Spatio-temporal evolution and influencing factors of synergizing the reduction of pollution and carbon emissions - Utilizing multi-source remote sensing data and GTWR model.

Grasping current circumstances and influencing components of the synergistic degree regarding reducing pollution and carbon has been recognized as a crucial part of China in response to the protection of the environment and climate mitigation. With the introduction of remote sensing night-time light, CO2 emissions at multi-scale have been estimated in this study. Accordingly, an upward trend of "CO2-PM2.5" synergistic reduction was discovered, which was indicated by an increase of 78.18% regarding the index constructed of 358 cities in China from 2014 to 2020. Additionally, it has been confirmed that the reduction in pollution and carbon emissions could coordinate with economic growth indirectly. Lastly, it has identified the spatial discrepancy of influencing factors and the results have emphasized the rebound effect of technological progress and industrial upgrades, whilst the development of clean energy can offset the increase in energy consumption thus contributing to the synergy of pollution and carbon reduction. Moreover, it has been highlighted that environmental background, industrial structure, and socio-economic characteristics of different cities should be considered comprehensively in order to better achieve the goals of "Beautiful China" and "Carbon Neutrality".

Dynamics of Hierarchical Urban Green Space Patches and Implications for Management Policy.

Accurately quantifying the variation of urban green space is the prerequisite for fully understanding its ecosystem services. However, knowledge about the spatiotemporal dynamics of urban green space is still insufficient due to multiple challenges that remain in mapping green spaces within heterogeneous urban environments. This paper uses the city of Hangzhou to demonstrate an analysis methodology that integrates sub-pixel mapping technology and landscape analysis to fully investigate the spatiotemporal pattern and variation of hierarchical urban green space patches. Firstly, multiple endmember spectral mixture analysis was applied to time series Landsat data to derive green space coverage at the sub-pixel level. Landscape metric analysis was then employed to characterize the variation pattern of urban green space patches. Results indicate that Hangzhou has experienced a significant loss of urban greenness, producing a more fragmented and isolated vegetation landscape. Additionally, a remarkable amelioration of urban greenness occurred in the city core from 2002 to 2013, characterized by the significant increase of small-sized green space patches. The green space network has been formed as a consequence of new urban greening strategies in Hangzhou. These strategies have greatly fragmented the built-up areas and enriched the diversity of the urban landscape. Gradient analysis further revealed a distinct pattern of urban green space landscape variation in the process of urbanization. By integrating both sub-pixel mapping technology and landscape analysis, our approach revealed the subtle variation of urban green space patches which are otherwise easy to overlook. Findings from this study will help us to refine our understanding of the evolution of heterogeneous urban environments.

The Relationship between Addictive Use of Short-Video Platforms and Marital Satisfaction in Older Chinese Couples: An Asymmetrical Dyadic Process.

Increasing evidence indicates that the addictive use of social media can have a detrimental effect on marital satisfaction, due mainly to the decrease in time and focus given to one's spouse. However, the impact of social media use among older couples remains under-investigated, and the research that does exist relies on individual-level data that do not allow the exploration of the dynamics between the dyadic partners. Therefore, the present study focused on older adults' use of short-video platforms, as these have been shown to be particularly addictive for older adults. A sample of 264 older couples was gathered (meanage = 68.02, SD = 8.68), and both spouses completed surveys reporting addictive use of short-video platforms, negative emotions, and marital satisfaction. Using an actor-partner interdependence model, we found an asymmetrical dyadic process in that the addictive use of short-video platforms by the wives was not only related to their own negative emotions, but also those of their spouse, as well as to decreased marital satisfaction. Meanwhile, addictive use by the husbands seemed to relate only to their own increased negative emotions, as well as to decreased marital satisfaction. Together, the findings from this study reveal dyadic dynamics with delineated pathways through which the addictive use of short-video platforms can damage older couples' interactive processes and marital satisfaction.

