Complex multivariate water quality impact assessment on Krivaja River
-
Radivoj Tomić
,
Višnja Mihajlović
Abstract
This study assesses the water quality of the upper course of the Krivaja River in northern Serbia, part of the Tisza and Danube catchments. The analysis focuses on a 2 km stretch with the highest pollution levels, using monitoring data from 2014 to 2017 and synthetic surface water pollution indicators. A total of 96 samples were collected at 6 different surface water sampling points, 16 per site. Wastewater were collected from 4 sampling points, 16 before its inflow into the Krivaja River and 32 as effluent. Surface water sampling where taken in accordance with SRPS EN ISO 5667-3:2007, and wastewater with SRPS ISO 5667-10: 2007 methods. Analyses of metals in water samples were carried out by atomic absorption spectroscopy, and nitrogen and phosphorus compounds were measured by UV spectrophotometry. Comparison with national standards revealed extremely high pollution levels: COD exceeded limits by 43 times, BOD5 by 76 times, total nitrogen by 23 times, and N-NH3 by 211 times. Nickel concentrations also surpassed the European Union Directive 2013/39/EU limits. The Nemerow index values (27.6–102) highlighted heavily polluted discharge sources, such as animal feed production. Cluster analysis confirmed these findings. Key pollutants include organic indicators (COD, BOD5, KMnO4), nutrients (NO2 −, NO3 −, TN, N-NH3), TSS, Cl−, and Fe. Metals (Mn, Zn, Ni, Pb, Cu, Cr, Cd, As) showed minor variations, minimally impacting the overall score. Poultry wastewater exhibited significant quality fluctuations, ranging from moderately to heavily polluted (PIW = 4.05–8.20). The primary cause of poor water quality is inadequate wastewater treatment in the region. Urgent measures are needed to mitigate pollution and restore the Krivaja River’s ecological integrity.
Graphical abstract
1 Introduction
Wastewater has the most impact on water quality, originating from industrial and agricultural activities, as well as urban wastewaters, causing different kinds of ecological and sanitary problems [1]. Wastewater in Serbia is often discharged into a recipient without adequate treatment or directly without any kind of treatment. Implementation of national and European legislation, particularly the Council Directive 96/61/EC (IPPC), has been neither systematic nor sufficient, and wastewater still presents a great environmental problem [2]. Pollution of surface water presents a significant threat to aquatic organisms, because of the accumulation effect, as well as acute and chronic toxicity, which leads to loss of biodiversity [3]. The water management approach is based on the concept of sustainable development and integral management, considering all natural and anthropogenic aspects and a suitable public approach. The development of the river management plan must include the analysis of pressures and impacts on river quality. Pollution pressure is a result of activities that can directly cause deterioration of water quality. In most cases, this negative pressure is a result of the addition or release of substances into the environment, mostly in the form of wastewater originating from different industries, agricultural facilities, and municipalities. The quality of the investigated water samples and sources of contamination will define a specific approach to river management for this area and will inform the selection of the best approach in the development of a wastewater treatment plant and its technologies [4].
Surface water in Vojvodina, the northern province of Serbia, is generally heavily polluted, including sediment matrix, and has been a subject of many research studies [4,5,6,7,8,9]. The monitoring program has been based on previous investigations at this or nearby locations [5,6,7,8,10], and on specific characteristics of the investigation region, with regard to the steady economic growth of the food industry with bad quality effluents, agricultural activities, flooding processes, and others [4]. Such highly polluted surface water is the Krivaja River, the longest river (109 km) that flows completely within the borders of Vojvodina (Figure 1). Over the years, high anthropogenic pressure has been present on the upper course of the Krivaja River, which is the subject of this study, with the discharge of effluents from urban and industrial sources [8]. The main industrial effluents originate from the food industries (Tables 1 and 2), but diffuse agricultural pollution is possible as well, since no protection exists between the surrounding agricultural land and the river itself [6].
The geographical location of the study area and sampling points.
Description of surface (S0–S5) and waste (W1–W4) water sampling sites
| Sites | Latitude | Longitude | Description of location |
|---|---|---|---|
| Surface water sampling sites (Krivaja River) | |||
| S0 | 45°49′36.14″ | 19°37′52.73″ | Upstream from Bačka Topola. Water with no wastewater discharge points |
| S1 | 45°48′24.24″ | 19°37′50.77″ | Downstream from Bačka Topola. No major polluters |
| S2 | 45°48′12.55″ | 19°37′44.63″ | Downstream from the urban wastewater discharge point as a major pollution source. Upstream from poultry |
| S3 | 45°47′52.27″ | 19°37′41.02″ | Downstream from the poultry wastewater discharge point as the main pollution source. Upstream from the meat industry |
| S4 | 45°47′37.48″ | 19°37′41.04″ | Downstream from the meat industry wastewater discharge point as the main pollution source. Upstream from the animal feed production plant |
| S5 | 45°47′36.43″ | 19°37′24.34″ | Downstream from the animal feed production plant wastewater discharge point as the main pollution source |
| Wastewater sampling sites | |||
| W1 | 45°48′18.01″ | 19°37′47.18″ | Descriptions of polluters are given in Table 2 |
| W2 | 45°47′55.34″ | 19°37′40.62″ | |
| W3 | 45°47′40.65″ | 19°37′41.85″ | |
| W4 | 45°47′33.02″ | 19°37′39.83″ | |
Short description of polluters in the investigated site with relevant quality parameters presented in the Integrated Pollution Prevention and Control Reference Document on Best Available Techniques in the Food, Drink and Milk Industries (BREF, 2006)
| Wastewater sources | Industry | Scope of industry | Short wastewater treatment description | Annual quantity of discharged wastewater | Pollutant of interest defined by FDM BREF |
|---|---|---|---|---|---|
| W1 | Public communal company | Collecting, treating, and distributing drinking and wastewater from households and small commercial buildings | Primary treatment: sedimentation and settling | 760,000 m3 | COD, BOD, TN, TP, TSS |
| W2 | Slaughterhouses and the meat industry (poultry) | Livestock (chicken) breeding, slaughtering, and production of meat products | Primary treatment of wastewater (lagoon) | 175,750 m3 | COD, BOD, TN, TP, TSS, Cl−, OF, |
| W3 | Slaughterhouses and the meat industry | Slaughtering large cattle and the production of meat products | Primary treatment of wastewater by a settling tank and an aerated lagoon | 87,250 m3 | COD, BOD, TN, TP, TSS, Cl− and OF |
| W4 | Animal feed production from animal by-products | Collecting and processing animal by-products and the production of animal feed | Primary treatment of wastewater with an aerated lagoon | 12,500 m3 | COD, BOD, TN, TP, TSS |
High anthropogenic pressure on rivers and streams represents a significant environmental problem globally [11,12,13,14,15,16,17,18,19,20,21], especially in highly populated countries like China, but no reference data were found regarding heavy (organic) pollution on a very short section (2 km) of water body with a relatively low flow rate. This makes this article a unique opportunity to evaluate these negative impacts using recommendations from similar studies [11,12,13,14,15,16,17,18,19,20,21], national regulations, and best available techniques [22,23].
The primary objective of this study was to evaluate the impact of four major pollution sources (Table 1) on the upper course of the Krivaja River between 2014 and 2017. This evaluation encompassed a comprehensive assessment of wastewater quality, employing an integrated approach to identify pollution sources, analyze the distribution of contaminants, and determine their environmental impacts. The study highlights the severe influence of urban wastewater, poultry processing, pig slaughterhouses, and animal feed production on the river’s ecosystem, with each source contributing distinct pollutant profiles.
