Effects of land use configuration, seasonality and point source on water quality in a tropical watershed: A case study of the Johor River Basin

Disentangling the main factors influencing spring algal blooms in the Three Gorges Reservoir using partial least square structural equation modelling

Eutrophication and the associated algal blooms is a pervasive problem affecting global health, aquaculture, agriculture, water-related industries, and freshwater ecosystems. Spring algal blooms (SABs), which are less common than summer blooms, occur during a time that is thought to be less advantageous due to mild winds, little precipitation, and a relatively small amount of NPS pollutants being transported. Thus, It is pertinent to understand further the factors influencing SABs directly and/or indirectly for improved management. Consequently, Partial least square structural equation modelling (PLS-SEM) was employed to measure the direct and indirect effects of nutrients, lake hydrodynamics (Lake HD), meteorological elements (ME), stratification, Lake Bio-Optics, and Bottom Sediment-Water interaction (BSWI) in Gaoyang Lake of the Three Gorges Reservoir which is characterized by SABs. Based on our findings, total phosphorus (TP) and total nitrogen (TN) together slightly outperformed TP alone in explaining variations in chlorophyll a (chl-a), but the difference was not statistically significant. Thus the parsimonous PLS-SEM model with TP was chosen, and it explained 66.8%, 54.0%, 21.4%, and 59.7% variation in stratification, Lake Bio-Optics, nutrients, and chl-a, respectively. Surprisingly, ME and Lake Bio-Optics had a negative total effect on chl-a during the study. The magnitude of factors influencing SAB occurrence was of the order Lake Hydrodynamics > nutrients > Bottom Sediments-Water Interphase > Lake Bio-Optics > Meteorological Elements > Stratification. This study successfully decoupled and quantified several latent variables' complex simultaneous causal effects on chl-a.

Exploring spatial and seasonal water quality variations in Kelani River, Sri Lanka: a latent variable approach

Water quality degradation poses a significant challenge globally, especially in developing nations like Sri Lanka. Extensive monitoring programs designed to address escalating river pollution collect multiple water quality parameters over extended periods and varied locations. However, the sheer volume of data can be overwhelming, making it difficult to process effectively and interpret accurately using conventional methods. In this study, latent variable (LV) and unsupervised machine learning techniques were used to investigate spatial and seasonal variations of surface water quality for 17 parameters across 17 locations along the Kelani River, Sri Lanka, using monthly water quality parameters from 2016 to 2020. Pearson's correlation matrix identified 10 parameters significantly affecting water quality variations and factor analysis (FA) generated five LVs, accounting for 77% of the total variance in the dataset. The identified LVs showed multiple methods of river pollution. Hierarchical clustering analysis and self-organizing mapping methods clustered stations in a closely analogous manner. Stations near industrial zones and the river mouth showed higher water quality variance, often exceeding national guidelines. Correlation testing revealed strong relationships between water quality and catchment hydrometeorological variations during monsoonal seasons. Spatial analyses showed increased LV variance in the Lower Kelani River Basin, indicating higher pollutant levels in different seasons. Industrial effluents (LV-2 and LV-4) and domestic and municipal sewage (LV-3 and LV-5) exhibit greater seasonal fluctuations. The results showed that the proposed LV approach has the potential to assist authorities in addressing water pollution amidst the complexity of multiple water quality parameters.

Dams alter the control pattern of watershed land use to riverine nutrient distribution: Comparison of three major rivers under different hydropower development levels in Southwestern China

