Multi-spatial Scale Research Articles

Spatiotemporal dynamics and driving factors of energy-related carbon emissions in the Yangtze River Delta region based on nighttime light data

Owing to China’s massive area and vastly differing regional variations in the types and efficiency of energy, the spatiotemporal distributions of regional carbon emissions (CE) vary widely. Regional CE study is becoming more crucial for determining the future course of sustainable development worldwide. In this work, two types of nighttime light data were integrated to expand the study’s temporal coverage. On this basis, the distribution of energy-related CE in the Yangtze River Delta (YRD) region of China was estimated at a multispatial scale. Then the spatiotemporal dynamics of CE were explored based on the estimated results. The findings showed that the growth rate of CE in the YRD displayed three stages, and the total CE fluctuated upward. The spatial pattern of CE demonstrated a step-like decline from east to west. However, the Gini coefficient indicated that the differences in CE between cities gradually decreased since the CE had a strong spatial positive correlation in the YRD. Multiple factors affected the spatial variation of CE in the YRD, with economic level and population as the “critical” influencing elements, which determined the absolute amount of CE. This study provides a long-term analysis of CE dynamics while enhancing explanatory accuracy. The methods offer novel perspectives and tools for regional CE research. The results reveal spatial heterogeneity and clustering patterns of CE within regions, contributing valuable scientific evidence for monitoring regional CE and formulating emission reduction policies.

A Multi-Spatial Scale Ocean Sound Speed Prediction Method Based on Deep Learning

As sound speed is a fundamental parameter of ocean acoustic characteristics, its prediction is a central focus of underwater acoustics research. Traditional numerical and statistical forecasting methods often exhibit suboptimal performance under complex conditions, whereas deep learning approaches demonstrate promising results. However, these methodologies fall short in adequately addressing multi-spatial coupling effects and spatiotemporal weighting, particularly in scenarios characterized by limited data availability. To investigate the interactions across multiple spatial scales and to achieve accurate predictions, we propose the STA-ConvLSTM framework that integrates spatiotemporal attention mechanisms with convolutional long short-term memory neural networks (ConvLSTM). The core concept involves accounting for the coupling effects among various spatial scales while extracting temporal and spatial information from the data and assigning appropriate weights to different spatiotemporal entities. Furthermore, we introduce an interpolation method for ocean temperature and salinity data based on the KNN algorithm to enhance dataset resolution. Experimental results indicate that STA-ConvLSTM provides precise predictions of sound speed. Specifically, relative to the measured data, it achieved a root mean square error (RMSE) of approximately 0.57 m/s and a mean absolute error (MAE) of about 0.29 m/s. Additionally, when compared to single-dimensional spatial analysis, incorporating multi-spatial scale considerations yielded superior predictive performance.

Multi-granularity negotiation correction method for satellite digital twin model

Affected by uncertainty, there are errors between the digital twin model and the real system, which need to be corrected and the uncertainty of the model quantitatively evaluated as the basis for selecting the model when using it. However, the satellite digital twin model has the characteristics of multi-dynamic, multi-spatial scale, and multi-physical field coupling. The rigidity problem of differential equations and the multi-scale problem of partial differential equations will appear during numerical solution. If many telemetry parameters are corrected at the system level at the same time, the results will not converge. A multi-granularity negotiation correction framework for satellite digital twin models is proposed. The parameters are grouped by analyzing the correlation and frequency domain characteristics of telemetry parameters. Models of different granularities of satellite subsystems and components are established, and multi-granularity digital twin models are constructed on demand. The coupling relationship between different levels of satellite components is studied, and a negotiation correction method is proposed. The framework is verified using real on-orbit telemetry data. The research results show that the negotiation correction method proposed in this paper improves the RMSE accuracy by more than that of the unused one , and the correction results are more systematic and complete.