By synthesizing data on key physicochemical and chemical parameters, the research provides a detailed overview of the current wastewater quality from food industry effluents and its adverse effects on the Krivaja River. The findings underscore significant challenges in wastewater management in the region, including inadequate treatment processes and high pollutant loads, particularly organic matter, nitrogen compounds, and suspended solids.
The results emphasize the urgent need for establishing an advanced wastewater treatment plant to mitigate pollution levels, improve river water quality, and support sustainable environmental practices. This study not only identifies critical pollution hotspots but also lays the groundwork for effective remediation strategies and long-term river basin management. In order to detect and classify sources of pollution, different quality indices were applied: river pollution index (RPI) adopted from Talalaj [24], landfill water pollution index (LWPI), water quality index (WQI) [11], and Nemerow index (see Supplementary material, Section 1). LWPI was initially developed to assess groundwater pollution near landfills but was adapted here for river water assessment and renamed RPI. In addition, the correlation between the key pollutants during the 4-year monitoring period was determined using multivariate statistical methods (principal component analysis [PCA]) to determine the level and genesis of the contaminants detected in the surface and wastewater samples. The obtained results are also compared with the relevant national regulations and recommendations from the Integrated Pollution Prevention and Control Reference Document on Best Available Techniques in the Food, Drink and Milk Industries (BREF, 2006) [23].
2 Methods
2.1 Description of study area
The main subject of this article is the Krivaja River located in the northern part of Serbia, Autonomous Province of Vojvodina (Figure 1). Krivaja River is the longest natural water body (109 km) with its whole course in Vojvodina. The flow of the Krivaja River is oriented from north to south, and it flows through several settlements. Due to the developed economy in this region (mostly the food industry), this river suffers from high loads of untreated effluents. The focus of this study is the upper course of the Krivaja River that belongs to the Danube River catchment, as shown in Figure 1. This section of the river (about 6 km) is selected based on high loads of pollution introduced through effluents originating from municipalities and the food industry in the region. The four major polluters presented in Table 2 are located within less than 2 km of this section (Figure 1). In return, this section of the Krivaja River is highly polluted, with poor organoleptic and visual properties (bad smell, algal bloom, coloration).
Aside from agricultural diffuse pollution (cultivation of various crop types; orchards and vineyards), the first major pollution point introduced to the Krivaja River is urban wastewater (W1, Table 2). Urban wastewater is collected from households and small commercial facilities in Bačka Topola. Bačka Topola is a small settlement with about 15,000 residents. According to the conceptual design for the future wastewater treatment plant in this area, the annual wastewater of Bačka Topola is calculated for 20,000 population equivalents (unit per capita loading). The annual average discharge of wastewater for the last 10 years is about 760,000 m3. Treatment of this effluent includes only primary treatment, sedimentation. Upon settling, effluent is discharged directly into the river basin. The data presented are based on several years of continuous monitoring.
Downstream the flow of the Krivaja River, the next major pollution point comes from poultry (W2, Table 2), which deals with the breeding and slaughtering of livestock (chicken) and processing and production of animal meat products. A total of about 175,750 m3 of wastewater is released into the Krivaja River per year. Before discharging wastewater is subjected to biotreatment. The third major polluter of the Krivaja River is also the meat industry (W3, Table 2). The main difference is the type of meat that is processed. Instead of the poultry industry, this meat industry includes pig slaughterhouses and the processing and production of pork meat products. Wastewater treatment is carried out by precipitation and aeration (lagoons). Precipitation is used for the removal of suspended solids, while aerobic treatment takes place in the lagoons, after which the treated water is released into the Krivaja River. The average annual quantity of released wastewater from this industry is about 87,250 m3.
The last major pollution in the investigated section of the Krivaja River is the animal feed industry (W4, Table 2). The scope of this industry includes the collection of animal by-products that are used as raw material for the production of animal feed. The treatment of wastewater is done by natural aeration and sedimentation in two sequentially positioned lagoons. About 12,500 m3, annually, of wastewater is discharged from this industry.
The water flow of Krivaja River has ranged from 1,901 during the dry season and up to 20,822 m3/day during the rainy season, with an average value of 10,087 m3/day. Based on wastewater flow from polluters (Table 1), it was determined that in total 1,790 m3/day of wastewater was discharged directly into the river. The key problem is the minimal flow of water in the Krivaja River when the water body consists mostly of wastewater.
2.2 Surface water sampling sites and sample collection
This study investigates physicochemical and chemical parameters at six different surface water (S0–S5, see Figure 1) sampling points presented in Table 1, in the monitoring period from 2014 to 2017. The last five sampling points (S1–S5) at the Krivaja River were selected according to major polluters present in this region. Sampling point S0 (Figure 1) is selected as a reference site in order to assess the natural quality of the Krivaja River, considering possible pollution originating from agricultural activities. Diffuse pollution from agricultural activities can possibly affect the impact assessment of other polluters, which was already documented in our previous research [25]. Sampling sites S1–S5 are located downstream from the wastewater discharge points, as shown in Figure 1.
Surface water samples from the Krivaja River were collected according to standard methods for surface water sampling SRPS EN ISO 5667-1:2008 and SRPS ISO 5667-6:1997 [26,27]. Samples were stored in 1.5 l plastic bottles and 1 l glass bottles for the analysis of general parameters, and 200 ml plastic bottles for the analysis of selected metals. Samples were kept at 4°C prior to transport to the laboratory and appropriately preserved until the moment of sample preparation. Conservation and manipulation of the samples were performed in agreement with the standard method SRPS EN ISO 5667-3:2007 [28].
A total of 96 surface water samples (S0–S5) were collected in order to assess the quality of Krivaja River during the monitoring period 2014–2017.
2.3 Wastewater sampling sites and sample collection
In order to assess the negative impact of major polluters in the investigated section of the Krivaja River, wastewater samples were collected from four sampling points (W1–W4) shown in Figure 1. These sampling points were selected according to the wastewater discharge points of four major polluters in the region (Table 2). Wastewater was sampled just before its inflow into the Krivaja River and after appropriate treatment. In order to assess the efficiency of wastewater treatment, influent (wastewater before treatment) is sampled. Wastewater samples were taken in accordance with the standard method SRPS ISO 5667-10:2007 [28]. Storage and handling of wastewater samples were the same as with surface water samples.
A total of 32 wastewater samples (effluent) in the monitoring period 2014–2017 were taken from four sampling points (W1–W4): after wastewater treatment at the end-of-pipe point, before inflow into the Krivaja River. Only 16 samples of influent (wastewater before treatment) were taken in the same period of time.
2.4 Physicochemical methods
Analyses of selected metals (Mn, Zn, Ni, Pb, Cu, Cr, Cd, As) in surface and wastewater samples (with acidification/fixation with cc HNO3 immediately after sampling) were carried by atomic absorption spectroscopy (PerkinElmer Analyst 700) according to the standard methods EPA 7010, EPA 7000b, and SRPS EN 1483:2008. The relative standard deviations (% RSD), based on triplicate measurements (n = 3), were below 10.0% for flame atomic absorption spectroscopy, 16.0% for graphite furnace atomic absorption spectroscopy, and 14.0% for cold vapor atomic absorption spectroscopy. These RSD values refer to the precision obtained during the analysis of all targeted metals in surface and wastewater samples, indicating acceptable analytical repeatability for each applied technique.