Land use plays a critical role in managing water quality in a watershed, as it governs the import and distribution of nutrients. In addition to the land use, some rivers in Southwest China are encountering a new environmental stressor of damming, which is being driven by the national strategy of hydropower development. However, the coupling effect of land use and dams on nutrients remains poorly understood, challenging the effective management of riverine water quality. Therefore, this study examined the nutrients in the Nu, Yarlung Tsangpo (YT), and Lancang (LC) Rivers, which have no dam, 1 dam, and 11 dams, respectively, during different regulatory periods (spring and fall) to identify variations in nutrient control patterns influenced by land use and dams. The findings suggested that an increase in hydropower development contributed to a notable shift in nutrient patterns from land use regulation towards dam regulation and coupling effects. Land use dominated the nutrient variations of the Nu (27.4 %-32.8 %) and low hydropower development YT (25.2 %-30.9 %) Rivers during both seasons, but the primary contributors to the nutrient variations of the high hydropower development LC River were dams (17.9 %-41.6 %) and coupling effects (16.5 %-29.0 %). Dams transform nutrient levels and compositions through internal reservoir cycling, decoupling land use and nutrients. Partial least-squares structural equation model analysis further suggested that the coupling effects of the LC River were seasonal-specific, which was primarily attributed to hydrological variations that affected their interactions. During spring, the reservoir underwent a drainage mode characterized by high-level nutrients in the bottom water. Combined with the import of riverine nutrients, it exacerbated the increase of nutrients (synergistic effect). In contrast, the reservoir transitioned into a storage mode where it intercepted nutrients from the upstream and watershed during the fall, leading to a reduction in the previously observed increasing trend and an increase in nutrient variability (antagonism effect).

Effects of landscape changes on water quality: A global meta-analysis

Landscape changes resulting from anthropogenic activities and climate changes severely impact surface water quality. A global perspective on understanding their relationship is a prerequisite for pursuing equity in water security and sustainable development. A sequent meta-analysis synthesizing 625 regional studies from 63 countries worldwide was conducted to analyze the impacts on water quality from changing landscape compositions in the catchment and explore the moderating factors and temporal evolution. Results exhibit that total nitrogen (TN), total phosphorus (TP), and chemical oxygen demand (COD) in water are mostly concerned and highly responsive to landscape changes. Expansion of urban lands fundamentally degraded worldwide water quality over the past 20 years, of which the arid areas tended to suffer more harsh deterioration. Increasing forest cover, particularly low-latitude forests, significantly decreased the risk of water pollution, especially biological and heavy metal contamination, suggesting the importance of forest restoration in global urbanization. The effect size of agricultural land changes on water quality was spatially scale-dependent, decreasing and then increasing with the buffer radius expanding. Wetland coverage positively correlated with organic matter in water typified by COD, and the correlation coefficient peaked in the boreal areas (r=0.82, p<0.01). Overall, the global impacts of landscape changes on water quality have been intensifying since the 1990s. Nevertheless, knowledge gaps still exist in developing areas, especially in Africa and South America, where the water quality is sensitive to landscape changes and is expected to experience dramatic shifts in foreseeable future development. Our study revealed the worldwide consistency and heterogeneity between regions, thus serving as a research roadmap to address the quality-induced global water scarcity under landscape changes and to direct the management of land and water.

Response of water quality in major tributaries to the difference of multi-scale landscape indicators in Dongting Lake basin, China

River water quality has been increasingly deteriorated because of the influence of natural process and anthropogenic activities. Quantifying the influence of landscape metrics, namely topography and land use pattern, which encompass land use composition and landscape configuration, across different spatial and seasonal scales that reflect natural process and anthropogenic activities, is highly beneficial for water quality protection. In this study, we focused on investigating the effects of topography, landscape configuration and land use composition on water quality at different spatial scales, including 1-km buffer and sub-watershed, and seasonal scales, including wet and dry season, based on the monthly water quality data in 2016 of Dongting Lake in China. Multivariate statistical analysis of redundancy analysis and partial redundancy analysis was used to quantify the contributions of these factors under different scales. Our results showed that among the three environmental groups, topography made the greatest pure contribution to water quality, accounting for 11.4 to 30.9% of the variation. This was followed by landscape configuration, which accounted for 9.4 to 23.0%, and land use composition, which accounted for 5.9 to 15.7%. More specifically, water body made the greatest contribution to the water quality variation during dry season at both spatial scales, contributing 16.6 to 17.2% of the variation. In contrast, edge density was the primary interpreter of the variability in water quality during wet season at both spatial scales, accounting for 9.9 to 11.1% of the variation. The spatial variability in the influence of landscape metrics on water quality was not markedly distinct. However, these metrics have a minimal impact difference on water quality at the buffer scale and the sub-watershed scale. Moreover, the contribution of landscape configuration varied the most from the buffer to sub-watershed scales, indicating its importance for the spatial scale difference in water quality. The findings of this study offer useful insights into enhancing water quality through improved handling of landscape metrics.