A neutron-star-driven X-ray flash associated with supernova SN 2006aj.

Supernovae connected with long-duration gamma-ray bursts (GRBs) are hyper-energetic explosions resulting from the collapse of very massive stars ( approximately 40 M\circ, where M\circ is the mass of the Sun) stripped of their outer hydrogen and helium envelopes. A very massive progenitor, collapsing to a black hole, was thought to be a requirement for the launch of a GRB. Here we report the results of modelling the spectra and light curve of SN 2006aj (ref. 9), which demonstrate that the supernova had a much smaller explosion energy and ejected much less mass than the other GRB-supernovae, suggesting that it was produced by a star whose initial mass was only approximately 20 M\circ. A star of this mass is expected to form a neutron star rather than a black hole when its core collapses. The smaller explosion energy of SN 2006aj is matched by the weakness and softness of GRB 060218 (an X-ray flash), and the weakness of the radio flux of the supernova. Our results indicate that the supernova-GRB connection extends to a much broader range of stellar masses than previously thought, possibly involving different physical mechanisms: a 'collapsar' (ref. 8) for the more massive stars collapsing to a black hole, and magnetic activity of the nascent neutron star for the less massive stars.

[Diagnosis study of rice leaf under phosphorus insufficiency based on spectral features of scan image and pattern recognition].

Insufficiency of phosphorus could greatly effect rice production, thus it is significant to adopt quick and nondestructive diagnosis of phosphorus content. The present paper focused on first expanded leaves with different phosphorus fertilization levels, comprehensively extracted 26 features' spectral information such as color, texture and shape etc. Single feature index analysis was conducted. Then features were collected to integrate CfsSubsetEval + Scattersearch method for optimizing, evaluation and choosing. Based on the feature selection for different leave positions, leaves in different phosphorus fertilization levels were finally classified into three grades (extremly insufficient, significant insufficient and normal) according to rough set theory. Results showed that the accuracy of recognition was very high while few phosphorus contained in the leaves. Moreover, the third expanded leaf is the best part for phosphorus-nutrient diagnosis.

Heavy metals in soil-vegetable system around E-waste site and the health risk assessment.

Investigating the farmland quality around electronic waste (E-waste) dismantling site and taking positive measures to ensure local food safety are urgent. Eleven types of vegetables (n = 184) and their corresponding soils were collected from vegetable fields in a city with famous historical e-waste activities in China. Nemerow integrated pollution indices analysis revealed that local vegetable fields suffered from heavy metal pollution to a certain extent, especially with regards to Cd, Cu, and Zn. The human health risk models provided by USEPA have been used to evaluate the non-carcinogenic and carcinogenic risks associated with the consumption of vegetables by local residents. Results indicated that both adults and children were suffering potential health risks. And the consumption of lettuce and sweet potato caused the greatest health risk, whereas cabbage and cowpea were relatively safe. The bioaccumulation factors (BAF) of heavy metals in various vegetables were calculated, and different vegetables showed huge variance in metal accumulation. Considering both contamination status and health risk assessment, cabbage and cowpea were selected as low accumulators of heavy metals. This study reveals the need for adjusting plantation structure and applying amendments to current protocols to alleviate the adverse effects caused by soil pollution.

Petrologic Characteristics and Chemical Structures of Macerals in a Suite of Thermally Altered Coals by Confocal Raman.