Conductivity, pH, and dissolved oxygen (DO) were measured at the sampling sites using portable instruments: WTW InoLab and Hanna model HI 933000. Nitrogen and phosphorus compounds were measured by standard methods listed in Table S1 (Supplementary material) on a UNICAM SP600 UV spectrophotometer at appropriate wavelengths and cells (quartz and glass). TOC contents were analyzed by ElementarLiquiTOCII, with oxidation by combustion at 850°C. pH measurements were carried out on a WTW InoLab portable instrument. Conductivity measurements were carried out on a Hanna model HI 933000. Chemical oxygen demand was determined according to the standard method SRPS ISO 6060:1994. Biological oxygen demand (BOD) was determined according to the manometric method (internal laboratory method: H1.002). Sulfates were determined by the standard method P-V-44/A, after precipitation of sulfate ions. Chlorides were determined by argentometric titration (SRPS ISO 9297:1997) [27].
Laboratory quality control methods included the use of standard operating procedures, calibration with standards, analysis of reagent blanks, recovery of spiked samples, and analysis of replicates. The method detection limits, practical quantitation levels, recoveries and precision, expressed as the relative standard deviation (RSD) of the analytical procedures for parameters measured in surface and wastewater samples, are detailed in Table S1 (Supplementary Material).
2.5 Data analysis
2.5.1 Surface water pollution indicators
In order to assess the quality of Krivaja River and the negative impacts of major polluters in the investigated area, the following surface water pollution indices were used: RPI, WQI [11], and Nemerow index (see Supplementary material, Section 1). Calculations of pollution indices were based on analyzed river quality parameters. Surface water quality parameters were chosen based on wastewater characterization (Tables 3 and 4), national legislation [22,29,30], local characteristics, and previous research [4,5,7]. For example, heavy metals are not the greatest concern regarding wastewater from major polluters in Bačka Topola, which are animal meat processing industries, but previous research indicates high concentrations of some heavy metals, dominantly in sediment [5,7].
Comparison of the physicochemical and chemical parameters investigated at all Krivaja river sampling sites (S0–S5) in the monitoring period 2014–2017, with relevant regulation values
| Parameter | Unit | Selected surface sampling points of Krivaja river | |||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| S0 | S1 | S2 | S3 | S4 | S5 | Regulation values (1) | |||||||||||||||
| Range | Mean | SD | Range | Mean | SD | Range | Mean | SD | Range | Mean | SD | Range | Mean | SD | Range | Mean | SD | Class II | Class III | ||
| pH | — | 7.43–8.03 | 7.84 | 0.24 | 7.58–8.05 | 7.88 | 0.18 | 7.56–7.95 | 7.73 | 0.15 | 7.70–8.12 | 7.91 | 0.17 | 7.64–8.01 | 7.77 | 0.15 | 7.58–8.03 | 7.83 | 0.17 | 6.5–8.5 | 6.5–8.5 |
| EC | µS/cm | 792–1,188 | 985 | 206 | 825–1,217 | 1,069 | 185 | 988–1,202 | 1,117 | 81 | 1,074–1,386 | 1,232 | 130 | 989–1,304 | 1,167 | 141 | 1,624–2,120 | 1,738 | 223 | 1,000 | 1,500 |
| DO | mg/l | 3.7–9.8 | 5.3 | 2.6 | 2.2–3.9 | 2.7 | 0.7 | 0.6–1.7 | 1.1 | 0.5 | 0.5–1.1 | 0.8 | 0.2 | 0.8–1.6 | 1.2 | 0.3 | 0.6–0.7 | 0.6 | 0.1 | 6 | 5 |
| TSS | mg/l | 8–84 | 39 | 32 | 21–92 | 61 | 31 | 111–174 | 143 | 24 | 64–143 | 103 | 35 | 60–95 | 80 | 13 | 135–179 | 161 | 18 | 25 | 35 |
| COD | mg/l | 23–63 | 37 | 20.35 | 80–138 | 105 | 25.98 | 398–511 | 461 | 44.77 | 554–657 | 606 | 38.62 | 223–318 | 285 | 36.95 | 1,275–1,358 | 1,312 | 32.71 | 15 | 30 |
| BOD5 | mg/l | 6–28 | 18 | 9 | 44–85 | 68 | 19 | 124–179 | 153 | 20 | 176–219 | 203 | 17 | 82–96 | 88 | 6 | 477–585 | 533 | 43 | 5 | 7 |
| TOC | mg/l | 8.6–20.8 | 17.4 | 5.0 | 15.9–33.7 | 23.6 | 6.6 | 29.3–44.7 | 38.7 | 5.8 | 47.8–63.4 | 53.7 | 6.2 | 27.4–43.8 | 36.1 | 6.2 | 52.8–78.2 | 66.6 | 11.7 | 6 | 15 |
| TN | mg/l | 3.7–13.4 | 8.6 | 4.7 | 11.5–18.4 | 15.6 | 2.9 | 23.7–29.4 | 27.0 | 2.8 | 29.9–38.5 | 33.8 | 3.9 | 30.5–38.9 | 34.1 | 3.5 | 160.8–211.0 | 184.0 | 20.0 | 2 | 8 |
| N-NH3 | mg/l | 0.04–10.51 | 3.72 | 3.67 | 4.32–10.78 | 8.32 | 2.74 | 14.83–26.17 | 18.84 | 4.59 | 25.71–29.79 | 27.32 | 1.54 | 21.05–31.45 | 28.36 | 4.29 | 107.6–146.1 | 126.94 | 16.81 | 0.4 | 0.6 |
| NO3 − | mg/l | 0.01–4.32 | 1.83 | 1.61 | 1.60–3.71 | 2.55 | 0.81 | 1.98–6.13 | 4.32 | 2.13 | 2.75–3.45 | 3.04 | 0.27 | 1.03–2.34 | 1.62 | 0.56 | 0.21–1.28 | 0.71 | 0.43 | 3 | 6 |
| NO2 − | mg/l | 0.01–0.24 | 0.09 | 0.08 | 0.04–0.32 | 0.18 | 0.11 | 0.57–0.85 | 0.69 | 0.10 | 0.39–0.61 | 0.50 | 0.09 | 0.02–0.29 | 0.17 | 0.11 | 0.05–0.11 | 0.09 | 0.02 | 0.03 | 0.12 |
| TP | mg/l | 0.22–0.93 | 0.46 | 0.28 | 0.44–1.41 | 1.02 | 0.09 | 1.02–1.88 | 1.37 | 0.38 | 1.19–1.72 | 1.45 | 0.20 | 1.24–1.62 | 1.44 | 0.16 | 1.35–2.02 | 1.70 | 0.27 | 0.3 | 0.4 |
| OP | mg/l | 0.004–0.93 | 0.25 | 0.24 | 0.92–1.14 | 0.98 | 0.20 | 0.82–1.19 | 0.95 | 0.16 | 1.18–1.64 | 1.41 | 0.18 | 0.87–1.19 | 1.04 | 0.13 | 1.24–1.91 | 1.59 | 0.28 | 0.2 | 0.2 |
| Cl− | mg/l | 53.2–84.9 | 65.3 | 12.6 | 64.7–96.2 | 74.1 | 12.9 | 52.8–211.4 | 148.8 | 59.2 | 56.1–120.9 | 83.2 | 26.2 | 72.2–118.8 | 98.6 | 20.4 | 67.4–92.7 | 78.8 | 12.3 | 100 | 150 |