Effects of landscape pattern on water quality at multi-spatial scales in Wuding River Basin, China

Urbanization and agricultural land use have led to water quality deterioration. Studies have been conducted on the relationship between landscape patterns and river water quality; however, the Wuding River Basin (WDRB), which is a complex ecosystem structure, is facing resource problems in river basins. Thus, the multi-scale effects of landscape patterns on river water quality in the WDRB must be quantified. This study explored the spatial and seasonal effects of land use distribution on river water quality. Using the data of 22 samples and land use images from the WDRB for 2022, we quantitatively described the correlation between river water quality and land use at spatial and seasonal scales. Stepwise multiple linear regression (SMLR) and redundancy analyses (RDA) were used to quantitatively screen and compare the relationships between land use structure, landscape patterns, and water quality at different spatial scales. The results showed that the sub-watershed scale is the best spatial scale model that explains the relationship between land use and water quality. With the gradual narrowing of the spatial scale range, cultivated land, grassland, and construction land had strong water quality interpretation abilities. The influence of land use type on water quality parameter variables was more distinct in rainy season than in the dry season. Therefore, in the layout of watershed management, reasonably adjusting the proportion relationship of vegetation and artificial building land in the sub-basin scale and basin scope can realize the effective control of water quality optimization.

Multi-scale response relationship between water quality of rivers entering lakes from different pollution source areas and land use intensity: a case study of the three lakes in central Yunnan

As the main supply source of lakes, the water quality of the rivers entering the lakes directly determines the water safety and sustainable development of the lakes. Human activities are the direct cause of changes in the water quality of rivers entering lakes, and land use intensity is the direct manifestation of human activities on the land surface. Although significant progress has been made in studying the relationship between land use changes and water quality in lakes, there is still a lack of research on exploring the relationship between land use intensity and water quality at multiple scales, especially in comparative studies of different pollution source areas. To address this problem, this study used Pearson's correlation analysis and land use intensity index method to explore the response relationship between river water quality and land use intensity at different spatial and temporal scales and different pollution source areas using three lakes in central Yunnan as examples. The results showed that land use intensity was generally positively correlated with water quality, but the response relationship between land use intensity and different water quality indicators was significantly different at different scales and for different pollution source areas. Compared to non-urban areas, the impact of land use intensity on water quality is more significant in urban areas. Compared to the rainy season, the correlation between CODNa, TP, and NH3-N values and land use intensity is stronger during the dry season, while the correlation between COD, TN, and land use intensity is weaker during the dry season. When viewed at different scales, different water quality indicators have different scale effects, but overall, the larger the scale, the stronger the correlation. Therefore, in the work of lake water environmental governance, it is necessary to consider comprehensively from multiple scales and perspectives and adopt measures that are more suitable for regional water pollution prevention and control.

Unraveling heterogeneity of dissolved organic matter in highly connected natural water bodies at molecular level

