Geochemical and Climatic Influences on Spatiotemporal Water Quality Changes in Drinking Water Source Lakes in Pakistan: Implications for Environmental and Public Health

Water resource management is becoming essential due to many anthropogenic and climatic factors resulting in dwindling water resources. Traditionally, geographic information systems (GIS) and remote sensing (RS) have long been instrumental in water resource assessment and management as the satellites or airborne units are periodically utilized to collect data from large areal extent. However, these platforms have limited computational capability and localized storage systems. Recently, these limitations have been overcome by the application of Google Earth Engine (GEE) that offers a faster and more reliable cloud-based GIS and remote sensing platform that leverages its parallel processing capabilities. Thereby, in recent years, GEE has witnessed rapid and accelerated adoption and usage in a wide variety of domains, including water resource monitoring, assessment and management. However, no systematic studies have been made to review the GEE application in water resource management. This review article is a maiden attempt towards developing an understanding of the functioning of GEE and its application in water resource assessment, covering both of its aspects viz (a) water quantity and (b) water quality. The review further attempts to illustrate its capabilities in real-world utility, through a case study conducted to analyze water quality and quantity of lake mead, a reservoir of Hoover Dam, Nevada (USA), at a monthly scale for a 3-year period spanning from 2021 to 2023. The results of this case study showcase the applicability of GEE to the water resource quantity and quality monitoring, assessment and management problems. The review further discusses the existing challenges with the application of GEE in water resource assessment and the scope for further improvement. In conclusion, after tackling the existing challenges with GEE, the application of GEE in water resources has huge potential for management planning of our water resources by addressing the forthcoming challenges.

The Watarase River, running through Japan’s northern Kanto region, has a long history of trace-metal contamination originating from the Ashio Copper Mine. Given the historical importance of incidents at this mine, understanding spatiotemporal environmental changes in the river, including changes in water quality, is important. By using long-term water-quality monitoring data (1960–2010), we aimed to reconstruct the spatiotemporal changes in six water-quality variables—the concentrations of three metals (copper, zinc, arsenic), biochemical oxygen demand (BOD), chemical oxygen demand (COD), and concentration of nitrate-nitrogen—along the Watarase River using generalized additive mixed models. The modeling results clearly demonstrate that during the 1960 and 1970s, metal pollution levels (as represented by copper and zinc) greatly decreased (from 450–2300 to 8–39 μg Cu L−1 and from 490–1500 to 17–52 μg Zn L−1), whereas organic pollution levels, as represented by the BOD and COD increased. Unique changes were observed in the cases of arsenic and nitrate-nitrogen (e.g., marked increases in the 1960s). From the 1980s until 2010, gradual decreases in the levels of metal and organic pollution were generally observed. Only in the 2000s were annual mean concentrations of copper in the lower reaches of the Watarase River lower than the US Environmental Protection Agency (EPA) water-quality criterion.

The Yihe River is an important river in Shandong Province, China. It is a catchment river for the South-to-North Water Diversion Project (SNWDP-ER), providing a variety of benefits and ecosystem services, such as flood and drought regulation, fishery and aquaculture, drinking water sources, and biodiversity conservation. In order to objectively reflect the status and changing trend of water environmental quality of the Yihe River, reduce the cost of detection, and improve the efficiency of water quality evaluation, samples were collected at 8 sampling sites in the 220km main stream of the Yihe River from 2009 to 2019. The spatiotemporal variations of 10 water quality indicators were analyzed, including pH, water temperature (WT), dissolved oxygen (DO), 5-day biochemical oxygen demand (BOD5), chemical oxygen demand (COD), total phosphorus (TP), ammonia nitrogen (NH3-N), nitrate (NO3-N), fluoride (F-), and sulphate (SO42-). The water quality index (WQI) was used to evaluate the spatiotemporal water quality changes, and the minimum WQI (WQImin) model consisting of five key indicators, i.e., NH3-N, BOD5, DO, SO42-, and WT, was built by using stepwise multiple linear regression analysis. The results indicated that the water quality indicators in the Yihe River showed significant spatiotemporal variations. With the exception of the COD and TP, the other water quality indicators conformed to the Class I or II standards of China, indicating that the water quality of the Yihe River was better than most natural water bodies. Seasonally, the WQI was better in the autumn and higher in the upstream area compared to the downstream. The water quality remained at the "good" level. The weighted WQImin model performed well in evaluating water quality, with coefficient of determination (R2), mean square error (MSE), and percentage error (PE) values of 0.903, 3.05, and 1.70%, respectively.