| SO4 2− | mg/l | 50.1–109.7 | 89.6 | 23.8 | 80.7–128.3 | 102.9 | 19.1 | 87.9–158.1 | 143.0 | 33.7 | 122.2–165.4 | 137.8 | 17.9 | 78.5–153.4 | 118.2 | 32.1 | 102.4–157.3 | 126.2 | 24.3 | 100 | 200 |
| Fe | mg/l | 0.09–0.44 | 0.22 | 0.15 | 0.27–0.56 | 0.43 | 0.13 | 0.17–0.59 | 0.40 | 0.17 | 0.18–0.61 | 0.42 | 0.18 | 0.19–0.44 | 0.32 | 0.09 | 0.41–0.66 | 0.52 | 0.10 | 0.5 | 1 |
| Mn | µg/l | 49.5–180.8 | 128.5 | 65.8 | 57.4–143.1 | 94.9 | 35.8 | 39.5–121.9 | 80.3 | 36.4 | 38.5–141.6 | 91.7 | 37.7 | 27.5–91.2 | 46.9 | 26.9 | 15.1–25.8 | 18.7 | 4.1 | 100 | 300 |
| Ni | µg/l | 0.77–2.11 | 1.62 | 0.92 | 0.79–8.26 | 5.09 | 3.54 | 0.82–34.17 | 15.96 | 15.65 | 0.77–25.34 | 11.38 | 11.04 | 0.89–21.19 | 9.44 | 9.08 | 5.82–47.40 | 20.79 | 17.15 | 34(2) | 34(2) |
| Zn | µg/l | 0.01–0.07 | 0.04 | 0.03 | 0.01–0.09 | 0.06 | 0.04 | 0.03–0.13 | 0.08 | 0.04 | 0.02–0.06 | 0.04 | 0.02 | 0.09–0.48 | 0.22 | 0.16 | 0.15–0.22 | 0.18 | 0.03 | 0.3 | 2 |
| Cd | µg/l | 0.05–0.11 | 0.09 | 0.03 | 0.05–0.11 | 0.07 | 0.03 | 0.06–0.11 | 0.07 | 0.03 | 0.05–0.19 | 0.09 | 0.06 | 0.06–0.13 | 0.08 | 0.05 | 0.11–0.21 | 0.13 | 0.05 | 0.45 (2) | 0.6 (2) |
| Cr | µg/l | 0.64–1.58 | 0.92 | 0.56 | 1.38–7.24 | 4.45 | 2.55 | 7.65–21.41 | 13.92 | 5.90 | 3.35–15.60 | 11.13 | 4.78 | 1.40–8.14 | 4.42 | 2.49 | 3.81–18.76 | 11.03 | 6.06 | 50 | 100 |
| Cu | µg/l | 0.32–7.70 | 3.32 | 3.05 | 2.49–9.46 | 5.54 | 3.02 | 7.22–48.20 | 24.28 | 17.91 | 1.09–15.62 | 9.30 | 5.94 | 3.02–32.70 | 17.36 | 12.34 | 15.39–74.50 | 55.10 | 23.23 | 40 | 500 |
| Pb | µg/l | 2.06–3.97 | 2.83 | 1.04 | 2.08–4.02 | 2.85 | 1.13 | 2.11–4.05 | 2.83 | 1.04 | 2.06–4.11 | 2.86 | 1.08 | 2.10–4.01 | 2.96 | 1.11 | 3.97–11.98 | 8.46 | 3.08 | 14(2) | 14(2) |
| As | µg/l | 1.84–4.81 | 3.75 | 1.77 | 1.52–5.63 | 3.53 | 1.80 | 3.82–8.26 | 6.04 | 1.76 | 4.05–8.49 | 5.56 | 1.47 | 6.40–9.19 | 7.27 | 1.76 | 6.31–9.20 | 7.05 | 1.04 | 10 | 50 |
(1) Official Gazette of RS (50/2012).
(2) Official Gazette of RS (24/2014).
Comparison of the physicochemical and chemical parameters investigated at all wastewater (after treatment) discharge points (W1–W4) in the monitoring period 2014–2017, with relevant regulation values
| Parameter | Unit | Discharged wastewater sampling points | Regulation values (I) | |||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| W1 | W2 | W3 | W4 | Municipal wastewater | Meat industry | Carcass render industry | ||||||||||
| Range | Mean | SD | Range | Mean | SD | Range | Mean | SD | Range | Mean | SD | |||||
| pH | — | 7.10–7.85 | 7.50 | 0.25 | 6.20–8.06 | 7.28 | 0.97 | 7.23–8.33 | 7.59 | 0.50 | 7.03–7.60 | 7.44 | 0.27 | 6.9–9.0 | 6.9–9.0 | 6.9–9.0 |
| EC | µS/cm | 663–1,317 | 996 | 236 | 790–2,045 | 1,252 | 694 | 1,048–1,551 | 1,289 | 231 | 3,820–31,505 | 14,038 | 12,070 | — | — | — |
| OM | mg/l | 120–592 | 239 | 202 | 175–252 | 204 | 42 | 102–265 | 201 | 73 | 666–4,952 | 2,088 | 1,963 | — | — | — |
| TSS | mg/l | 40–1,042 | 368 | 294 | 45–202 | 98 | 90 | 21–144 | 105 | 58 | 1,187–3,245 | 2,196 | 851 | 60 | 35 | 35 |
| TDS | mg/l | 420–993 | 596 | 233 | 617–679 | 652 | 32 | 539–692 | 608 | 69 | 1,086–6,414 | 3,004 | 2,343 | 5,000 | — | — |
| COD | mg/l | 83–462 | 269 | 148 | 157–281 | 207 | 65 | 98–485 | 258 | 184 | 2,521–16,883 | 6,336 | 6,137 | 1,000 | 150 | 150 |
| BOD5 | mg/l | 31–204 | 114 | 72 | 22–960 | 342 | 335 | 44–189 | 117 | 62 | 998–4,667 | 2,415 | 1,666 | 500 | 25 | 25 |
| TOC | mg/l | 27.3–154.9 | 87.5 | 49.2 | 76.6–117.1 | 93.8 | 20.9 | 46.7–179.5 | 100.8 | 63.9 | 840.3–3823.7 | 1646.5 | 1454.8 | — | — | — |
| KMnO4 cons. | mg/l | 52.3–373.1 | 160.8 | 124.9 | 88.9–267.8 | 178.0 | 89.5 | 84.4–258.3 | 161.2 | 78.3 | 1375.2–3394.9 | 1925.0 | 981.1 | — | — | — |
| TN | mg/l | 34.1–138.8 | 63.7 | 42.7 | 82.8–233.0 | 167.3 | 76.8 | 63.5–114.0 | 85.9 | 21.5 | 707.1–4363.9 | 1803.5 | 1715.8 | 150 | 18 | 50 |
| TKN | mg/l | 33.9–137.2 | 63.3 | 42.0 | 82.7–232.5 | 167.2 | 76.7 | 63.4–113.8 | 85.8 | 21.5 | 707.0–3360.2 | 1552.5 | 1217.4 | 120 | — | — |
| N-NH3 | mg/l | 14.2–103.6 | 39.9 | 36.3 | 45.3–114.0 | 71.8 | 36.9 | 15.3–90.9 | 44.8 | 33.0 | 5.3–1862.1 | 733.8 | 731.9 | 100 | 10 | — |
| NO3 − | mg/l | 0.01–0.44 | 0.14 | 0.12 | 0.01–0.08 | 0.04 | 0.03 | 0.01–0.21 | 0.09 | 0.08 | 0.01–1.07 | 0.38 | 0.33 | — | — | — |
| NO2 − | mg/l | 0.001–0.557 | 0.212 | 0.195 | 0.004–0.076 | 0.024 | 0.017 | 0.004–0.049 | 0.015 | 0.013 | 0.004–0.811 | 0.219 | 0.204 | — | — | — |
| TP | mg/l | 1.52–5.71 | 2.75 | 1.72 | 6.16–6.59 | 6.32 | 0.25 | 1.81–2.45 | 2.03 | 0.30 | 2.77–7.60 | 5.18 | 2.65 | 20 | 2 | — |
| OP | mg/l | 0.75–5.23 | 2.18 | 1.85 | 3.03–6.27 | 4.91 | 1.68 | 1.64–2.41 | 1.97 | 0.33 | 2.63–7.40 | 3.98 | 2.29 | — | — | — |
| Cl− | mg/l | 45.1–52.9 | 48.8 | 3.1 | 50.3–64.7 | 56.7 | 7.6 | 41.7–122.3 | 82.5 | 40.8 | 51.6–203.4 | 120.8 | 63.1 | 30 | 0.4 | — |
| Fats and oils | mg/l | 2.1–109.0 | 37.3 | 44.0 | 30.5–90.5 | 58.3 | 30.7 | 20.2–189.8 | 93.4 | 72.8 | 93.5–197.5 | 144.3 | 45.6 | 50 | 20 | — |
| Fe | mg/l | 0.40–3.08 | 1.23 | 1.08 | 0.77–2.00 | 1.54 | 0.68 | 0.95–7.98 | 4.19 | 3.09 | 0.24–16.84 | 5.57 | 5.22 | 200 | — | — |
| Mn | mg/l | 0.05–0.21 | 0.12 | 0.06 | 0.17–0.32 | 0.23 | 0.08 | 2.03–7.11 | 4.89 | 2.26 | 0.58–22.51 | 9.08 | 8.86 | 5 | — | — |
| Ni | µg/l | 0.75–16.44 | 8.40 | 6.60 | 2.83–9.38 | 6.85 | 3.52 | 0.77–14.84 | 7.92 | 5.76 | 7.11–45.28 | 19.36 | 17.49 | 100 | — | — |
(1) Official Gazette of RS (67/2011, 48/2012, 1/2016).