Thermally altered coals affect the safety and efficiency of coal mining and utilization, but most studies of thermally altered coal have only focused on the whole coal instead of on different macerals. Thermally altered coals have complex and special maceral components; not only intrinsic macerals but also newly formed macerals such as pyrolytic carbon can be observed. The shape and texture of intrinsic macerals also change significantly after thermal alteration, especially for vitrinite. Therefore, employing an in situ testing method to study the macerals in thermally altered coals is necessary. Herein, a confocal Raman imaging microscope was used. Results show that macerals become coked in samples adjacent to the sill, and circular mosaic texture is the most common texture observed in this series of samples. In samples away from the sill, inertinite is isotropic, while in samples closest to the sill, anisotropic inertinite can be found. The Raman spectra of inertinite and vitrinite (mosaic texture) are significantly different. For vitrinite, the D and G peaks are closer, and the height of D is lower than for inertinite. The Raman spectra of thermally altered coal include 13 bands after curve fitting. Curved-fitted results show that for vitrinite, polycondensation provides the nucleation of mesophase spheres, and newly formed aromatics take part in the growth of mesophase spheres. However, for inertinite, excessive amorphous carbon and substituents, such as aromatic alkyl and aryl-alkyl ether, form cross-linked structures and hinder the anisotropic development of inertinite.

An interpretable self-adaptive deep neural network for estimating daily spatially-continuous PM2.5 concentrations across China.

Accurate estimation of daily spatially-continuous PM2.5 (fine particulate matter) concentration is a prerequisite to address environmental public health issues, and satellite-based aerosol optical depth (AOD) products have been widely used to estimate PM2.5 concentrations using statistical-based or machine learning-based models. However, statistical-based models oversimplify the AOD-PM2.5 relationships, whereas complex machine learning technologies ignore the spatiotemporal heterogeneity of the predictors and demonstrate shortage in interpretation. Besides, large AOD data gaps resulting in PM2.5 estimation biases have been seldom imputed in previous studies, especially at national scales. To fill the above research gaps, this study attempts to present a feasible methodology to estimate daily spatially-continuous PM2.5 concentrations in China. The AOD data gaps across China were first imputed via a random forest (RF) model. Then, an interpretable self-adaptive deep neural network (SADNN) model, incorporating AOD, meteorological and other auxiliary predictors, was developed to estimate daily spatially-continuous PM2.5 concentrations from 2017 to 2018. Five-fold sample (site)-based cross-validation results showed a high accuracy of the SADNN model, with coefficient of determination and root mean square error values equal to 0.86 (0.84) and 13.07 (14.30) µg/m3, respectively, outperforming the standard DNN and the RF model. Furthermore, the SADNN model identified the spatiotemporal patterns of predictor importance, and demonstrated that the boundary layer height, elevation and AOD were the most important predictors both spatially and temporally. And the predictor importance in the Qinghai-Tibet Plateau was different from that in the rest of China. These results enhance our understanding of AOD-PM2.5 relationships and elucidate the estimated PM2.5 datasets with complete coverage are applicable for related air pollution studies and epidemiological cohort studies. Moreover, considering the effective nonlinear model capability and interpretability, the SADNN model is beneficial for not only PM2.5 estimation but also other earth data and scenarios.

[Leaf characteristics extraction of rice under potassium stress based on static scan and spectral segmentation technique].

The timing, convenient and reliable method of diagnosing and monitoring crop nutrition is the foundation of scientific fertilization management. However, this expectation cannot be fulfilled by traditional methods, which always need excessively work on sampling, detection and analysis and even exhibit lagging timing. In the present study, stable images for potassium-stressed leaf were acquired using stationary scanning, and object-oriented segmentation technique was adopted to produce image objects. Afterwards, nearest neighbor classifier integrated the spectral, shape and topologic information of image objects to precisely identify characteristics of potassium-stressed features. Diagnosing with image, the 3rd expanded leaves are superior to the 1st expanded leaves. In order to assess the result, 250 random samples and an error matrix were applied to undertake the accuracy assessment of identification. The results showed that the overall accuracy and kappa coefficient was 96.00% and 0.9453 respectively. The study offered an information extraction method for quantitative diagnosis of rice under potassium stress.

Evaluation of satellite rainfall products for modeling water yield over the source region of Blue Nile Basin.

