Search Results (6,596)

Forest fire is one of the dominant disturbances in the forests of Heilongjiang Province, China, and is one of the most rapid response predictors that indicate the impact of climate change on forests. This study calculated the Canadian FWI (Fire Weather Index) and its components from meteorological record over past years, and a linear model was built from the monthly mean FWI and monthly fire numbers. The significance test showed that fire numbers and FWI had a very pronounced correlation, and monthly mean FWI was suitable for predicting the monthly fire numbers in this region. Then FWI and its components were calculated from the SRES (IPCC Special Report on Emission Scenarios) A2 and B2 climatic scenarios, and the linear model was rebuilt to be suitable for the climatic scenarios. The results indicated that fire numbers would increase by 2.98%–129.97% and −2.86%–103.30% in the A2 and B2 climatic scenarios during 2020–2090, respectively. The monthly variation tendency of the FWI components is similar in the A2 and B2 climatic scenarios. The increasing fire risk is uneven across months in these two climatic scenarios. The monthly analysis showed that the FFMC (Fine Fuel Moisture Code) would increase dramatically in summer, and the decreasing precipitation in summer would contribute greatly to this tendency. The FWI would increase rapidly from the spring fire season to the autumn fire season, and the FWI would have the most rapid increase in speed in the spring fire season. DMC (Duff Moisture Code) and DC (Drought Code) have relatively balanced rates of increasing from spring to autumn. The change in the FWI in this region is uneven in space as well. In early 21st century, the FWI of the north of Heilongjiang Province would increase more rapidly than the south, whereas the FWI of the middle and south of Heilongjiang Province would gradually catch up with the increasing speed of the north from the middle of 21st century. The changes in the FWI across seasons and space would influence the fire management policy in this region, and the increasing fire numbers and variations in the FWI scross season and space suggest that suitable development of the management of fire sources and forest fuel should be conducted. Full article

Fractional vegetation cover (FVC) is a crucial indicator for measuring the growth of surface vegetation. The changes and predictions of FVC significantly impact biodiversity conservation, ecosystem health and stability, and climate change response and prediction. Southwest China (SWC) is characterized by complex topography, diverse climate types, and rich vegetation types. This study first analyzed the spatiotemporal variation of FVC at various timescales in SWC from 2000 to 2020 using FVC values derived from pixel dichotomy model. Next, we constructed four machine learning models—light gradient boosting machine (LightGBM), support vector regression (SVR), k-nearest neighbor (KNN), and ridge regression (RR)—along with a weighted average heterogeneous ensemble model (WAHEM) to predict growing-season FVC in SWC from 2000 to 2023. Finally, the performance of the different ML models was comprehensively evaluated using tenfold cross-validation and multiple performance metrics. The results indicated that the overall FVC in SWC predominantly increased from 2000 to 2020. Over the 21 years, the FVC spatial distribution in SWC generally showed a high east and low west pattern, with extremely low FVC in the western plateau of Tibet and higher FVC in parts of eastern Sichuan, Chongqing, Guizhou, and Yunnan. The determination coefficient R² scores from tenfold cross-validation for the four ML models indicated that LightGBM had the strongest predictive ability whereas RR had the weakest. WAHEM and LightGBM models performed the best overall in the training, validation, and test sets, with RR performing the worst. The predicted spatial change trends were consistent with the MODIS-MOD13A3-FVC and FY3D-MERSI-FVC, although the predicted FVC values were slightly higher but closer to the MODIS-MOD13A3-FVC. The feature importance scores from the LightGBM model indicated that digital elevation model (DEM) had the most significant influence on FVC among the six input features. In contrast, soil surface water retention capacity (SSWRC) was the most influential climate factor. The results of this study provided valuable insights and references for monitoring and predicting the vegetation cover in regions with complex topography, diverse climate types, and rich vegetation. Additionally, they offered guidance for selecting remote sensing products for vegetation cover and optimizing different ML models. Full article