Establishing a method for characterizing spatiotemporal changes in the quality of the ecological environment in a timely and accurate manner is of great significance for the protection and sustainable development of the ecological environment in the Yellow River Basin (YRB). In this study, the Google Earth Engine (GEE) platform was used as a basis for constructing the remote-sensing-based ecological index (RSEI), and the RSEI was used to evaluate the quality of the ecological environment in the YRB. The results indicated that the mean of the RSEI values showed two stages of rapid improvement and slow improvement during 1990–2020. From 1990 to 2000, the average growth trend was 0.005/a with a growth rate of 21.15%, with the main contributions of bad to poor (101,800 km2), poor to medium (56,900 km2), and medium to good (70,800 km2) ecological environmental quality levels. From 2000 to 2020, the average growth trend was 0.002/a with a growth rate of 2.13%, with main contributions of poor to bad (65,100 km2) and good to medium (35,200 km2) ecological environmental quality levels. From 1990 to 2020, there was a 76.38% improvement in the ecological environmental quality of the entire YRB, in which significant improvement accounted for 26.14%. The reductions in the ecological environmental quality accounted for 23.62%, of which significant reductions accounted for just 1.46%. The improvement in the ecological environmental quality of the YRB showed a trend of increasing sustainability, which is expected to continue. The distribution of the ecological environmental quality in the YRB showed obvious regional aggregation, whereby cold spots were concentrated in the northern and central regions of the YRB, which are the sandy and hilly ravine areas of the Loess Plateau. However, the areas corresponding to hot spot clusters decreased with time, and their significance also decreased. Thus, our study demonstrates that the GEE platform can be used to determine the spatiotemporal changes in the ecological environmental quality of the YRB in a timely and accurate manner.

The water resource management is crucial to protect environment and ecological cycle. The detection of temporal and spatial changes in the lake's water extent is important for sustainable land planning. Therefore, the areal changes over the wetlands must be well monitored. Bafa Lake is an essential downstream water in the Büyük Menderes Basin which is the largest river basin of the Aegean Region. Google Earth Engine (GEE) is an easy-to-use online remote sensing data processing platform based on cloud computing. In this study, the long-term spatio-temporal changes of Bafa Lake between 1984-2022 have been analyzed using Landsat-5/8 satellite images on the GEE platform. A total of 1093 Landsat images were processed. The annual water areas were computed through composite images per year. The water area extraction was done using the normalized water difference index (NDWI). The minimum and maximum lake water areas in 38 years were detected as 5474 ha and 6789 ha in 1990 and 2006, respectively. In the accuracy assessment according to random sampling points, the Overall Accuracy (OA) was calculated as 98% and the kappa coefficient as 0.96. The water surface area was increased by 3.9% from 1984 to 2022. Between 2015-2022, the maximum increase or decrease in the lake area compared to the previous year observed as less than 1%. Therefore, there has not been a notable variation in the water area of Bafa Lake in the past few years.

A year-long study from May 2018 to April 2019 investigated the spatio-temporal changes in water quality of seven major rivers in the Giresun Province in northeastern Turkey. The results of the analysis were classified according to WHO limit values for drinking water and water quality classes were determined with respect to the Turkish Surface Water Quality Regulation (TSWQR). In addition, sodium percentage (% Na), sodium absorption ratio (SAR), magnesium hazard (MH), and residual sodium carbonate (RSC) values were calculated to evaluate the eligibility of the streams for irrigation purposes. Multivariate statistical analysis methods such as one-way ANOVA, Pearson correlation index (PCI), cluster analysis (CA), principal components analysis/factor analysis (PCA/FA) were used to determine the differences and similarities of streams, pollution sources and relations between parameters. As a result of the PCA, four factors accounted for 71.52% of all factors. These factors show that the major changes in water quality parameters were associated with point-based pollution such as wastewater from household and industrial resources, non-point sources such as agricultural activities and natural phenomena such as flooding, rock and soil erosion. The pH values in the range of 7.77 to 9.8 were slightly alkaline compared to the TSWQR range of pH 6–9. In certain months, the average NO2-N values (> 0.06 mg/L) in Batlama Stream, Aksu Stream, Yağlıdere Stream and Gelevera Stream was class III (medium) water quality and mean fluoride values in streams (1 and 1.19 mg/L) were class II water quality according to TSWQR. The levels of anionic surfactants in all streams were class III (polluted) water quality, except for Pazarsuyu Stream with class II (less polluted) water quality. The annual mean water quality index (WQI) values of the streams ranged from 25.69 (excellent) to 32.39 (good). When the water quality was evaluated for irrigation, the SAR, Na%, RSC and MH did not exceed the limit values. Based on these indices it was assessed that settlements and related anthropogenic activities along the riverbanks did not affect the water quality currently. This does not mean that it may not have an effect in the near future.