The exploring of molecular-level heterogeneity of dissolved organic matter (DOM) in highly connected water bodies is of great importance for pollution tracing and lake management, and provides new perspectives on the transformations and fate of DOM in aquatic systems. However, the inherent homogeneity of DOM in connected water bodies poses challenges for its heterogeneity analysis. In this work, an innovative method combining fluorescence spectroscopy, high-resolution mass spectrometry (HRMS), and cluster analysis was developed to reveal the heterogeneity of DOM in highly connected water bodies at the molecular level. We detected 4538 molecules across 36 sampling sites in Chaohu Lake using HRMS. Cluster analysis based on excitation-emission matrix (EEM) data effectively divided the sampling sites into four clusters, representing the water bodies from West Chaohu Lake, East Chaohu Lake, agricultural land, and urban areas. Analysis of DOM in the western and eastern parts of the lake revealed that aerobic degradation led to a decrease in CHOS and aliphatic compounds, alongside an increase in CHO and highly unsaturated and phenolic compounds. Furthermore, we unveiled the characteristics and sources of heterogeneity in DOM from agricultural land and urban areas. Our method accurately captured the heterogeneous distribution of DOM in the lake and revealed the heterogeneous composition of DOM at molecular level. This work underscores the importance of integrating complementary spectroscopic analyses with HRMS in DOM research with similar compositions.

Pollution source identification and abatement for water quality sections in Huangshui River basin, China

Accurately obtaining the pollution sources and their contribution rates is the basis for refining watershed management. Although many source analysis methods have been proposed, a systematic framework for watershed management is still lacking, including the complete process of pollution source identification to control. We proposed a framework for identification and abatement of pollutants and applied in the Huangshui River Basin. A newer contaminant flux variation method based on a one-dimensional river water quality model was used to calculate the contribution of pollutants. The contributions of various factors to the over-standard parameters of water quality sections at different spatial and temporal scales were calculated. Based on the calculation results, corresponding pollution abatement projects were developed, and the effectiveness of the projects was evaluated through scenario simulation. Our results showed that the large scale livestock and poultry farms and sewage treatment plants were the largest sources of total nitrogen (TP) in Xiaoxia bridge section, with contribution rates of 46.02% and 36.74%, respectively. Additionally, the largest contribution sources of ammonia nitrogen (NH3-N) were sewage treatment plants (36.17%) and industrial sewage (26.33%). Three towns that contributed the most to TP were Lejiawan Town (14.4%), Ganhetan Town (7.3%) and Handong Hui Nationality town (6.6%), while NH3-N mainly from the Lejiawan Town (15.9%), Xinghai Road Sub-district (12.4%) and Mafang Sub-district (9.5%). Further analysis found that point sources in these towns were the main contributor to TP and NH3-N. Accordingly, we developed abatement projects for point sources. Scenario simulation indicated that the TP and NH3-N could be significantly improved by closing down and upgrading relevant sewage treatment plants and building facilities for large scale livestock and poultry farms. The framework adopted in this study can accurately identify pollution sources and evaluate the effectiveness of pollution abatement projects, which is conducive to the refined water environment management.

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.

Output characteristics and driving factors of non-point source nitrogen (N) and phosphorus (P) in the Three Gorges reservoir area (TGRA) based on migration process: 1995-2020

Although physical models at present have made important achievements in the assessment of non-point source pollution (NPSP), the requirement for large volumes of data and their accuracy limit their application. Therefore, constructing a scientific evaluation model of NPS nitrogen (N) and phosphorus (P) output is of great significance for the identification of N and P sources as well as pollution prevention and control in the basin. We considered runoff, leaching and landscape interception conditions, and constructed an input-migration-output (IMO) model based on the classic export coefficient model (ECM), and identified the main driving factors of NPSP using geographical detector (GD) in Three Gorges Reservoir area (TGRA). The results showed that, compared with the traditional export coefficient model, the prediction accuracy of the improved model for total nitrogen (TN) and total phosphorus (TP) increased by 15.46 % and 20.17 % respectively, and the error rates with the measured data were 9.43 % and 10.62 %. It was found that the total input volume of TN in the TGRA had declined from 58.16 × 10⁴ t to 48.37 × 10⁴ t, while the TP input volume increased from 2.76 × 10⁴ t to 4.11 × 10⁴ t, and then decreased to 4.01 × 10⁴ t. In addition Pengxi River, Huangjin River and the northern part of Qi River were high value areas of NPSP input and output, but the range of high value areas of migration factors has narrowed. Pig breeding, rural population and dry land area were the main driving factors of N and P export. The IMO model can effectively improve prediction accuracy, and has significant implications for the prevention and control of NPSP.