Revealing various change characteristics and drivers of ecological vulnerability in the mountains of southwest China

Ecological vulnerability (EV) assessment is an important means of reflecting the evolutionary process of the local ecological environment, which is crucial for ecological security. The current EV assessment still faces challenges in comprehensively revealing its spatio-temporal change characteristics due to diversity, multi-scale complexity and non-linearity of ecosystems. In this study, we comprehensively revealed the spatio-temporal change features and driver mechanisms of EV in Yunnan Province (YP) using methods such as breaks for additive seasonal and trend (BFAST) and Geodetector. According to the findings, (1) the ecological vulnerability index (EVI) in YP decreased by 0.0265 on the raster scale between 2000 and 2018, and the ecological vulnerability level (EVL) was mainly dominated by level III. At the city scale, EVL in YP was dominated by level IV. At the basin scale, the average EV of the Jinsha River basin was much greater other basins. (2) The linear trend of EV in the YP mainly showed an insignificant decrease, mostly concentrated in the YP’s eastern region. The non-linear trend mainly showed a monotonous decrease, with the mutation time concentrated in 2009. The future persistence trend of EV under different coupling modes was dominated by anti-persistence decrease (Sen-MK + Hurst) and anti-persistence monotonous decrease (BFAST + Hurst), with an area percentage of 40.19 % and 25.13 %, respectively. (3) Gross primary productivity was the priority factor influencing YP’s EV (q = 0.4137). This study not only enriches the cases of EV assessment studies in the high mountain valley area but also bridges the gap of analysing the multi-spatial scale characteristics and change trends of regional EV.

LI3D-BiLSTM: A Lightweight Inception-3D Networks with BiLSTM for Video Action Recognition

This paper proposes an improved video action recognition method, primarily consisting of three key components. Firstly, in the data preprocessing stage, we developed multi-temporal scale video frame extraction and multi-spatial scale video cropping techniques to enhance content information and standardize input formats. Secondly, we propose a lightweight Inception-3D networks (LI3D) network structure for spatio-temporal feature extraction and design a soft-association feature aggregation module to improve the recognition accuracy of key actions in videos. Lastly, we employ a bidirectional LSTM network to contextualize the feature sequences extracted by LI3D, enhancing the representation capability for temporal data. To improve the model’s robustness and generalization ability, we introduced spatial and temporal scale data augmentation techniques in the preprocessing stage, effectively extracting video key frames and capturing key regional actions. Furthermore, we conducted an in-depth study on spatio-temporal feature extraction methods for video data, effectively extracting spatial and temporal information through the LI3D network and transfer learning. Experimental results demonstrate that the proposed method achieves significant performance improvements in video action recognition tasks, providing new insights and approaches for research in related fields.

A multi-spatial scale analysis of anthropogenic nitrogen and phosphorus inputs in a large river basin: environmental effect and policy impact

A multi-spatial scale analysis of anthropogenic nitrogen and phosphorus inputs in a large river basin: environmental effect and policy impact

Multi-scale population analysis unit construction method considering scene feature variability and long/short-term patterns in spatiotemporal population activities

ABSTRACT Differences in the spatial scale and component elements of urban scenes affect the analysis of spatiotemporal changes in population distribution (PD) patterns. Fixed scales and geometric forms constrained previous analyses of PD and spatiotemporal changes, which neglected the realistic characteristics of urban scenarios. These limitations hinder the ability to capture the diversity and spatiotemporal representation of PD patterns across multiple spatial scales. This study developed a multi-spatial scale population analysis unit (PAU) construction method considering the scenario heterogeneity and long/short-term patterns of spatiotemporal population changes (PC). Initially, we decompose the multi-scale changes in population time series patterns and their relationships with scene features along the temporal dimension to analyze the primary and secondary factors. Starting from the temporal characteristics of the PD and PC, we established a validation and correction method for the factors. Finally, combined with a multi-feature clustering method, a multi-scale PAU construction method driven by scene feature factors is proposed. Experiments were conducted at different spatial scales in both routine and emergent scenarios. The results indicated that this method can help to obtain more homogeneous analysis regions and enhance the stability and phase pattern representations of changes in PDs, thereby improving the interpretability of analytical results.