Rainfall data is a vital input for many ecosystem service modeling in general and hydrological modeling in particular. However, accurate rainfall data with sufficient spatiotemporal distribution is inadequate in the Blue Nile Basin (Ribb watershed) due to uneven distribution of rain gauge networks. Advances in remote sensing science have provided alternative sources of rainfall data with high spatiotemporal resolution. But the accuracies of different satellite rainfall datasets are not uniform across space and time that need to be checked. The overarching objective of this study is to evaluate the performance of four satellite-based rainfall products [Tropical Applications of Meteorology using Satellite and ground-based observations (TAMSAT-v2.0 and v3.0), Climate Hazards Group InfraRed Precipitation with Station data version two (CHIRPS-v2.0), and Tropical Rainfall Measuring Mission version seven (TRMM-3B43 v7.0)] in the data-scarce region of the Blue Nile Basin in Ethiopia. The evaluation was carried out through direct comparison with the observed rainfall and through simulation of annual water yield using InVEST model for monthly, seasonal, and annual time scales. In general, the results show that the performance of satellite rainfall differs in time scale, topography, and method of evaluation. CHIRPS v2.0 rainfall product shows good performance both at monthly (R2 = 0.83) and annual (r = 0.85) time scales regardless of elevation. TRMM-3B43 v7.0 well performed over the mountainous area, which makes it the best rainfall data than other products at seasonal time scale (r = 0.86). CHIRPS v2.0 and TAMSAT v3.0 are equally applicable to that of gauged rainfall data for annual water yield simulation (Bias = 1.01 and 1.08 respectively). The findings of this study indicated the best performance of CHIRPS v2.0 and TAMSAT v3.0 satellite rainfall products, and hence, these products can be used for water management and decision-making process, particularly in the data-scarce watersheds.

The impact of climate changes on the water footprint of wheat and maize production in the Nile Delta, Egypt.

Spatial-temporal information of different water resources is essential to rationally manage, sustainably develop, and optimally utilize water. This study focused on simulating future water footprint (WF) of two agronomically important crops (i.e., wheat and maize) using deep neural networks (DNN) method in Nile delta. DNN model was calibrated and validated by using 2006-2014 and 2015-2017 datasets. Moreover, future data (2022-2040) were obtained from three Representative Concentration Pathways (RCP) 2.6, 4.5, and 8.5, and incorporated into DNN prediction set. The findings showed that determination-coefficient between historical-predicted crop evapotranspiration (ETc) varied from 0.92 to 0.97 for two crops. The yield prediction values of wheat-maize deviated within the ranges of -3.21% to 3.47% and -4.93% to 5.88%, respectively. Based on the ensemble of RCP, precipitation was forecasted to decease by 667.40% and 261.73% in winter and summer in western as compared to eastern, respectively, which will ultimately be dropped to 105.02% and 60.87%, respectively parallel to historical. Therefore, the substantial fluctuations in precipitation caused an obvious decrease in green WF of wheat (i.e., 24.96% and 37.44%) in western and eastern, respectively. Additionally, for maize, it induced a 103.93% decrease in western and an 8.96% increase in eastern. Furthermore, increasing ETc by 8.46% and 12.45% gave rise to substantially increasing (i.e., 8.96% and 17.21%) in western for wheat-maize compared to the east, respectively. Likewise, grey wheat-maize WF findings reveals that there was an increase of 3.07% and 5.02% in western as compared to -14.51% and 12.37% in eastern. Hence, our results highly recommend the optimal use of the eastern delta to save blue-water by 16.58% and 40.25% of total requirements for wheat-maize in contrast to others. Overall, the current research framework and results derived from the adopted methodology will help in optimal planning of future water under climate change in the agricultural sector.

Mapping the fine-scale spatial pattern of artificial light pollution at night in urban environments from the perspective of bird habitats.