Determining the main contributing factors to nutrient concentration in rivers in arid northwest China using partial least squares structural equation modeling

Understanding the main driving factors of oasis river nutrients in arid areas is important to identify the sources of water pollution and protect water resources. Twenty-seven sub-watersheds were selected in the lower oasis irrigated agricultural reaches of the Kaidu River watershed in arid Northwest China, divided into the site, riparian, and catchment buffer zones. Data on four sets of explanatory variables (topographic, soil, meteorological elements, and land use types) were collected. The relationships between explanatory variables and response variables (total phosphorus, TP and total nitrogen, TN) were analyzed by redundancy analysis (RDA). Partial least squares structural equation modeling (PLS-SEM) was used to quantify the relationship between explanatory as well as response variables and fit the path relationship among factors. The results showed that there were significant differences in the TP and TN concentrations at each sampling point. The catchment buffer exhibited the best explanatory power of the relationship between explanatory and response variables based on PLS-SEM. The effects of various land use types, meteorological elements (ME), soil, and topography in the catchment buffer were responsible for 54.3% of TP changes and for 68.5% of TN changes. Land use types, ME and soil were the main factors driving TP and TN changes, accounting for 95.56% and 94.84% of the total effects, respectively. The study provides a reference for river nutrients management in arid oases with irrigated agriculture and a scientific and targeted basis to mitigate water pollution and eutrophication of rivers in arid lands.

Effects of land use and slope on water quality at multi-spatial scales: a case study of the Weihe River Basin

Exploring the impact of land use and slope on basin water quality can effectively contribute to the protection of the latter at the landscape level. This research concentrates on the Weihe River Basin (WRB). Water samples were collected from 40 sites within the WRB in April and October 2021. A quantitative analysis of the relationship between integrated landscape pattern (land use type, landscape configuration, slope) and basin water quality at the sub-basin, riparian zone, and river scales was conducted based on multiple linear regression analysis (MLR) and redundancy analysis (RDA). The correlation between water quality variables and land use was higher in the dry season than in the wet season. The riparian scale was the best spatial scale model to explain the relationship between land use and water quality. Agricultural and urban lands had a strong correlation with water quality, which was most affected by land use area and morphological indicators. In addition, the greater the area and aggregation of forest land and grassland, the better the water quality, while urban land presented larger areas with poorer water quality. The influence of steeper slopes on water quality was more remarkable than that of plains at the sub-basin scale, while the impact of flatter areas was greater at the riparian zone scale. The results indicated the importance of multiple time-space scales to reveal the complex relationship between land use and water quality. We suggest that watershed water quality management should focus on multi-scale landscape planning measures.

Spatiotemporal variations of river water turbidity in responding to rainstorm-streamflow processes and farming activities in a mountainous catchment, Lai Chi Wo, Hong Kong, China

River turbidity is an important factor in evaluating environmental water quality, and turbidity dynamics can reflect water sediment changes. During rainfall periods, specifically in mountainous areas, river turbidity varies dramatically, and knowledge of spatiotemporal turbidity variations in association with rainfall features and farming activities is valuable for soil erosion prevention and catchment management. However, due to the difficulties in collecting reliable field turbidity data during rainstorms at a fine temporal scale, our understanding of the features of turbidity variations in mountainous rivers is still vague. This study conducted field measurements of hydrological and environmental variables in a mountainous river, the Lai Chi Wo river, in Hong Kong, China. The study results revealed that variations of turbidity graphs during rainstorms closely match variations of streamflow hydrographs, and the occurrence of the turbidity peaks and water level peaks are almost at the same time. Moreover, the study disclosed that the increasing rates of the turbidity values are closely related to the rainfall intensity at temporal scales of 15 and 20 min, and the impact of farming activities on river turbidity changes is largely dependent on rainfall intensity. In the study area, when the rainfall intensity is larger than 35 mm/hr at a time interval of 15 min, the surface runoff over the farmland would result in higher river water turbidity downstream than that upstream. The study results would enrich our understanding of river water turbidity dynamics at minute scales and be valuable for further exploration of the river water environment in association with turbidity.