Combined effects of river hydromorphological disturbances on macroinvertebrate communities: Multispatial scales analysis of central European rivers

Hydro-morphological threats impact the natural physical characteristics of river ecosystems, such as flow regimes, sediment transport, and channel morphology. These negative effects can occur at multiple scales, ranging from local microhabitats to geographic regions. Understanding these interactions can be useful for an integrated conservation approach and is needed for effective freshwater management. The aim of the study was to elucidate the combined effects of hydro-morphological threats on macroinvertebrates at three spatial scales: macroscale, including whole catchments, mesoscale (hydro-morphological changes in individual river sections) and the microscale, describing the microhabitat conditions of European rivers. The diversity and trophic structure of 1120 local macroinvertebrate communities in 28 catchments of various hydro-morphological disturbance levels, ranging from 0 to 2400 m asl, were analyzed. The response of macroinvertebrates to the main disturbance gradient differed between mountain and lowland communities. Random forest analysis indicated that the most important predictor of the ecological, diversity, and trophic indices was described by flow rate reduction. Generalized additive mixed models showed that decreased flow combined with river incision explained most of the variation in macroinvertebrate indices. Our results emphasize that based on multi-spatial scale analysis, hydro-morphological threats are very important factors in invertebrates biodiversity loss. Thus, to implement effective river management, we should pay more attention to the combined effects of geomorphological threats.

A multi-spatial scale assessment of land-use stress on water quality in headwater streams in the Platinum Belt, South Africa

River water quality is affected by various stressors (land-uses) operating at different hydrological spatial scales. Few studies have employed a multi-scaled analyses to differentiate effects of natural grasslands and woodlands, agriculture, impoundments, urban and mining stressors on headwater streams. Using a multi-scaled modeling approach, this study disentangled the distinct spatial signatures and mechanistic effects of specific stressors and topographic drivers on individual water quality parameters in tributaries of the Gwathle River Catchment in the Platinum Belt of South Africa. Water samples were collected on six occasions from 15 sites on three rivers over 12-months. Physio-chemical parameters as well as major anions, cations and metals were measured. Five key water quality parameters were identified using principal components analysis: sulfate, ammonium, copper, turbidity, and pH to characterise catchment water quality conditions. Using class-level composition (PLAND) and connectedness (COHESION) metrics together with topographic data, generalized linear mixed models were developed at multiple scales (sub-basin, cumulative catchment, riparian buffers) to identify the most parsimonious model with the dominant drivers of each water quality parameter. Ammonium concentrations were best explained by urban stress, Cu increased with mining and agriculture, turbidity increased with elevation heterogeneity, agriculture, urbanisation and fallow lands all at the sub-basin scale. River pH was positively predicted by slope heterogeneity, mining cover and impoundment connectivity at the catchment scale. Sulfate increased with mining and agriculture composition in the 100 m riparian buffer. Hierarchical cluster analysis of water quality and scale-dependent parsimonious drivers separated the river sites into three distinct groups distinguishing pristine, moderately impacted, and heavily mined sites. By demonstrating stressor- and scale-dependent water quality responses, this multi-scale nested modeling approach reveals the importance of developing adaptive, targeted management plans at hydrologically meaningful scales to sustain water quality amid intensifying land use.

Multi-spatial scale and multi-frequency resolution Proper Orthogonal Decomposition for patterns of wall-streamwise pressure gradient fluctuation of impinging jets

In the coating industry, seemingly ordered millimeter-scale W or X-like patterns inevitably appear on the coating wiped by jets. Considering the dynamic control of coating thickness by the maximum wall-streamwise pressure gradient on the impinging wall, exploring its fluctuation characteristics may provide insights into W or X-like patterns formation and suppression. Large Eddy Simulation is conducted to simulate jets impinging on a dry wall. The computed pressure gradient is processed by a two-dimensional Discrete Wavelet Transform using Coif2 wavelet, helping determine the spatial scale dominated by its fluctuation energy and providing snapshots on specific spatial scales. Afterward, snapshots on selected spatial scales undergo processing using either multi-scale Proper Orthogonal Decomposition (mPOD) or spectral POD, depending on the presence of some distinguishable peaks in its spectrum. This process can extract modes with spectral purity on the corresponding spatial scale, providing an advantage in addressing problems strongly linked to spatial scales. Applying this methodology, the study unveiled the evolution of the pressure gradient modes with frequency on the energy-dominant spatial scale, ranging from 0.625 mm × 0.785 mm to 1.25 mm × 1.57 mm. This evolution may be the cause of W or X-like patterns and suggests introducing a guiding jet to mitigate them.