The increase in artificial light at night (ALAN) is a global concern, while the pattern of ALAN pollution inside urban areas has not yet been fully explored. To fill this gap, we developed a novel method to map fine-scale ALAN pollution patterns in urban bird habitats using high spatial resolution ALAN satellite data. First, an ALAN pollution map was derived from JL1-3B satellite images. Then, the core habitat nodes (CHNs) representing the main habitats for urban birds to inhabit were identified from the land cover map, which was produced using Gaofen2 (GF2) data, and the high probability corridors (HPCs), indicating high connectivity paths, were derived from Circuitscape software. Finally, the ALAN patterns in the CHNs and HPCs were analysed, and the mismatch index was proposed to evaluate the trade-off between human activity ALAN demands and ALAN supply for the protection of urban birds. The results demonstrated that 115 woodland patches covering 4149.0 ha were selected as CHNs, and most of the CHNs were large urban parks or scenic spots located in the urban fringe. The 2923 modelled HPCs occupying 1179.2 ha were small remaining vegetation patches and vegetated corridors along the major transport arteries. The differences in the ALAN pollution patterns between CHNs and HPCs were mainly determined by the characteristics of the green space patches and the light source types. The polluted regions in the CHNs were clustered in a few regions that suffered from concentrated and intensive ALAN, while most of the CHNs remained unaffected. In contrast, the 727 HPCs were mainly polluted by street lighting was scattered and widely distributed, resulting a more varying influence to birds than that in the CHNs. Relating patterns of the ALAN to bird habitats and connectivity provides meaningful information for comprehensive planning to alleviate the disruptive effects of ALAN pollution.

[Detecting land use change using PCA-enhancement and multi-source classifier from SPOT images].

Concomitant with the rapid global urbanization process, land use change detection has been the focus and "hot spot" of global change research all the time. In the present study, the rigorous orthorectification was first applied to the SPOT-5 data to guarantee precise geometric correction and image registration. Afterwards, a methodology integrating PCA-enhancement and multi-source classifier was adopted to detect the land use changes in urban area. The results show that the first three PCs derived from multi-temporal-PCA contain most of the spectral information among which unchanged land use is highlighted in PC1 and PC2, and changed land use is mainly enhanced in PC3. The following multi-source classifier integrating unsupervised classifier (ISODATA) and supervised classifier (Maximum Likelihood) accurately extracts all the information. The findings from accuracy assessment demonstrate that the overall accuracy for the proposed method reaches 92.58, KAPPA coefficient is 0.92, and proving figures are also produced for the user's and producer's accuracies. It was further found that the proposed method yielded better accuracy than that of traditional post-classification comparison approach.

[Cotton identification and extraction using near infrared sensor and object-oriented spectral segmentation technique].

The real-time, effective and reliable method of identifying crop is the foundation of scientific management for crop in the precision agriculture. It is also one of the key techniques for the precision agriculture. However, this expectation cannot be fulfilled by the traditional pixel-based information extraction method with respect to complicated image processing and accurate objective identification. In the present study, visible-near infrared image of cotton was acquired using high-resolution sensor. Object-oriented segmentation technique was performed on the image to produce image objects and spatial/spectral features of cotton. Afterwards, nearest neighbor classifier integrated the spectral, shape and topologic information of image objects to precisely identify cotton according to various features. Finally, 300 random samples and an error matrix were applied to undertake the accuracy assessment of identification. Although errors and confusion exist, this method shows satisfying results with an overall accuracy of 96.33% and a KAPPA coefficient of 0.926 7, which can meet the demand of automatic management and decision-making in precision agriculture.

[Application of mutual information to variable selection in diagnosis of phosphorus nutrition in rice].