Background: Nutrition, cooking, and gardening lessons individually and together have been shown to increase fruit and vegetable (FV) consumption in school-aged children. The CATCH Rainbow program incorporated nutrition education, cooking, and gardening lessons aimed at increasing FV consumption in elementary school-aged children and assessed changes in participants’ BMI, self-reported FV consumption, and skin carotenoid levels at baseline and post-intervention. Methods: Two-hundred and twenty-five 4th and 5th graders (mean age: 9.8 years and 52% male participants) at Genoa Elementary School participated in six cooking and six gardening sessions between September 2021 and May 2022. Each nutrition education session was 25 min long, paired with either hands-on cooking activities or gardening skills. At baseline and post-intervention, participants’ height and weight were assessed with a stadiometer/scale, and skin carotenoid measurement was taken by a Veggie Meter^® (Longevity Link Corporation (Salt Lake City, UT, USA)). Students also completed the Block Food Frequency Questionnaire to self-report FV consumption at both time points. Focus groups were conducted with children at the end of the program for qualitative feedback. Results: paired samples T-test and regression analysis results indicate no significant decrease in BMI or significant increase in skin carotenoid scores from pre- to post-intervention. However, though not significant, there was an increase in self-reported FV intake by 0.4 servings. Additionally, the qualitative feedback was positive, as children mentioned benefits of healthy eating and expressed enjoyment for growing, cooking, and tasting fruits and vegetables. Conclusion: Results from this study can be used to guide future cooking and gardening programs for elementary school children. Time of the year when implementing these programs and collecting data may impact study outcomes due to seasonal variations in fruit and vegetable intake. Full article

This research addresses a gap in localized air quality assessments by measuring pollution levels in Klaipeda, a Baltic port city, using passive solid particle collectors and nitrogen dioxide (NO₂) diffusion tubes. Passive sampling techniques were employed due to their cost-effectiveness and ease of deployment, allowing for practical monitoring over short-term periods. By targeting diverse functional zones, this study aims to provide a comprehensive analysis of air pollution patterns and seasonal variations in the region. Air pollution, primarily from NO₂ and particulate matter (PM), poses significant risks to public health, especially in densely populated urban areas. Air quality was assessed by measuring total suspended particulates (TSP) and NO₂ concentrations across 19 strategically chosen sites, covering key functional zones, such as industrial areas, green spaces, residential neighborhoods, transport hubs, and the port. Results show elevated pollution levels near major roads and the port area, likely driven by heavy traffic, industrial emissions, and port activities. These patterns correlate with areas of higher population density, highlighting the intersection of air quality challenges with human health risks in urbanized zones. Seasonal data reveal a notable peak in NO₂ concentrations during winter, likely due to increased heating demand and reduced atmospheric dispersion. These findings suggest that air quality management strategies should be adaptive to seasonal fluctuations, particularly by addressing emissions from heating sources in colder months. The study underscores the necessity of integrating sustainable urban planning with targeted air quality interventions. Expanding green spaces, enhancing traffic regulation, and establishing protective zones near industrial areas are critical strategies for mitigating pollution. These insights are essential for guiding both urban development and public health policies in Klaipeda and other coastal cities facing similar environmental challenges. Full article

The performance of heat pump systems for heating and cooling heavily relies on the thermal conditions of their reservoirs. This study introduces a novel thermal reservoir, detailing a 2017 project where the Municipality of Stavanger installed a heat exchanger system on the wall of a main wastewater tunnel beneath the city center. It provides a comprehensive account of the system’s design, installation, and performance, and presents an Artificial Neural Network (ANN) model that predicts heat pump capacity, electricity consumption, and outlet temperature across seasonal variations in wastewater temperatures. By integrating domain knowledge with the ANN, this study demonstrates the model’s capability to detect anomalies in heat pump operations effectively. The network also confirms the consistent performance of the heat exchangers from 2020 to 2024, indicating minimal fouling impacts. This study establishes wastewater heat exchangers as a safe, effective, and virtually maintenance-free solution for heat extraction and rejection. Full article

Quantifying the vertical distribution of leaf chlorophyll content (LCC) is integral for a comprehensive understanding of the physiological status and function of winter wheat crops, having significant implications for crop management and yield optimization. In this study, we investigated the vertical LCC trait of winter wheat during two consecutive field growth seasons using proximal multispectral imaging measurements to evaluate vertical variations of LCC within winter wheat canopies. The results revealed the non-uniform vertical LCC distribution varied across the entire growth season. The effects of nitrogen fertilization rate on LCC among vertical layers increased gradually from upper to lower layers of canopy. To enhance LCC prediction accuracy, this study proposes a deep transfer learning network model for leaf trait estimation (LeafTNet). It integrates the advantages of physical radiative transfer simulations with deep neural network through transfer learning. The results demonstrate that the LeafTNet achieved remarkable predictive performance and strong robustness. Furthermore, the proposed method exhibits superior estimation accuracy compared to empirical statistical method and traditional machine learning method. This study highlights the performance of LeafTNet in accurately and efficiently quantifying LCC from proximal multispectral data, which provide technical support for the estimation of the vertical distribution of leaf traits and improve crop management. Full article