Spatiotemporal variation and driving factors of water quality in Yunnan-Guizhou plateau lakes, China

The Yunnan-Guizhou Plateau (YGP) lakes are the typical plateau rift lakes and an important water source in southwest China. However, there is a scarcity of research on its spatiotemporal water quality variations and driving factors, especially on long-term scales. Herein, multiple water quality indicators for 11 natural lakes on the YGP were measured from 2005 to 2020. In this study, the effects of natural lake attributes, human activities, and meteorological conditions on water quality were also analyzed. The results showed that the water quality of the YGP lakes tended to degrade, and was divided into heavy, medium, and light pollution types. Total phosphorus (TP), total nitrogen (TN), permanganate index (COD_Mn), and biochemical oxygen demand (BOD₅) increased by 14.69%, 14.44%, 22.61%, and 11.26%, respectively, from 2005 to 2020. Natural attributes of lakes and land use types were the main reasons for the spatial heterogeneity of water quality in YGP. In contrast, the temporal evolution of lake water quality was mainly related to human activities and climatic conditions. The smaller the water/ terrestrial area ratio, water storage capacity, and water depth, the easier the eutrophication and the worser the water quality of YGP lakes. Land degradation accelerated the deterioration of water quality in plateau lakes, while ecological land played an improving role. This study summarizes the water quality changes and influencing factors in YGP lakes over the past 15 years, which can provide a scientific database reference for water environment protection in YGP.

Temporal variations and trends prediction of water quality during 2010-2019 in the middle Yangtze River, China

Water quality plays an important role in river habitats. This study revealed the annual and seasonal variations and trend prediction of water quality in the middle Yangtze River after the third impoundment stage of the Three Gorges Reservoir. Multivariate statistical methods including principal component analysis/factor analysis (PCA/FA), Mann-Kendall (M-K) tests, discriminant analysis (DA), rescaled range (R/S) analysis, and the Canadian Council of Ministers of the Environment Water Quality Index (CCME-WQI) were used. Herein, eight water quality constituents including pH, electrical conductivity (EC), chloride (Cl), dissolved oxygen (DO), ammonia nitrogen (NH₃N), total phosphorus (TP), water temperature (T), and permanganate index (CODmn) were monthly monitored in the Jiujiang hydrological transaction during 2010-2019. The information of eight water quality constituents, related to salinity, nutrient status, and oxidation reactions efficiency, was extracted. Water quality status remained as fair-good during 2010-2019 based on the results of CCME-WQI, with the seasonal significance ranked as T > DO > Cl > pH > EC > TP > NH₃N > CODmn. In the future decade, annual average T was predicted to continue to increase although it might decrease in the wet season. EC was predicted to continue increasing annually especially in the wet season while Cl might decrease. NH₃N and TP might maintain a significant decreasing trend in the future wet and dry seasons. DO maintained significantly increasing especially in the future dry seasons, whereas CODmn will continue to decrease annually and seasonally. The continued alkalization trend of waterbody was predicted, which is more significant in the wet season. The results provide helpful references for the ecological protection of the middle Yangtze River.

Spatial distribution and sources of heavy metals in the sediment and soils of the Yancheng coastal ecosystem and associated ecological risks