PCCN-MSS: Parallel Convolutional Classification Network Combined Multi-Spatial Scale and Spectral Features for UAV-Borne Hyperspectral With High Spatial Resolution Imagery

PCCN-MSS: Parallel Convolutional Classification Network Combined Multi-Spatial Scale and Spectral Features for UAV-Borne Hyperspectral With High Spatial Resolution Imagery

The coupling relationship and driving mechanism between urbanization and ecosystem services in the Yellow River Basin from a multi-spatial scale perspective

Rapid urbanization has led to ecological destruction and associated issues. Macro policies wield substantial influence over urbanization and its relationship with the environment. Without considering the differences in scale, macro policies may be ineffective at addressing urbanization’s adverse impacts on the environment, and even worsen this relationship. We used data on 622 counties, 76 prefectures, and 7 urban agglomerations in the Yellow River Basin to examine the development level, coupling coordination degree, and spatial patterns of urbanization and ecosystem services at three scales during 2000–2020. Further, we explored the driving mechanisms in the relationship between urbanization and ecosystem services. We found that: First, the coupling coordination was relatively low but showed an upward trend. A sizeable spatiotemporal difference existed, with higher (lower) coordination in the east (west). Second, the coupling coordination of each scale exhibited significant spatial positive correlations. The low-value heterogeneous region was embedded around the agglomeration region, and polarization was significant. The larger the scale, the stronger the agglomeration effect. Further, the coupling coordination spatial agglomeration effect of each scale gradually weakened over time. Third, the spatial and temporal distributions of coupling coordination and its agglomeration characteristics at different scales differed. The urban agglomeration scale showed significant overall coordination or agglomeration characteristics, and prefecture and county regions showed local and unique characteristics within urban agglomerations. Fourth, the dominant factors influencing the spatial patterns of the coupling coordination at the county, prefecture, and urban agglomeration scales differed. The interaction and factor detection showed linear and double-factor enhancements. We find that economic development, government policies, environmental protection, and natural factors are the combined effects of urbanization and ecosystem services. Our research method can provide a reference for other river basins, and the results can help governments in formulating policies for sustainable development at different spatial scales.

Determinants of land use conflicts with the method of cross-wavelet analysis: Role of natural resources and human activities in spatial-temporal evolution

The imbalance and mismatch between land use structures and resource elements lead to the intensified competition for land use cover, and the resulting land use conflicts (LUCs) have attracted much attention. However, there are few studies on multi-spatial scale determinants of LUCs, due to relatively discrete scales or single determinant used in previous studies. It is a great challenge to explore the method of prioritizing research multi-scales in view of the determinants of LUCs. This study took the Shenyang Economic Zone, a developed urban agglomeration at high latitudes in China, and used cross-wavelet analysis to clarify the determinants of LUCs between natural resources and human activities in typical years (1980, 1993, 2006, and 2019). The cross-wavelet coefficients were used to extract the determinants of LUCs, and the cross-wavelet phase angles were applied to reveal the acting direction and spatial multi-scale correlation of LUCs with the determinants in the Shenyang Economic Zone. The results showed that: (1) the determinants of LUCs in the study area were distance to industrial and mining storage, intensity, soil types, digital elevation model (DEM), and landforms in 1980, were DEM and distance to industrial and mining storage in 1993, were distance to industrial and mining storage, intensity, and diversity in 2006, and were energy consumption of industrial enterprises, gross domestic product (GDP), and intensity in 2019. From 1980 to 2019, the dominant determinants influencing LUCs in the SEZ shifted from a joint influence of natural resources and human activities to dominance of human activities. (2) The action direction of LUCs with determinants had both positive and negative effects in different spatial positions. (3) The multi-scale correlations between LUCs and their determinants were obvious, and the spatial scale correlations varied significantly. The spatial scale between 94,629 and 118950 m was the most suitable spatial scale to explain the association between LUCs and determinants. The results indicated that the competition between natural resources would aggravate LUCs, however, human activities were the main cause of LUCs. Therefore, we believed that territorial spatial planning should not only consider the land distribution pattern, but also took the determinants as control variables to participate in territorial spatial planning. Our findings could provide a scientific basis for alleviating regional land contradictions and effectively allocating resource elements.