Studies of the impact of biomass burning and the emissions of trace gases from biomass burning, especially using long-term observations, are scarce in China. We utilize solar absorption spectra obtained via ground-based high-resolution Fourier transform infrared (FTIR) spectroscopy to retrieve the atmospheric total columns and vertical profiles of carbon monoxide (CO), formaldehyde (H₂CO), and hydrogen cyanide (HCN) in Hefei, China. Seasonal and interannual variability in the three gases from 2016 to 2022 are analyzed. Atmospheric CO shows significant seasonal variations, peaking during spring and winter, and declining during summer, with a seasonal amplitude of 8.07 × 10¹⁷ molecules cm⁻² and a seasonal variability of 29.35%. H₂CO and HCN have similar seasonal patterns to each other, with high concentrations in summer and low concentrations in winter. The seasonal amplitude of H₂CO and HCN are 1.89 × 10¹⁶ molecules cm⁻² and 2.32 × 10¹⁵ molecules cm⁻², respectively, with a seasonal variability of 133.07% and 34.69%, respectively. The means of the annual variation rate for CO, H₂CO, and HCN are (−2.67 ± 2.88)% yr⁻¹, (2.52 ± 12.48)% yr⁻¹ and (−3.48 ± 7.26)% yr⁻¹, respectively. To assess the influence of biomass burning on the variations in column concentrations of the three gases, the correlation between CO, H₂CO, and HCN was analyzed. The months during which the monthly correlation coefficient between CO and H₂CO with HCN exceeds 0.8, and the fire radiative power (FRP) observed by satellites is larger than its monthly average are regarded as a biomass-burning occurrence in Anhui province. Additionally, the enhancement ratios of ΔH₂CO/ΔCO and ΔHCN/ΔCO were calculated for the periods impacted by the biomass burning. Finally, backward trajectory cluster analysis and the potential source contribution function (PSCF) calculation identified the air mass transport pathways and the potential source areas at the Hefei site. Full article

The growing interest in groundwater as a sustainable resource for water supply regulation is noteworthy. Just as surface reservoirs in many countries are primarily designed to manage seasonal fluctuations throughout the year, aquifers possess significant reserves, making them particularly well suited for interannual regulation, especially during droughts. In the face of climate change, this form of regulation may increasingly highlight the importance of groundwater resources. For instance, the temporary use of groundwater reserves through intensive pumping in arid or semiarid regions, compensating for seasonal or interannual variations in natural water recharge, can significantly affect aquifers. The exploitation of groundwater reserves may lead to adverse effects over time, eventually being deemed overexploitation and subject to environmental or even legal issues. This work assesses the interannual regulation capacity of aquifers and estimates the groundwater renewal rates and periods for aquifers according to river basins. We first present the mathematical background and development of a method to assess the hydrodynamic volumes (renewable groundwater reserves) in large regions. This method builds on prior knowledge of the distribution functions of spring water contributions based on their discharge and for lithological groups exhibiting similar hydrogeological behavior. Furthermore, it establishes a relationship between spring discharges and hydrodynamic volumes, facilitating the integration of the latter based on discharge. Although proposed for Spain, the method can also be implemented to other regions where data are available. Full article

The traditional remote sensing ecological index (RSEI), based on principal component analysis (PCA) to integrate four evaluation indexes: greenness (NDVI), humidity (WET), dryness (NDBSI), and heat (LST), is insufficient to comprehensively consider the influence of each eco-environment evaluation index on eco-environment quality (EEQ). In this research, a new comprehensive remote sensing ecological index (CRSEI) based on the quaternion Copula function is proposed to comprehensively characterize EEQ responded by integrating four eco-environment evaluation indexes. Additionally, the spatiotemporal variation of EEQ in Henan Province is evaluated using monthly CRSEI data from 2001 to 2020. The results show that: (1) The applicability and monitoring accuracy of CRSEI are better than that of RSEI, which can be used to assess the EEQ. (2) The EEQ of Henan Province declined between 2001 and 2010 but significantly improved and rebounded from 2011 to 2020. During this period, CRSEI values were higher in West and South Henan and lowest in central Henan, with West Henan consistently showing the highest values across all seasons. (3) The EEQ in Henan Province exhibited a tendency of deterioration from the central cities outward, followed by improvement from the outer areas back towards the central cities. In 2010, regions with poor EEQ made up 68.3% of the total area, whereas by 2020, regions with excellent EEQ accounted for 74% of the total area. (4) The EEQ was significantly negatively correlated with human activities, while it was positively correlated with precipitation. The research provides a reference and guidance for the scientific assessment of the regional eco-environment. Full article