The present study obtained data of rice canopy spectrum, and P and chlorophyll content at typical growth stages with different rates of P supply by means of solution experiment. The effects of P treatments on leaf P and chlorophyll content were analyzed statistically using LSD's multiple comparison at a probability of 0.05; By mutual information (MI) variable selection procedure, the optimal spectral variables were identified at 536, 630, 1040, 551 and 656 nm, and their corresponding mutual information values were 1.0575, 1.1039, 1.135 3, 1.1417 and 1.1494 respectively; based on these sensitive bands, the built feed-forward artificial neural network model (ANN) had higher precision for P content estimation than the multiple linear regression model (MLR). Its RMSE of cross-validation and R were 0.038 8 and 0.9882, respectively, for the calibration data set, and the RMSE of prediction and R were 0.0505 and 0.9892, respectively, for the test data set. Therefore, it was suggested that MI was encouraged for quantitative prediction of leaf P content in rice with visible/near infrared hyperspectral information without assumption on the relationship between independent and dependent variables. But more work is needed to explain why these bands are sensitive to leaf P content in rice.

[Diagnoses of rice nitrogen status based on characteristics of scanning leaf].

In the present research, the scanner was adopted as the digital image sensor, and a new method to diagnose the status of rice based on image processing technology was established. The main results are as follows: (1) According to the analysis of relations between leaf percentage nitrogen contents and color parameter, the sensitive color parameters were abstracted as B, b, b/(r+g), b/r and b/g. The leaf position (vertical spatial variation) effects on leaf chlorophyll contents were investigated, and the third fully expanded leaf was selected as the diagnosis leaf. (2) Field ground data such as ASD were collected simultaneously. Then study on the relationships between scanned leaf color characteristics and hyperspectral was carried out. The results indicated that the diagnosis of nitrogen status based on the scanned color characteristic is able to partly reflect the hyperspectral properties. (3) The leaf color and shape features were intergrated and the model of diagnosing the status of rice was established with calculated at YIQ color system. The distinct accuracy of nitrogen status was as follows: N0: 74.9%; N1 : 52%; N2 : 84.7%; N3 : 75%. The preliminary study showed that the methodology has been proved successful in this study and provides the potential to monitor nitrogen status in a cost-effective and accurate way based on the scanned digital image. Although, some confusion exists, with rapidly increasing resolution of digital platform and development of digital image technology, it will be more convenient for larger farms that can afford to use mechanized systems for site-specific nutrient management. Moreover, deeper theory research and practice experiment should be needed in the future.

Estimating Dynamic Cellular Morphological Properties via the Combination of the RTCA System and a Hough-Transform-Based Algorithm.

The xCELLigence real-time cell analysis (RTCA) system has the potential to detect cellular proliferation, migration, cytotoxicity, adherence, and remodeling. Although the RTCA system is widely recognized as a noninvasive and efficient tool for real-time monitoring of cellular fate, it cannot describe detailed cell morphological parameters, such as length and intensity. Transforming growth factor beta(TGF-ß) induced the epithelial-mesenchymal transition (EMT), which produces significant changes in cellular morphology, so we used TGF-ß to treat A549 epithelial cells in this study. We compared it with lipopolysaccharide (LPS) and cigarette smoke extract (CSE) as stimulators. We developed an efficient algorithm to quantify the morphological cell changes. This algorithm is comprised of three major parts: image preprocessing, Hough transform (HT), and post-processing. We used the RTCA system to record the A549 cell index. Western blot was used to confirm the EMT. The RTCA system showed that different stimulators produce different cell index curves. The algorithm determined the lengths of the detected lines of cells, and the results were similar to the RTCA system in the TGF-ß group. The Western blot results show that TGF-ß changed the EMT markers, but the other stimulator remained unchanged. Optics-based computer vision techniques can supply the requisite information for the RTCA system based on good correspondence between the results.

Assessing and mapping cultural ecosystem services supply, demand and flow of farmlands in the Hangzhou metropolitan area, China.