2.5.1.1 RPI
RPI was used to quantify the impacts of four major polluters (Tables 1 and 2) on the quality of the Krivaja River. The calculation of RPI was performed using the equation for the calculation of LWPI proposed by Talalaj [24]. LWPI was originally used to estimate the landfill influence on water quality. That is why the new index is named RPI. For the calculation of the LWPI, the following equation was used:
where LWPI is the quality index for groundwater impacted by the landfill and n is the number of groundwater pollutants. In this study, the same equation is used to estimate the influence of wastewater on the Krivaja River. S i is calculated using the following equation:
where C p represents the concentration of the i-th parameter in each downstream surface water (river) sample and C b represents the concentration of the i-th parameter in the upstream surface water (river) sample (Table 3). For the pH, the S i should be calculated by placing it in the denominator of the lower pH value, as the ratio [24]:
Weight values (w i ) for selected parameters were derived from Pesce and Wunderlin [11] and are presented in Table S2 (see Supplementary material). Groundwater and surface water are different environmental media with different effects on human health and the environment, which require the usage of appropriate parameter (variable) weight values. The assigned weight value (w i ) for total organic carbon (TOC) is 3, because it represents the content of organic matter, as well as chemical oxygen demand (COD) and biochemical oxygen demand (BOD) which both have weight values 3 according to Pesce and Wunderlin [11]. Fe and Mn were given a weight value of 1, the same as the Mg weight value assigned by Pesce and Wunderlin [11]. Niwas was given a weight value of 3, because of its low natural occurrence (0.77–2.11 µg/l, Table 3) and toxic effect on human health and biota [31]. Total nitrogen (TN) was given weight values of 2 based on w i values for nitrates (NO3 −) and nitrites (NO2 −).
Classification of water quality based on RPI values is adopted from the original source and is given in Table S3 (see Supplementary material).
2.5.1.2 WQI
The WQI is a method of quantitation of water quality providing an integrated methodology for describing a designated level of cumulative water quality [32]. In the present study, each water quality parameter was assigned a weight on the basis of its assessed effect on primary health [11], followed by the concept of normalization. Selected water quality parameters and their weight values are presented in Table S2 (see Supplementary material). The WQI ranges from 0 to 100, with the highest values representing excellent water quality. Rating of WQI parameters (normalization) was performed according to [11,33,34,35]. Normalization factors for TOC, Fe, Mn, and Ni were derived from national standard values [29,30]. Water quality rating is presented in Table S4 (see Supplementary material). The WQI equation was established as follows [11]:
where n is the total number of water quality parameters, C i is the normalized value of parameter i, and P i is the weight value of parameter i.
Classification of final WQI scores is adopted from the literature [33] and is given in Table S5 (see Supplementary material).
2.5.1.3 Nemerow index
Urban wastewater (W1) characterization with Nemerow index included usage of the following parameters: pH, total suspended solids (TSS), total dissolved solids (TDS), COD, BOD5, TN, TKN, N-NH3, TP, Cl−, fats, Fe, Mn, and Ni. For characterization of wastewaters originating from slaughterhouses and meat processing industries (W2 and W3), fewer parameters were used: pH, TSS, COD, BOD5, TN, N-NH3, TP, Cl−, and oils and fats. Characterization of animal feed industry wastewater included the use of the least number of parameters: pH, TSS, COD, BOD5, and TN.
The efficiency of wastewater treatment was used to better understand wastewater quality on discharge points (W1–W4) and its effects on the Krivaja River quality.
The Nemerow index method is commonly used to describe water quality under some negative influence. The first time the Nemerow index was used to assess surface water quality was by Li et al. [36], and since then, it has been often used for that purpose [16,37,38,39]. At the beginning of the development, the Nemerow index had a simple form that is now referred to as the single-factor pollution index. During the course of time, a more sophisticated method for the calculation of the Nemerow index was developed [40]. The Nemerow index method is based on two main values: concentration of a specific parameter in a sample (C i ) and standard value of the same parameter (S i ). Wastewater quality, being governed by appropriate legislation, is also subjected to the quality assessment using the single-factor pollution and Nemerow index. The standard values used for calculation of single-factor pollution and Nemerow index, presented in Tables 3 and 4, were derived from national standards [22,29,30]. The grading method for Nemerow index, as well as the single-factor pollution index, is given in Table S6 (see Supplementary material). The single-factor pollution index method is formulated as follows:
where P i is the pollution index for the i-th parameter, C i is the monitoring value of the i-th parameter in each wastewater or surface water sample, and S i is the standard value for the i-th parameter.
The Nemerow index method is formulated as
where PI is the Nemerow index for the i-th pollutant, P iavg is the mean value of P i for all samples, and P imax is the maximum value of P i for all samples.
Selection of parameters was based on available standard values. Several parameters were used for calculation of P i and PI: pH, electrical conductivity (EC), DO, total suspended solids (TSS), chemical oxygen demand (COD), BOD, total organic carbon (TOC), TN, ammonium nitrogen (N-NH3), nitrates (NO3 −), nitrites (NO2 −), total phosphorus (TP), orthophosphates (OP), chlorides (Cl−), sulfates (SO4 2−), Fe, Mn, Ni, Zn, Cd, Cr, Cu, Pb, and As. Wastewater pollution indices were calculated using a specific set of parameters according to wastewater origin: urban wastewater (pH, TSS, TDS, COD, BOD5, TN, total Kjeldahl nitrogen [TKN], N-NH3, TP, Cl−, fats and oils, Fe, Mn, and Ni), meat industry (pH, TSS, COD, BOD5, TN, N-NH3, TP, Cl−, fats and oils), and animal by-products processing industry (pH, TSS, COD, BOD5, and TN).