Multi-scale estimation of poverty rate using night-time light imagery

Explicit poverty data are critical for policymaking and targeting humanitarian aid. Poverty rate is the most widely accepted definition of a poverty status. However, poverty rate data are commonly available at country-level. Here, we proposed an approach that estimates the poverty rate at different spatial scales with a consistent standard within a country. We first trained the model based on household survey data and publicly available remote sensing data to derive a wealth index map, and then we developed the relationship between the wealth index and the poverty rate at country-level. The relationship finally was applied to estimate the multi-spatial scale poverty rates in the study area. We made validation between the estimated and statistical province-level poverty rates using relative error and R-Square. A case study was carried out in Mozambique. The results showed that the proposed model has a good ability to estimate poverty rate with an overall accuracy of 85.21%, as well as an R-Square of 0.94. There was a huge gap within Mozambique, with the Northern Provinces holding high poverty rates and the Southern Provinces holding low poverty rates. The district-level poverty rate map might reflect the negative impact of climate disasters, as well as the positive influence of economic and trade exchange. Given that the data we use are publicly available, the proposed methodology can be applied to other countries to estimate poverty rates at various spatial scales.

Effects of comprehensive landscape patterns on water quality and identification of key metrics thresholds causing its abrupt changes

Comprehensive landscape patterns influence water quality with multiple factors, complex processes, and scale dependence. However, studies identifying landscape thresholds causing abrupt water quality changes and characterizing the contribution of topography to water quality are still limited. Exploring the impact mechanisms of natural geographical and landscape characteristics on spatial and seasonal water quality variations is conducive to watershed water resource protection and ecosystem restoration. Based on water quality monitoring data of Minjiahe River in the typical headwater area of the upstream Dan River in China from 2019 to 2021, we employed redundancy analysis, partial redundancy analysis, and nonparametric change-point analysis to analyze the relationship between stream water quality and multi-spatial scale comprehensive landscape patterns, to obtain the interactive and independent contributions of different landscape categories at multi-spatial scales on water quality, and to find the key landscape threshold leading to abrupt changes in water quality. Results showed that landscape configuration, landscape composition, and topographic factors collectively explain over 89.1% of water quality variation. Most seasonal variations in water quality were primarily caused by landscape configuration. The landscape composition was mainly responsible for the differences in water quality variations among spatial scales. The topographic factors made the least independent contribution and had a potential impact on overall water quality variation. In order to protect the water quality of streams, it is more reasonable to regulate the landscape at different scales. At the sub-catchment scale, interspersion and juxtaposition index (IJI) and landscape shape index (LSI) should be controlled below 82% and 22. At the 100 m riparian scale, farmland, urban land, IJI, and LSI should be controlled below 29%, 6.5%, 92%, and 26, respectively. Our results provide important guidance for optimizing landscape patterns and water conservation in the watershed.

A planning scheme for energy storage power station based on multi-spatial scale model

With the continuous development of renewable energy, it has become important to make efficient use of renewable energy. However, the uncertainty and randomness of renewable energy can cause instability in the operation of power systems and can lead to the problem of abandoning PV power and wind power. To reduce the waste of renewable energy and increase the use of renewable energy, this paper proposes a provincial-city–county spatial scale energy storage configuration model based on the power supply and load situation of the power grid in recent years, which can better adapt to different scenarios. The objective function has been redesigned to take into account the differences in investment returns in different regions, with the objective of minimizing costs, making full use of energy storage devices to reduce the abandonment of renewable energy, achieving efficient use of renewable energy and ensuring system power balance. The proposed model further takes into account the difference in dispatching capacity of different regions and effectively reduces the occurrence of load cutting according to regional characteristics. Finally, the reasonableness of the model is verified by simulation with power grid data for a typical day in spring and a typical day in autumn in a region of China.

Buffer zone-based trace elements indicating the impact of human activities on karst urban groundwater

The tanglesome allocation of landscape types at various spatial dimensions is an important component influencing the quality of groundwater environment in karst cities. Trace elements can be used as indicators of the extent of impact on groundwater which is an effective means of tracing groundwater contamination. In this study, we studied the influence of landscape patterns on trace elements in groundwater of typical karst cities in Southwest China (Guiyang City) on a multi-spatial scale by using multivariate statistical analysis. According to the sampling points, buffer zone scales with different radii (500 m, 1000 m, 1500 m, and 4000 m) were established to quantify the land use model. There are suburban and urban differences in trace element content. The city center has higher levels of trace elements compared to suburban areas, especially Li, Ni, Tl, Cu, Sr, Co, As, and Mn. In addition, the outcomes of the multiple linear regression had shown that the size effect of the association from landscape pattern to trace elements varies with different indicators and parameters. The results of redundancy analysis showed an overall change in trace elements was better interpreted by the landscape pattern of the 1500 m-scale buffer. At the same time, at the 1500 m scale, Ni, Tl, Cu, Co, As, Cr, Sr, Li, and Mn were positively correlated with the urban landscape index (4LPI, 4LSI), influenced by urban anthropogenic activities, while Cd, Zn, and Pb were positively correlated with the cropland landscape index (1AI, 1LPI), influenced by agricultural activities. This study indicates that trace elements are a reliable indicator for tracing groundwater contamination. The buffer zone can reflect the extent of urban impacts on groundwater and provide a new and effective analytical tool for groundwater management.