The Arabian Gulf, a semi-enclosed basin in the Middle East, connects to the Indian Ocean through the Strait of Hormuz and is surrounded by seven arid countries. This study examines the water cycle of the Gulf and its surrounding areas using data from the GRACE and GRACE Follow-On missions, along with ERA5 atmospheric reanalysis data, from 05/2002 to 05/2017 and from 07/2018 to 12/2023. Our findings reveal a persistent water deficit due to high evaporation rates, averaging 370 ± 3 km³/year, greatly surpassing precipitation, which accounts for only 15% of the evaporative loss. Continental runoff provides one-fifth of the needed water, while the remaining deficit, approximately 274 ± 10 km³/year, is balanced by net inflow of saltwater from the Indian Ocean. Seasonal variations show the lowest net inflow of 26 ± 49 km³/year in March and the highest of 586 ± 53 km³/year in November, driven by net evaporation, continental input, and changes in the Gulf’s water budget. This study highlights the complex hydrological dynamics influenced by climate patterns and provides a baseline for future research in the region, which will be needed to quantify the expected changes in the hydrological cycle due to climate change. Full article

The period of freezing is an important phenological characteristic of lakes in the Northern Hemisphere, exhibiting higher sensitivity to regional climate changes and aiding in the detection of Earth’s response to climate change. This review systematically examines 1141 articles on seasonal frozen lakes from 1991 to 2021, aiming to understand the seasonal variations and control conditions of ice-covered lakes. For the former, we discussed the physical structure and growth characteristics of seasonal ice cover, changes in water environmental conditions and primary production, accumulation and transformation of CO₂ beneath the ice, and the role of winter lakes as carbon sources or sinks. We also proposed a concept of structural stratification based on the differences in physical properties of ice and solute content. The latter provided an overview of the ice-covered period (−1.2 d decade⁻¹), lake evaporation (+16% by the end of the 21st century), the response of planktonic organisms (earlier spring blooming: 2.17 d year⁻¹) to global climate change, the impact of greenhouse gas emissions on ice-free events, and the influence of individual characteristics such as depth, latitude, and elevation on the seasonal frozen lakes. Finally, future research directions for seasonally ice-covered lakes are discussed. Considering the limited and less systematic research conducted so far, this study aims to use bibliometric methods to synthesize and describe the trends and main research points of seasonal ice-covered lakes so as to lay an important foundation for scholars in this field to better understand the existing research progress and explore future research directions. Full article

Agricultural land parcels (ALPs) are essential for effective agricultural management, influencing activities ranging from crop yield estimation to policy development. However, traditional methods of ALP delineation are often labor-intensive and require frequent updates due to the dynamic nature of agricultural practices. Additionally, the significant variations across different regions and the seasonality of agriculture pose challenges to the automatic generation of accurate and timely ALP labels for extensive areas. This study introduces the cadastral-to-agricultural (Cad2Ag) framework, a novel approach that utilizes cadastral data as training labels to train deep learning models for the delineation of ALPs. Cadastral parcels, which are relatively widely available and stable elements in land management, serve as proxies for ALP delineation. Employing an adapted U-Net model, the framework automates the segmentation process using remote sensing images and geographic information system (GIS) data. This research evaluates the effectiveness of the proposed Cad2Ag framework in two U.S. regions—Indiana and California—characterized by diverse agricultural conditions. Through rigorous evaluation across multiple scenarios, the study explores diverse scenarios to enhance the accuracy and efficiency of ALP delineation. Notably, the framework demonstrates effective ALP delineation across different geographic contexts through transfer learning when supplemented with a small set of clean labels, achieving an $F_1$-score of 0.80 and an Intersection over Union (IoU) of 0.67 using only 200 clean label samples. The Cad2Ag framework’s ability to leverage automatically generated, extensive, free training labels presents a promising solution for efficient ALP delineation, thereby facilitating effective management of agricultural land. Full article