2.5.2 Wastewater pollution indicator
The negative impact of wastewater discharged from four major pollutants (Table 2) investigated in this study was assessed primarily using RPI. Quality of wastewater was determined using previously described single-factor pollution and Nemerow indices using the dataset presented in Table 4.
The efficiency of wastewater treatment at all four major polluters was calculated to better understand the quality of wastewater discharged into the Krivaja River. Wastewater treatment efficiency was determined for two periods (2014–2015 and 2016–2017) using six parameters relevant for the industry and polluters presented in this study, including COD, BOD5, TSS, fats and oils, TN, and TP. A simple equation was used to calculate wastewater treatment efficiency: E(%) = ((C i – C e )/C i )·100, with E being the mean efficiency expressed as a percentage. C i represents the concentration in influent, while C e represents the concentration in wastewater after treatment (Supplementary material, Section 1, Table S4).
2.6 Multivariate statistical analyses
To systematically monitor the highly contaminated Krivaja River, a combination of statistical and empirical methods was applied using multiple variables. Inorganic and organic parameters were measured at six river sampling sites (S0–S5), generating 96 samples between 2014 and 2017, and compared with 32 samples from nearby wastewater sources (W1–W4). The selection of the most important organic and inorganic representative variables was made using data from the S (33) and W (34) sampling locations (67 in total). PCA was used for variable reduction and systematization, helping to distinguish sources of pollution – whether anthropogenic (e.g., industry, agriculture), geogenic (e.g., lithology), or due to chemical processes like complexation. Hierarchical agglomerative cluster analysis (CA) using Ward’s method with squared Euclidean distances was performed to evaluate the similarities and relationships among sampling sites. This approach highlighted both temporal and spatial variations in water quality and revealed specific linkages between sites. Additionally, analysis of variance (ANOVA) was used to determine significant spatial and temporal variations (p < 0.05). The integrated use of PCA, CA, and ANOVA, supported by expert selection of input variables, provided a robust assessment of the factors influencing the quality of river and wastewater in the Krivaja River system. All these statistical methods are conducted in STATISTICA (Statsoft Inc., USA; version 13.2). This approach can reveal specific linkages between sampling sites, as it indicates similarities or dissimilarities between their specific parameters [41,42,43]. The quality of waste and river water was used to identify the potential influencing factors that explain changes in quality parameters of the Krivaja River.
3 Results
3.1 Monitoring results of physicochemical and chemical parameters
Based on monitoring data from S0 to S5 sites and comparisons with national legislation values [29] and European Union Directive 2013/39/EU [44], several conclusions and recommendations can be stated. The general water quality parameters from Table 3 largely exceed the limit values from national legislation, classifying all sampling sites with moderate ecological status. This kind of water can be used as drinking water with prior treatment by coagulation, flocculation, filtration, and disinfection, and also for bathing and recreation, irrigation, industrial use (process and cooling water). From the aspects of mean concentration of heavy metals and As, this water body has a good status, but from the point of maximum values, a moderate ecological status could be assigned too. Since S5 acts as a sink for pollution (Figure 1), several parameters exhibit significantly higher mean values. For example, COD is approximately 43 times, BOD5 is around 76, TN is around 23, and N-NH3 is around 211 times higher than prescribed values. Compared to the European Union Directive value of 4 μg/l for Ni, values of this metal at S2–S5 site points were several times higher. All these imply emerging actions for the revitalization of the Krivaja River body.
Comparison of the physicochemical and chemical parameters investigated at all wastewater discharge points (W1–W4) in the monitoring period 2014–2017, with relevant national regulation [22] values (Table 4), resulted in significant multiple values of concern. Several parameters, TSS, COD, BOD5, and TN, are the parameters that should have much lower values. For example, the average concentration of several key parameters from animal feed industry wastewater discharge (W4 from Tables 1 and 4) has BOD5, TSS, COD, TN, round 96, 62, 42, and 36 times higher, respectively. This is in accordance with the previous conclusion of S5 as a sink for water pollution and W4 as a water discharge key point. Also, for slaughterhouses and the meat industry W2 and W3 points (Figure 1 and Table 4), the phenomenon of multiple impacts on the water quality is present from aspects of TSS, COD, BOD5, TN, N-NH3, TP, Cl−, and fats and oil values. Urban wastewater also has a similar trend of pollution, but somewhat lesser impact.
3.2 Synthetic water quality indicators
To complement the physico-chemical characterization of water samples, four synthetic water quality indicators were applied in order to assess the pollution status and environmental risk associated with discharges into the Krivaja River. The WQI, Nemerow Index for surface water (PIS), RPI, and Nemerow Index for wastewater (PIW) provide an integrated view of pollution severity at both river and discharge sites. These indicators help to condense complex datasets into simple numerical values or qualitative classes, allowing for spatial and temporal comparisons while facilitating the identification of critical pollution hotspots.
3.2.1 WQI
WQI values calculated for surface water sampling sites ranged from 32.7 to 86.9 (Table 5), reflecting considerable variation in water quality conditions along the Krivaja River. According to the classification by Banerjee and Srivastava (2009), these results correspond to water quality classes from “excellent” to “very poor.” The lowest WQI values were consistently recorded at S3 and S4, located downstream of major discharge points, indicating intensified contamination by untreated or insufficiently treated effluents. In contrast, the upstream site S0 generally exhibited better quality throughout the study period, indicating minimal anthropogenic influence. The WQI trends highlight a progressive deterioration in water quality along the river flow path, with seasonal fluctuations further modulating the classification thresholds.
Obtained yearly values for selected water quality/pollution indices
| Sites | WQI | Single-factor pollution index (Pi) | Nemerow index (PI) | |||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2014 | 2015 | 2016 | 2017 | SD | RSD | 2014 | 2015 | 2016 | 2017 | SD | RSD | 2014 | 2015 | 2016 | 2017 | SD | RSD | |
| S0 | 61.3 | 53.9 | 62.3 | 64.2 | 73.8 | 59.1 | 1.23 | 2.04 | 1.66 | 1.25 | 2.40 | 158 | 0.98 | 0.99 | 1.12 | 1.13 | 1.70 | 160 |
| S1 | 55.2 | 47.1 | 55.9 | 51.0 | 72.2 | 80.7 | 2.58 | 2.59 | 2.87 | 3.26 | 11.3 | 169 | 2.63 | 2.61 | 2.80 | 2.84 | 7.40 | 186 |
| S2 | 43.9 | 40.3 | 41.0 | 40.0 | 80.2 | 93.9 | 7.40 | 7.97 | 6.70 | 6.93 | 12.2 | 168 | 4.41 | 4.30 | 4.35 | 4.39 | 7.70 | 187 |
| S3 | 43.2 | 40.6 | 39.7 | 39.0 | 80.5 | 95.9 | 8.77 | 9.30 | 8.62 | 9.22 | 16.7 | 186 | 5.26 | 5.15 | 5.30 | 5.31 | 10.9 | 209 |
| S4 | 45.2 | 43.2 | 43.5 | 43.5 | 81.7 | 90.1 | 6.27 | 6.14 | 6.29 | 5.15 | 14.5 | 243 | 3.79 | 3.68 | 3.75 | 3.76 | 10.0 | 191 |
| S5 | 28.4 | 31.6 | 33.7 | 27.7 | 77.3 | 103 | 30.1 | 26.8 | 24.6 | 26.5 | 68.9 | 255 | 17.2 | 17.0 | 15.6 | 16.9 | 46.7 | 285 |
3.2.2 Nemerow index for surface water (PIS)
The Nemerow Pollution Index was calculated for each surface water location based on the single-factor pollution index approach. The results, presented in Table 5, indicate that PIS values exceeded the threshold of 1.0 at several locations and sampling times, suggesting moderate to severe pollution levels. The highest PIS scores were observed at S3 and S4, in line with WQI trends and proximity to industrial and municipal discharges. The index integrates both average and maximum pollution levels per parameter, which helps reveal locations where specific pollutants (e.g., COD, Ni, or Pb) disproportionately elevate overall risk. In contrast, values at reference site S0 remained consistently below the pollution threshold, supporting its role as a baseline station. The use of PIS thus confirmed spatial patterns of contamination and supported prioritization of impacted segments.