A Scientometric Review of Residential Segregation Research: A CiteSpace-Based Visualization

Residential segregation (RS) is a global phenomenon that has become an enduring and important topic in international academic research. In this review, using RS as the search term, 2520 articles from the period 1928–2022 were retrieved from the Scopus database and were visually analyzed using CiteSpace software. The results revealed the following: (1) The United States and its institutions have made outstanding contributions to RS research, while various scholars (e.g., Johnston, Massey, Forrest, Poulsen, and Iceland) have laid the foundation for RS research. (2) Mainstream RS research originates from three fields—psychology, education, and social sciences—while the trend of multidisciplinary integration is constantly increasing. (3) The research hotspots of RS include racial difference, sociospatial behavior, income inequality, mixed income communities, guest worker minorities, typical district segregation, occupational segregation, health inequalities, metropolitan ghetto, and migrant–native differential mobility. Furthermore, (4) gentrification, spatial analysis, school segregation, health disparity, immigrant, and COVID-19 have become new themes and directions of RS research. Future research should pay more attention to the impact of multi-spatial scale changes on RS as well as propose theoretical explanations rooted in local contexts by integrating multidisciplinary theoretical knowledge.

Physical Urban Area Identification Based on Geographical Data and Quantitative Attribution of Identification Threshold: A Case Study in Chongqing Municipality, Southwestern China

Although some methods have identified the physical urban area to a certain extent, the driving factors for the identification threshold have not been studied deeply. In this paper, vector building data and road intersection data are used for comparative validation based on the urban expansion curve method to identify the physical urban area using the meso-city scale. The geographical detector technique is used to detect how and to what extent the urban spatial structure factors, geographical environment factors and social economic factors affect the optimal distance threshold of 22 administrative districts in the Chongqing municipality. The results based on the vector buildings are more precise and show the characteristics of the physical urban area of core-periphery distribution and the distribution along the water corridor. From the results of quantitative attribution, it was found that the road network density, building density, urbanization rate and urban population density, and their interaction with regional GDP, play a critical role in the optimal distance threshold, with the index value of influence degree ≥0.79. Under the influence of different factors, the optimal distance thresholds of 22 administrative districts show adaptive characteristics. Looking forward to the future, this study provides ideas for further research on the morphological characteristics and distribution laws of multi-spatial scale cities.

Linking land use with riverine water quality: A multi-spatial scale analysis relating to various riparian strips

Exploring linkages between riverine water quality and land use is of great importance for catchment management and water quality conservation. Relationships between them are complex and site-specific. Therefore, land-use/landscape effects on water quality remain to be investigated. Multivariate statistics and empirical models are used to examine the influences of seasonality (wet and dry seasons), land use in various riparian strips, on water quality seasonality in the river networks of the Three Gorges Reservoir Area. Redundancy analysis (RDA) shows a comparable effect of land use on overall water quality (OWQ) during the two seasons. The variance of OWQ explained is generally higher in the 50 m riparian zone, whilst, the variance is higher along 200 m riparian in the wet season with land use composition presented as actual area. The explanatory variables of land use indices for the OWQ are quite distinct. Multiple linear regression (MLR) modeling indicates that models of water quality parameters (WQPs) depend on seasonality, riparian zone and representation of land use indices (calculated from percentage and actual area), this results in predictor variables of WQPs are highly variable. Land use within the 50 m riparian zone can predict WQPs well, and land use along the riparian zones predicts WQPs better in the dry season. We firstly compared the impacts of multi-riparian land use indices (presented as percentage and actual area) in combination with land use configuration on water quality. Our study provides critical information for efficiently land-use planning to protect water quality.