Studies of heavy metal pollution are essential for the protection of coastal environments. In this study, positive matrix factorization (PMF) and a GeoDetector model were used to evaluate the sources of heavy metal contamination and associated ecological risks along the Yancheng Coastal Wetland. The distribution of heavy metals was shown to be greatly affected by clay content, except for Cr in shoal. Components from 6.5 to 9φ have the strongest ability to absorb heavy metals, where the effects of Cd and Zn sequestration in the wetlands were most apparent. The abilities of various wetland environments to sequester heavy metals were shown to be Spartina alterniflora wetland > woodland > Phragmites australis wetland > aquaculture pond > shoal > paddy > meadow > dry land. The sources of the heavy metals included parent soil material (59%), agriculture (15%), and industrial pollutants (26%). According to the single-factor pollution index, there was no evidence of pollution except Cr and Pb. In general, the heavy metal pollution was insignificant. The order of pollution loading index was shoal > paddy field > dry land > Spartina Alterniflora wetland > aquaculture ponds > woodland > meadow > Phragmites australis wetland. The ecological harm of heavy metal exposure was slight except for Cd and Hg, where vehicle emissions appeared to be the main cause of heavy metal pollution.

Spatiotemporal characterisation of microplastics in the coastal regions of Singapore

In the 21st century, plastic production continues to increase at an unprecedented rate, leading to the global issue of plastic pollution. In marine environments, a significant fraction of plastic litter are microplastics, which have a wide range of effects in marine ecosystems. Here, we examine the spatiotemporal distribution of microplastics along the Johor and Singapore Straits, at surface and at depth. Generally, more microplastics were recorded from the surface waters across both Straits. Fragments were the dominant microplastic type (70%), followed by film (25%) and fiber (5%). A total of seven colours of microplastics were identified, with clear microplastics as the most abundant (64.9%), followed by black (25.1%) and blue (5.5%). Microplastics under 500 μm in size accounted for 98.9%, followed by particles 500-1000 μm (1%) and 1-5 mm (0.1%). During the monsoon season, the abundance of microplastics across various sites were observed to be > 1.1 times when compared to the inter-monsoon period. Rainfall was a closely related to the increased microplastic abundance across various sites in the Singapore Strait. This suggests that weather variations during climate change can play critical roles in modulating microplastic availability. Beach sediments facing the Singapore Strait recorded an abundance of 13.1 particles/kg, with polypropylene fragments, polyethylene pellets and thermoplastic polyester foam identified via Fourier transform infrared spectroscopy. Hence, it is crucial to profile the spatiotemporal variation of microplastic abundance in both the surface and in the water column to gain a better understanding of the threat caused by microplastic pollution in the coastal regions of Singapore.

Spatiotemporal Variations in Physicochemical and Biological Properties of Surface Water Using Statistical Analyses in Vinh Long Province, Vietnam

In this study, spatiotemporal fluctuations in surface water quality in Vinh Long province, Vietnam, were conducted using entropy weighting, water quality index (WQI), and multivariate statistical techniques, such as cluster analysis (CA), principal component analysis (PCA), and discriminant analysis (DA). The samples collected at 63 monitoring locations in March, June, and September were measured for 15 parameters. Compared to the Vietnamese standard, surface water was contaminated with organic matters, nutrients, microorganisms, and salinity. DA identified the most typical parameters (pH, turbidity, TSS, EC, DO, Cl−, E. coli, coliform) in distinguishing temporal variations in water quality with greater than 75% of the correction. CA group 63 sampling sites into 22 clusters representing different land use patterns. WQI determined the worst water quality was found in the agricultural areas. Based on the results of entropy weighting, EC, coliform, N-NH4+, BOD, N-NO3−, and Fe had significantly controlled surface water quality. Four principal components obtained from PCA explained 66.45% of the variance, suggesting the influences of geohydrological factors and anthropogenic activities, such as domestic, market area, agriculture, and industry. The findings of this study can provide useful information for authorities to evaluate the effectiveness of monitoring systems and plan for water quality management strategies.