3.2.3 RPI
The RPI values for the surface water sites, calculated using four core indicators (DO, BOD5, NH3-N, and SS), are shown in Table 6. This index, adapted from Talalaj (2014), allowed for the classification of sampling points into five qualitative categories, ranging from “non-polluted” to “severely polluted.” Sites S1–S4 consistently fell into the “polluted” or “severely polluted” categories across all campaigns, corroborating the deteriorated conditions indicated by both WQI and PIS. Particularly low RPI scores at S3 highlight the combined effect of organic loading and reduced oxygen levels due to intensive industrial effluent inputs. These results confirm that conventional parameters alone can effectively signal ecological stress, especially when used in parallel with multicomponent indices.
Obtained RPI values based on all major pollution sources
| Main pollution source | Yearly RPI value | |||||
|---|---|---|---|---|---|---|
| 2014 | 2015 | 2016 | 2017 | SD | RSD | |
| Minor diffuse pollution (no major polluters) | 0.46 | 0.44 | 0.31 | 0.37 | 0.51 | 53.8 |
| Municipal wastewaters | 1.20 | 1.12 | 1.07 | 1.01 | 2.22 | 148 |
| Poultry | 1.62 | 1.72 | 1.64 | 1.66 | 0.80 | 70.5 |
| Meat industry | 2.04 | 2.01 | 2.04 | 2.00 | 0.31 | 37.5 |
| Animal feed industry | 5.73 | 4.02 | 4.18 | 5.09 | 4.47 | 123 |
3.2.4 Nemerow index (PIW)
The Nemerow index was used to characterize wastewaters on discharge points W1–W4 (Table 1) affecting the Krivaja River quality. Wastewater quality parameters were chosen solely based on national legislation [22] using different parameters of interest for different types of wastewater.
Nemerow index values for wastewater characterization are given in Table 7. PIW values represent the extent of compliance with selected regulatory values (RS (67/2011, 48/2012, 1/2016)). According to the Nemerow index, urban wastewater (W1, Table 1) is characterized as “slight” for 2014 (PIW = 1.83) and “secure” for the period 2015–2017 (PIW = 0.51–0.53), indicating better quality of wastewater from a perspective of regulation values presented in Table 4. More drastic changes in wastewater quality are observed for wastewater originating from poultry (W2), whereas in 2016 this wastewater was classified as moderately polluted (PIW = 2.69), while for other years of monitoring classification goes up to heavily polluted water (PIW = 4.05–8.20). This indicates changes in production and wastewater treatment efficiency (Table 8). For wastewater from sampling point W3 only in the year of 2015 wastewater was classified as heavily polluted (PIW = 4.35), while for other years of monitoring wastewater was classified as moderately polluted (PIW = 1.86–2.95). By far, the worst PIW scores are obtained for wastewater from the W4 discharge point, classifying it as heavily polluted (PIW = 27.6–102). These abnormally high PIW values are a result of significantly higher levels of pollutants in wastewater at W4 in comparison to wastewaters from other discharge points (W1–W3) (Table 4). Consequently, much higher SD values are obtained for W4 (SD = 34.0) compared to W1–W3 (SD = 0.65–2.77) (Table 4).
Single-factor pollution and Nemerow index values for wastewater at discharge points W1–W4
| Wastewater discharge points | Single-factor pollution index | Nemerow index | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2014 | 2015 | 2016 | 2017 | SD | RSD | 2014 | 2015 | 2016 | 2017 | SD | RSD | |
| W1 | 1.72 | 0.48 | 0.45 | 0.47 | 0.63 | 80.4 | 1.83 | 0.53 | 0.53 | 0.51 | 0.65 | 76.6 |
| W2 | 7.82 | 8.02 | 2.65 | 3.98 | 2.71 | 48.3 | 7.92 | 8.20 | 2.69 | 4.05 | 2.77 | 48.5 |
| W3 | 2.48 | 4.32 | 2.93 | 2.60 | 0.85 | 27.5 | 2.51 | 4.35 | 2.95 | 1.86 | 1.05 | 36.1 |
| W4 | 102 | 42.2 | 35.9 | 27.5 | 34.0 | 65.4 | 102 | 42.3 | 36.0 | 27.6 | 34.0 | 65.3 |
Wastewater treatment efficiency for all major polluters
| Polluter | Treatment | Monitoring period | Wastewater treatment efficiency (%) |
|---|---|---|---|
| Municipal wastewater | Sedimentation and settling (W1, Table 2) | 2014–2015 | 6.86 |
| 2016–2017 | −17.4 | ||
| Poultry | Natural aeration and sedimentation (W2, Table 2) | 2014–2015 | −2.19 |
| 2016–2017 | 43.3 | ||
| Meat industry | Aeration and sedimentation (W3, Table 2) | 2014–2015 | 54.4 |
| 2016–2017 | 80.0 | ||
| Animal feed industry | Natural aeration and sedimentation (W4, Table 2) | 2014–2015 | −34.7 |
| 2016–2017 | −56.6 |
3.3 PCA and other synthetic parameters
As a significant number of potential polluters is present in just about 2 km of river flow, in order to extract and analyze the most relevant specific data and to identify variable which interacts with one another, PCA analysis was applied by wastewater discharge points (W1 to W4) and sampling locations (S0 to S5) (Figure 1 and Table 1). The obtained results are presented in Table 9. It is very important to distinguish these sources and to determine the cause and effect relationship. The interaction of the water–sediment system is also present, such as interaction with natural organic matter in river sediments that may derive from the input of terrestrial vegetation that can result in sorption [45,46].