Despite research on monitoring and mapping cultural ecosystem services (CESs) increasing exponentially in recent years, our knowledge of the CESs of farmlands is still inadequate, particularly in megacities. Analyzing the CESs of farmlands is a daunting challenge partly due to the lack of appropriate frameworks, and partly because of paucity of information on farmland. In this paper, the maximum entropy (Maxent) model along with agricultural big data were combined to measure and map CESs supply with respect to aesthetics and recreation, within farmlands in the Hangzhou metropolitan area of China between 2010 and 2016. We also quantified the characteristics of CESs based on demand and flow to enhance the understanding of the CESs of farmlands. The results indicated that the Maxent model was robust in mapping CESs supply. Moreover, the farmlands with high aesthetic supply were closely related to natural attributes and mainly distributed in rural areas. In contrast, the farmlands with high recreational supply were highly dependent on human factors which led to the rapid growth of such farmlands near the city center compared to aesthetic farmlands. Farmlands located along water bodies provided high integrated supply, while high demand was characteristic of strongly urbanized areas, and flow patterns of farmlands were greatly affected by popular scenic spots. The growth of CESs supply, demand and flow within farmlands might be closely related to the increase of agritourism, improved infrastructures, increasing income and leisure time of beneficiaries. This study highlighted the importance of the CESs of farmlands in the Hangzhou metropolitan area, although more research is needed to demonstrate the utility of the CESs of farmlands in urban environments.

Identifying the effects of land use change on sediment export: Integrating sediment source and sediment delivery in the Qiantang River Basin, China.

Dramatic land use change caused by the rapid economic development in China has impacted the sediment export dynamics in the large basin. However, how land use change affects sediment export is still poorly understood. This study provided an integrated analysis of the relationships in a "three-level" chain linked as follows: "land use change → changes in sediment source and sediment delivery → sediment export change" for a better understanding. It used the InVEST sediment delivery ratio (SDR) model to analyze the Qiantang River Basin (4.27 ∗ 104 km2), China. Sediment export change was examined from the two perspectives: the effects of land use change on sediment source and on sediment delivery. Correlations between changes in individual land use types and changes in sediment source and sediment delivery were identified. The results indicated that sediment export reduced from 1.69 t ha-1 yr-1 in 1990 to 1.22 t ha-1 yr-1 in 2015 because of the decreased sediment source and a weakened sediment delivery function. In the study area, the conversions of cropland to urban land (urbanization) and bare land to forestland (afforestation) were found to make the major contributions to reductions in soil loss and SDR, respectively. Furthermore, soil loss change resulted in the decreases in total value of sediment export and SDR change caused a large-scale spatial change in sediment export. Our hotspot analysis revealed that the Wuxi River watershed should be targeted for priority conservation to optimize land use/cover for reducing sediment export. This study demonstrates the benefits of taking a comprehensive approach to analyze the processes associated with sediment export change. These allow to improve sediment management and promote aquatic ecosystem health by providing specific future land use recommendations, aimed at source treatment and delivery interception.

Spatio-Temporal Trends and Identification of Correlated Variables with Water Quality for Drinking-Water Reservoirs.

It is widely accepted that characterizing the spatio-temporal trends of water quality parameters and identifying correlated variables with water quality are indispensable for the management and protection of water resources. In this study, cluster analysis was used to classify 56 typical drinking water reservoirs in Zhejiang Province into three groups representing different water quality levels, using data of four water quality parameters for the period 2006-2010. Then, the spatio-temporal trends in water quality were analyzed, assisted by geographic information systems (GIS) technology and statistical analysis. The results indicated that the water quality showed a trend of degradation from southwest to northeast, and the overall water quality level was exacerbated during the study period. Correlation analysis was used to evaluate the relationships between water quality parameters and ten independent variables grouped into four categories (land use, socio-economic factors, geographical features, and reservoir attributes). According to the correlation coefficients, land use and socio-economic indicators were identified as the most significant factors related to reservoir water quality. The results offer insights into the spatio-temporal variations of water quality parameters and factors impacting the water quality of drinking water reservoirs in Zhejiang Province, and they could assist managers in making effective strategies to better protect water resources.