Effects of land use on the heavy metal pollution in mangrove sediments: Study on a whole island scale in Hainan, China

In recent decades, mangrove ecosystems at coastal zone are experiencing rapid land-use conversion, however effects of land use on the heavy metal pollution in mangrove sediments still are not clear. This study investigated the concentration and distribution of heavy metals (including chromium (Cr), zinc (Zn), lead (Pb), copper (Cu), arsenic (As) and cadmium (Cd)) in different mangrove sediments with different land-use patterns along seashore of the whole Hainan island (with the third largest mangrove area of China). The effects of land use on the accumulation of heavy metals in these mangrove sediments are also analyzed. The results showed contaminations of ∑6Metals in this study following the order of arable lands (ARAB) > aquaculture ponds (AQUA) > riverine area (RIVER) > ecological area (ECOL) > construction area (CONS). Accumulation degree of As and Cd were high in the AQUA, ARAB, and RIVER area. As metal hotspots, ARAB, RIVER and AQUA area showed the deteriorated sediment quality with high pollution load index (>1). Redundancy discriminate analysis revealed that mangrove, paddy lands and aquaculture ponds related activities correlated well with the metal pollution. The results clearly revealed that different land uses would not only change the accumulation capacity of mangrove soil for heavy metals, but also contribute different sources of heavy metal pollution. These findings do help to facilitate land-use planning and contribute to guide a better mangrove wetland management at coastal zone.

Classification of catchments for nitrogen using Artificial Neural Network Pattern Recognition and spatial data

In hydrological modelling, classification of catchments is a fundamental task for overcoming deficits in observational datasets. Most attention on this issue has focussed on identifying the catchments with similar hydrological responses for streamflow. Yet, effective methods for catchment classification are currently lacking in respect to Dissolved Inorganic Nitrogen (DIN), a water quality constituent that, at increasing concentrations, is threatening nutrient sensitive environments. Pattern recognition, using standard Artificial Neural Network algorithm is applied, as a novel approach to classify datasets that are considered to be suitable proxies for biological and anthropogenic drivers of observed DIN releases. Eleven gauged Great Barrier Reef (GBR) catchments within Queensland Australia are classified using spatial datasets extracted from ecosystem (e.g. original ecosystem responses to biogeographic, land zone, land form, and soil type attributes) and land use maps. To evaluate the performance of the examined spatial datasets as a proxy for deductive classification, the classification process is repeated inductively, using observed DIN and streamflow data from gauging stations. The ANN-PR method is seen to generate the same classification score format for the differing dataset types, and this facilitates a direct comparison for model output for observed data corroborations. The Kruskal-Wallis test for independence, at p > 0.05, identifies the deductive classification approach as a predictor for classification using DIN observations, which lacks an independence from each other at a p value of 0.01 and 0.02. This study concludes that an ANN-PR method can integrate the ecosystem and land use mapping data to deductively classify the GBR catchments into four regions that also have similar patterns of DIN concentrations. Due to the uniform availability of the mapping data, the findings provide a sound basis for further investigations into the transposing of knowledge from gauged catchments to ungauged areas.

Urban Forest Research in Malaysia: A Systematic Review

Sustainable urban development is a major issue in developing countries, namely in environmental and social aspects. Urban forests have the potential to address these issues. Thus, it is not surprising that urban forest research is slowly gaining traction in these regions. However, there have been limited urban forest research reviews focusing on developing countries, especially tropical countries in the global south. Research reviews are vital in identifying the distribution of research themes, hence revealing research gaps and needs. Therefore, this review paper aims to provide a deep insight into the development of urban forest research in Malaysia in the past 20 years. The core purpose of this review is to analyze the distribution of research themes in Malaysia, thus identifying research gaps and needs in developing countries. A total of 43 articles were selected for this review, using the PRISMA framework. The distribution of research articles showed a continuous increase over time, especially for the past five years (2016 to 2021). The reviewed articles were categorized according to five emerging research themes in urban forestry. More than 41% of the reviewed articles fell under Theme 1 (the physicality of urban forests), with the majority being on biodiversity (n = 10). Theme 5 (the governance of urban forest) had the lowest research output (n = 3). Urban forestry research is slowly gaining prominence globally including the global south; however, there are obvious preferences in research focus, causing some research questions to be neglected. These research gaps are especially evident in four areas—soil science, ecophysiology, valuation (economics), and environmental justice. These research gaps should be addressed by the scientific community to ensure a thorough and complete research growth pertaining to urban forestry.