Streambank erosion in agricultural landscapes contributes high amounts of sediment and total-P to surface water, resulting in the degradation of stream habitats and reduction in ecological services. Moreover, the implication of future climate change on bank erosion is also a growing concern. Streambank erosion rates from riparian forest buffers (RFo), grass filters (GFi), row-crops (RCr) and pastures, including fenced pastures (FPa), rotationally grazed pastures (RPa), intensive rotationally grazed pastures (IPa), and continuously grazed pastures (CPa), in three landform regions of Iowa, were measured over seven years. Bank erosion pins were measured seasonally (spring, summer and fall) in the first five years (2002–2006) and yearly for two more years (2007–2008). It was found that summer and spring seasons are the important ones since the relationships between erosion and precipitation were significantly “strong” in almost all the riparian practices, and precipitation was found to be the main factor driving streambank erosion. Streambank mean soil losses and soil total-P losses from RFo (23.3 tons km⁻¹ yr⁻¹ and 9.8 kg km⁻¹ yr⁻¹, respectively), GFi (31.1 and 9.9) and FPa (44.0 and 23.7) practices were all significantly lower than the grazing pasture practices, including RPa (142.3 and 58), CPa (255 and 105.1), IPa (234.6 and 122.7) and RCr fields (352.9 and 118.9). Also, RPa had significantly lower total-P loss than CPa, IPa and RCr practices (RFo, GFi, FPa < RPa < CPa, IPa, RCr). RCr practices had the highest streambank soil losses among all other riparian practices (RFo, GFi, FPa < RPa < IPa, CPa < RCr). The study showed that riparian conservation practices (RFo, GFi and FPa) showcased significant benefits in mitigating streambank soil loss and associated soil total-P load to streams. However, their effectiveness is highly sensitive to changing climatic conditions and the extent of spatiotemporal variations. Full article

Vegetation cover can regulate regional climate and associated dry–wet variations. However, the effects of the quantitative structure and landscape pattern of vegetation cover on climatic drought remain unclear. Yunnan Province in China, with its abundant vegetation resources, provides a good setting for addressing this research gap. Our objective is to provide guiding recommendations for climate-warming mitigation through the study of the topic. This study adopted four periods of vegetation cover data, from 1992 to 2020, and explored their dynamics. Monthly average precipitation and temperature data from 125 meteorological stations in Yunnan were used to calculate standardized precipitation–evapotranspiration index (SPEI) for 1992–2020 to understand the responses of climatic drought to vegetation cover dynamics. The correlations between quantitative structure, landscape pattern, and climatic drought were investigated by Pearson’s correlation coefficient in 10 km, 20 km, 30 km, and 40 km grid cells, respectively. The results indicate that changes in the quantitative structure of vegetation could influence regional climates, with the contributions to climatic drought mitigation ranked in the following order: broad-leaved forest > shrubland > needle-leaved forest > cropland > grassland. Landscape patterns significantly affected local climates, where broad-leaved and needle-leaved forests had the strongest and most stable correlations with climatic drought, whereas shrubland and grassland showed weaker correlations. The correlations between landscape patterns and climatic drought were stronger during the dry season than the rainy season. Factors such as the landscape dominance index, fragmentation index, and aggregation index had a significant impact on climatic drought. The dominant and aggregated-distribution broad-leaved forests were conducive to climatic drought mitigation, while needle-leaved forests, croplands, and grasslands might exacerbate climatic drought. Full article

This study examines air quality in Polish health resorts (HR) and its potential health risks, challenging the belief that these environments always provide safe air for recovery. Over five years, air quality was evaluated by measuring concentrations of PM10 and benzo(a)pyrene (B(a)P) in seven resorts with varying environmental conditions. Using data from 3781 daily samples, both non-carcinogenic and carcinogenic risks were assessed for visitors (various age groups) and employees to determine health risks from prolonged exposure. The findings show frequent exceedances of national B(a)P limits, with some resorts, such as HR2 in Rabka-Zdrój and HR3 in Polanica-Zdrój, surpassing permissible levels by up to 320% and 373%, especially in winter. Non-carcinogenic risks exceeded safe limits by up to 40% for visitors in HR2 and 18% for employees in HR6 (Szczawno-Zdrój). Carcinogenic risks were up to 3.74 times higher than acceptable levels for visitors in HR2 and 3.15 times higher for employees in HR5 (Latoszyn), indicating a continuous risk from long-term exposure. These findings underscore the urgent need for measures to improve air quality in HRs. Given the global implications, similar risks could affect resorts worldwide. Therefore, future research should focus on cross-border studies and innovative strategies to manage pollution and protect health. Full article