The Xiao Bei mainstream, located in the middle reaches of the Yellow River, plays a vital role in regulating the quality of river water. Our study leveraged 73 years of hydrological data (1951–2023) to investigate long-term runoff trends and seasonal variations in the Xiao Bei mainstream and its two key tributaries, the Wei and Fen Rivers. The results indicated a significant decline in runoff over time, with notable interannual fluctuations and an uneven distribution of runoff within the year. The Wei and Fen Rivers contributed 19.75% and 3.59% of the total runoff to the mainstream, respectively. Field monitoring was conducted at 11 locations along the investigated reach of Xiao Bei, assessing eight water quality parameters (temperature, pH, dissolved oxygen (DO), chemical oxygen demand (COD), ammonia nitrogen (NH3-N), total phosphorus (TP), permanganate index (CODMn), and 5-day biochemical oxygen demand (BOD5)). Our long-term results showed that the water quality of the Xiao Bei mainstream during the monitoring period was generally classified as Class III. Water quality parameters at the confluence points of the Wei and Fen Rivers with the Yellow River were higher compared with the mainstream. After these tributaries merged into the mainstream, local sections show increased concentrations, with the water quality parameters exhibiting spatial fluctuations. Considering the mass flux process of transmission of the quantity and quality of water, the annual NH3-N inputs from the Fen and Wei Rivers to the Yellow River accounted for 11.5% and 67.1%, respectively, and TP inputs accounted for 6.8% and 66.18%. These findings underscore the critical pollutant load from tributaries, highlighting the urgent need for effective pollution management strategies targeting these tributaries to improve the overall water quality of the Yellow River. This study sheds light on the spatiotemporal changes in runoff, water quality, and pollutant flux in the Xiao Bei mainstream and its tributaries, providing valuable insights to enhance the protection and management of the Yellow River’s water environment.

Rapid urbanization often exerts massive pressure on the resources relied upon by the ecological environment. It is necessary to quickly evaluate the interaction and mutual influence between regional urbanization and the ecological environment. This paper uses the Google Earth Engine (GEE) platform, integrates MODIS and night light remote sensing data sets, and computes the remote sensing-based ecological index (RSEI) and the coupling coordination degree (CCD) to measure the coupling coordination and analyze the spatiotemporal changes in the Chengdu–Chongqing Economic Circle (CCEC) for 2010, 2015, and 2020. Our results demonstrate four key findings. Firstly, the CCD varies spatially; it peaks at the Chengdu and the West Chongqing Plains, decreasing outwards along the mountains, with the lowest degree of coupling in the central, southern, and northern edge areas of the CCEC. Additionally, it has shown a trend of maintaining unchanged first and then increasing, mainly responding to policy decisions. Secondly, the changes between the different coupling levels were almost stable and mainly occurred between adjacent levels. Thirdly, the coupling level of towns spreads outwards from the centers at Chengdu and Chongqing and has an overall upward trend in time. Fourthly, in the most recent year, the coupling types present a distribution pattern of one developing axis connected with two peaks. Specifically, the environment system lagging type aggregates in Chengdu, Chongqing, and their surrounding areas, and the others mainly are economic system lagging type. The high internal coupling type also mainly occurs in the high and low coupling levels. Under this context, constructive suggestions for developmental optimization in the study area were proposed.

BackgroundThis study focuses on Dhaka City and its impact on urban surface water. Cities, ecosystems, and agriculture need surface water. It is crucial for water resource planning and environmental preservation. The primary aim is to study how urbanization has affected surface water in Dhaka City over 30 years using satellite imagery.MethodsThe study analyzed three decades of urban surface water shifts using Landsat 5 TM and Landsat 8 OLI/TIRS satellite imagery and Google Earth Engine (GEE) with JavaScript code for water ratio index detection. To investigate water level changes, field observation surveys and secondary data analysis were conducted. This integrated methodology simplified surface water data extraction and analysis, making remote sensing easier and allowing cloud-based satellite data processing.ResultsThe study demonstrates that the amount of surface water in cities is going down, from 36.23 km2 in 1990 to 5.83 km2 in 2021, which is an enormous decrease. This means that about 20 square kilometers, or 45 percent of the water's surface, have been lost in the last 30 years. The main reasons for the drop are unplanned expansion of cities, accelerated real estate development, and more trade and economic activities in the study area.ConclusionsThe GEE algorithms provide useful insights into surface water's maximum and minimum extent, enabling appropriate planning and management. These findings aid Dhaka City's water resource management and environmental protection.

