellite investigations. In the case of extremely high chlorophyll-a concentrations, it is significantly underestimated by satellite remote sensing. This study proved the applicability of remote sensing to detect chlorophyll-a content but also pointed out the current limitations, thus assigning future development and research directions. Full article

Heat continues to be a hazard in the desert southwestern USA. This study presents the results of a preliminary microclimate field survey in two Avondale, Arizona, neighborhoods developed with artificial wastewater-treatment wetlands and one adjacent desert neighborhood. The preliminary field study reported here measured morning, near-noon, and afternoon air temperatures and, together with other observed variables, calculated mean radiant temperatures (critical to human comfort) at 28 locations across three neighborhoods on a sample day in September of 2018. The aim was to determine cooling effects of blue/green environments and identify benefits for residents. Overall results for September indicate 1–3 °C cooling, which is understandable for this time of year at summer’s end. Mean radiant temperature results are substantially different at lake sites versus dry neighborhood sites (by some 5–20 °C), likely due to the presence of fewer lateral radiant fluxes and cooler exposures at lake sites compared with dry neighborhoods. Cooling benefits likely provide year-round outdoor comfort compared to desert-landscaped communities. The authors reinforce the conclusion that recycled water and treatment systems can reduce local heat island conditions and aid in combating extreme heat in the desert southwest. This study also shows that constructed wastewater-treatment wetlands in desert cities support sustainable residential developments. Full article