The efficacy of land-use changes on aquatic ecosystems has been extensively studied in recent decades. Water resource management needs to understand the relationship between land-use change patterns and water quality, especially in urban areas. Hence, recognizing spatial-temporal changes in land use is required for sustainable development and proper water resource management. This research has developed an integrated model based on agent-based model (ABM) and multi-layer perceptron (MLP) neural network technique to predict the future land-use transformation tested on the North Ahvaz watershed, Iran. Random forest-supervised classification technique was applied to derive the land-use maps using Landsat 1989, 2004, and 2019 images in the Google Earth Engine (GEE) platform. The overall accuracy of classified land-use images was 0.82, 0.81, and 0.84, respectively, with the kappa coefficient of 0.74, 0.72, and 0.78. Land-use change analysis and generating transition potential maps were carried out in land change modeler (LCM) through MLP based on seven driving factors. Then, the land-use map for 2019 (for validation) and 2040 was simulated using the transition potential map and an agent-based approach. The ABM scenario was farmers' and urban landowners' decisions to convert undeveloped and unprotected lands to residential lands. The results showed that residential areas and pasture lands would grow by 67.96 km2 and 64.63 km2, and agricultural and barren lands would degrade about 84.19 km2 and 47.98 km2 during 2019-2040, respectively. Predicting land-use change through the integrated MLP-ABM model may be used to evaluate the effects of land-use change coming out of human decision-making.

Daily changes in household water access and quality in urban slums undermine global safe water monitoring programmes.

“十三五”时期，长江流域水环境质量改善明显，但湖泊水质和富营养化状况改善滞后. 长江中游作为我国淡水湖泊集中分布区域之一，部分湖泊存在水环境质量恶化和富营养化加重问题. 本文以长江中游区域国家开展监测的洪湖、斧头湖、梁子湖、大通湖、洞庭湖和鄱阳湖这6个典型湖泊为研究对象，科学评价其2016—2020年水质和富营养化时空变化特征及关键驱动因素，探讨其成因及治理对策. 结果表明，“十三五”时期长江中游湖泊水质和富营养化程度存在较大差异，与2016年相比，2020年大通湖水质改善最为明显，梁子湖水质变差，总磷是影响长江中游湖泊水质类别的主要因子; 洪湖富营养程度恶化最为严重，斧头湖次之，TLI(SD)对长江中游湖泊富营养化评价贡献最大. 目前长江中游湖泊呈有机污染加重和叶绿素a浓度升高现象，洪湖、斧头湖和梁子湖主要与氮、磷营养盐浓度升高有关，而大通湖、洞庭湖和鄱阳湖受水文过程、流域纳污量和湖泊管理等非营养盐因素影响较大. 总氮和总磷仍然是影响“十三五”时期长江中游湖泊水质和富营养化的最主要驱动力，且各湖泊总氮和总磷浓度变化均具有较强正相关性，建议开展河湖氮、磷标准衔接工作，提出河湖氮、磷标准限值或考核目标，以完善河湖水环境质量标准和生态健康影响评价技术规范. 同时，建议长江中游湖泊在开展截污控源、内源控制和生态修复的同时，进一步深化流域管理，特别是对洞庭湖、鄱阳湖、梁子湖和斧头湖等跨行政区湖泊，以提高湖泊治理与修复的系统性和整体性.;During the 13th Five-Year Plan period, the water ecological environment quality of the Yangtze River Basin has improved significantly, but the improvement of lake water quality and eutrophication has lagged behind. As one of the concentrated distribution regions of freshwater lakes in China, the middle reaches of the Yangtze River have the problems of deterioration of water quality and aggravation of eutrophication. This article chosed six typical lakes monitored by national agencies, including Lake Honghu, Lake Futou, Lake Liangzi, Lake Datong, Lake Dongting and Lake Poyang, to scientifically evaluate their spatiotemporal changes, key drivers of water quality and eutrophication from 2016 to 2020, and to discuss the degradation causes and governance countermeasures. The results revealed significant spatiotemporal changes in water quality and eutrophication of those lakes. From 2016 to 2020, the water quality of Datong Lake improved mostly, while Lake Liangzi became worse. TP is the main factor affecting the lake water quality and TLI(SD) is most important in the lake eutrophication evaluation. The eutrophication in Lake Honghu is the most serious, followed by Lake Futou. The lakes in the middle reaches of the Yangtze River widely experienced increasing organic pollution and chlorophyll-a concentration. Such degradation in Lake Honghu, Lake Futou and Lake Liangzi is mainly related to the increase of nitrogen and phosphorus nutrients, while Lake Datong, Lake Dongting and Lake Poyang are greatly affected by non-nutrient factors such as hydrological processes, pollution holding capacity and Lake management. TN and TP are still the main drivers on the water quality and eutrophication of these lakes during the 13th Five-Year Plan period, and change synergistically in each lake. We propose to put forward the standard limits or assessment targets of nitrogen and phosphorus in rivers and lakes, to improve their environmental quality standards of rivers and lakes and the technical specifications for ecological health impact assessment. At the same time, it is recommended that the lakes in the middle reaches of the Yangtze River should carry out pollution control, endogenous control and ecological restoration. It is important to strengthen watershed management, especially the lakes across administrative regions such as Lake Dongting, Lake Poyang, Lake Liangzi, and Lake Futou to improve the system and integrity of lake governance and restoration.

Quality of Delaware River water at Trenton, New Jersey

Ranau Lake is one of Indonesia's 30 national priority lakes facing pressures from climate change and human activities, negatively impacting its water quality and ecosystem. This study aims to analyze land cover changes in the catchment area, measure the changes in the lake's surface area, and examine the relationship between land cover changes and Ranau Lake's surface area from 2016–2022. The data includes Sentinel-1A IW GRDH imagery, ESRI land cover maps, and Google Earth images. The analysis employed Support Vector Machine (SVM) classification, spatial analysis, and linear regression. The results reveal that water bodies, vegetation, and built-up land categories experienced an increase of 36.78 hectares, 33.96 hectares, and 9.1 hectares, respectively, while bare land decreased by 80.03 hectares. Ranau Lake's surface area increased by 28.3 hectares, showing a significant relationship between land cover changes in water bodies (R² = 99.88%), bare land (R² = 94.92%), vegetation (R² = 66.06%), and built-up land (R² = 56.85%) and the lake's surface area. These findings highlight the critical role of land cover changes in influencing the dynamics of lake surface area, an essential indicator of ecosystem health. This study emphasizes the importance of sustainable land cover management in supporting Ranau Lake's conservation. Continuous use of SAR-based remote sensing technology is recommended for land cover monitoring, enabling data-driven decision-making in water resource management.

The Ecological Environment Quality (EEQ) is an important foundation for the sustainable development of society and economy. To assess the spatiotemporal changes of the EEQ in the Yangtze River Delta Urban Agglomeration (YRDUA), we selected MODIS images of 2001, 2006, 2011, 2016 and 2021 to construct the Modified Remote Sensing Ecological Index (MRSEI) based on Google Earth Engine (GEE) platform and Principal Component Analysis (PCA). Then, we evaluated the spatiotemporal changes and spatial autocorrelation of the EEQ in the YRDUA. The results showed that: the EEQ of the YRDUA was improved from 2001 to 2011, deteriorated from 2011 to 2016, and improved from 2016 to 2021. The overall EEQ of the YRDUA was at moderate or excellent level, and the EEQ in the south was better than that in the north. The EEQ of the southern cities in the study area was better and more stable, while that of the northern cities was relatively poor and changes relatively drastic. The EEQ of the YRDUA was mainly unchanged and improved from 2001 to 2021. The regions with improved EEQ were mainly distributed in the north and west, while those with deteriorated EEQ were mainly distributed in the east and south. The EEQ of the YRDUA was improved gradually from 2001 to 2006, and relatively stable from 2006 to 2011. From 2011 to 2016, the changes were drastic and the EEQ deteriorated greatly; while from 2016 to 2021, the EEQ of the YRDUA was improved, and the area of ecological deterioration was significantly reduced. From 2001 to 2021, the Globalmoran’s I value ranged from 0.838 ~ 0.918. In the past 20 years, NS area in the YRDUA accounted for the highest proportion, while the HH aggregation was mainly distributed in the southern part of the YRDUA, while LL aggregation was mainly distributed in the northern part, indicated that the EEQ in the southern part was better than that in the northern part. This study provides a promising approach to assess the spatiotemporal changes of EEQ in urban areas, which is crucial to formulate the ecosystem protection policies and sustainable development strategies of YRDUA.