Loadings of VW experimental variables (34) on significant principal components for the Krivaja River data set
| Variable | VW1 | VW2 | VW3 |
|---|---|---|---|
| EC (W1) | 0.853 | −0.028 | −0.510 |
| COD (W1) | −0.297 | −0.804 | 0.444 |
| BOD5 (W1) | −0.064 | 0.169 | 0.851 |
| TN (W1) | |||
| 0.980 | −0.151 | 0.087 | |
| N-NH3 (W1) | |||
| 0.956 | −0.268 | 0.121 | |
| TSS (W1) | |||
| 0.983 | −0.059 | 0.176 | |
| TDS (W1) | |||
| 0.964 | −0.231 | −0.044 | |
| pH (W2) | |||
| −0.449 | 0.434 | −0.762 | |
| EC (W2) | |||
| −0.158 | −0.827 | −0.539 | |
| COD (W2) | |||
| 0.468 | −0.153 | 0.871 | |
| BOD5 (W2) | |||
| 0.467 | −0.279 | 0.836 | |
| TN (W2) | |||
| 0.170 | −0.982 | 0.061 | |
| N-NH3 (W2) | |||
| −0.279 | −0.630 | −0.722 | |
| OF (W2) | |||
| 0.918 | 0.137 | 0.314 | |
| TP (W2) | |||
| 0.450 | −0.103 | 0.859 | |
| TSS (W2) | |||
| 0.980 | −0.071 | 0.157 | |
| TDS (W2) | 0.440 | 0.685 | −0.024 |
| pH (W3) | |||
| 0.974 | 0.024 | 0.226 | |
| EC (W3) | |||
| −0.681 | 0.706 | 0.133 | |
| COD (W3) | |||
| −0.462 | 0.608 | −0.645 | |
| BOD5 (W3) | |||
| −0.664 | 0.504 | −0.537 | |
| TN (W3) | |||
| 0.059 | 0.993 | −0.071 | |
| N-NH3 (W3) | |||
| −0.331 | 0.939 | −0.007 | |
| OF (W3) | |||
| 0.077 | −0.684 | −0.551 | |
| TP (W3) | |||
| −0.453 | 0.872 | −0.045 | |
| TSS (W3) | |||
| 0.174 | 0.297 | −0.937 | |
| EC (W4) | |||
| 0.910 | −0.226 | 0.242 | |
| COD (W4) | |||
| 0.985 | −0.065 | 0.160 | |
| BOD5 (W4) | |||
| 0.921 | 0.136 | 0.161 | |
| N-NH3 (W4) | |||
| 0.919 | 0.055 | 0.290 | |
| OF (W4) | |||
| −0.352 | 0.319 | 0.847 | |
| TP (W4) | |||
| 0.496 | −0.290 | 0.807 | |
| TSS (W4) | |||
| 0.233 | −0.417 | −0.879 | |
| TDS (W4) | |||
| 0.897 | −0.105 | 0.198 | |
| Eigenvalue | 19.9 | 10.9 | 9.20 |
| % Total variance | 43.6 | 24.9 | 20.9 |
| Cumulative % variance | 43.6 | 68.5 | 89.4 |
Note: Bold values indicate strong loadings; VW4 was not shown due to a small significance of variables.
First, the loadings of experimental variables (34) on significant principal components for the Krivaja River data set from wastewater sample points (W1–W4) (32 samples in period 2014–2017), three Verimax factors with eigenvalues above 1, and the factors were selected. The analysis focused on values with loading above 0.70, considered as significant (strong). For the investigated samples, in the monitoring period 2014–2017, VWs accounted for 89.4% of the total variance in the wastewater quality data set.
From VW1, the first and most important component accounted for 43.6% of the total variance, and the grouping of parameters with very high significance (>0.90) was dominantly diverse. Grouping by source point W1 has a set of EC, TN, TSS, TDS, and N-NH3 parameters. The grouping of these parameters could give the notion that nitrogen compounds, dominantly N-NH3, are found in both suspended and dissolved form. W2 is dominantly determined by OF and TSS based mostly on the VW1 factor. W3 has pH values as the most important variable. Parameters EC, COD, BOD5, N-NH3, and TDS are of significant importance for the W4 point, which suggests that organic matter is predominantly present in the dissolved form, which could also be confirmed with additional analysis, e.g., TOC (total organic compound) and DOC (dissolved organic compound). The second VW2 loading references shift to COD (−0.80) from W1, EC (−0.83) and TN (−0.98) from W2 as negative, implying different sources from other variables from the VW2 group particularly from the W3 sampling point like EC (0.71), TN (0.99), N-NH3 (0.94), and TP (0.87). In the VW3 group, different sources of pollution were observed too. Positively loadings have BOD5 (0.85) from W1, COD (0.87), BOD5 (0.84), and TP (0.86) from W2, and OF (0.85) and TP (0.81) from W4, which generally is in compliance with BAT recommendations (Table 2). pH (−0.76) and N-NH3 (−0.72) from the W2 source have somewhat less negative loadings than TSS (−0.94) from W3 and TSS (−0.88) from W4. Sampling points from W3 and W4 have similar organic material input (Table 2) which could explain the grouping of these parameters.
In order to compare the relationship between the quality of wastewater (W1–W4) and related sampling points (most importantly S1–S5), PCA analysis was also applied by sampling locations (Table 1). Loadings of experimental variables are presented in Table 10. VS1 loadings (38.3%) indicate the similar negative loadings of EC (round −0.90) from S1 to S4 location, implying similar ionic activities of water samples in terms of capacity to transmit electrical current. pH (−0.93), TP (−0.91), and TSS (−0.70) at S5 also have a similar trend as EC parameters of the first four locations, hinting that phosphorus is in suspended form. A negative correlation between TN and TSS at S1 (Table 10) and between DO and BOD5 at S4 was observed in VS2 with loadings of 26.3%. This could mean that suspended solids and nitrogen compounds at S1 might largely affect DO levels and organic matter presented as BOD5 at the S4 location. At S4 location, several variables like COD, TN, TSS, and N-NH3 showed moderate correlation. In VS3, BOD5 at S1 and COD at S5 have similar sources distinct from other parameters in the group. At S2 location, COD, BOD, and TP showed moderate to high correlation, similar to parameters from S3 (DO, BOD5, N-NH3, and TP). This indicates similar sources of pollution.
Loadings of VS experimental variables (33) on significant principal components for the Krivaja River data
| Variable | VS1 | VS2 | VS3 |
|---|---|---|---|
| EC (S1) | −0.986 | −0.003 | −0.006 |
| BOD5 (S1) | −0.085 | −0.309 | −0.930 |
| TN (S1) | −0.040 | −0.773 | 0.322 |
| TSS (S1) | 0.459 | −0.842 | 0.001 |
| EC (S2) | |||
| −0.929 | 0.093 | 0.356 | |
| DO (S2) | |||
| 0.924 | −0.015 | 0.366 | |
| COD (S2) | |||
| 0.535 | −0.079 | 0.841 | |
| BOD5 (S2) | |||
| 0.461 | −0.155 | 0.864 | |
| TP (S2) | |||
| 0.280 | 0.211 | 0.923 | |
| pH (S3) | |||
| 0.033 | 0.605 | 0.778 | |
| EC (S3) | |||
| −0.923 | 0.067 | 0.160 | |
| DO (S3) | |||
| 0.508 | 0.046 | 0.860 | |
| BOD5 (S3) | |||
| 0.179 | −0.341 | 0.903 | |
| N-NH3 (S3) | |||
| −0.246 | −0.092 | 0.843 | |
| TP (S3) | |||
| −0.126 | −0.210 | 0.937 | |
| TSS (S3) | |||
| 0.934 | 0.170 | 0.314 | |
| pH (S4) | |||
| −0.508 | 0.615 | 0.436 | |
| EC (S4) | |||
| −0.889 | 0.045 | −0.373 | |
| DO (S4) | |||
| 0.316 | −0.932 | −0.107 | |
| COD (S4) | |||
| 0.631 | 0.759 | −0.081 | |
| BOD5 (S4) | |||
| −0.455 | −0.771 | 0.445 | |
| TN (S4) | |||
| 0.156 | 0.702 | −0.656 | |
| N-NH3 (S4) | |||
| 0.505 | 0.853 | −0.088 | |
| TSS (S4) | |||
| 0.239 | 0.731 | 0.267 | |
| pH (S5) | |||
| −0.925 | 0.214 | 0.266 | |
| EC (S5) | |||
| 0.779 | 0.110 | −0.305 | |
| DO (S5) | |||
| 0.898 | 0.017 | 0.244 | |
| COD (S5) | |||
| 0.474 | −0.334 | −0.804 | |
| BOD5 (S5) | |||
| 0.916 | 0.277 | 0.256 | |
| TN (S5) | |||
| 0.966 | 0.206 | 0.134 | |
| N-NH3 (S5) | |||
| 0.824 | −0.518 | 0.063 | |
| TP (S5) | |||
| −0.910 | −0.178 | −0.292 | |
| TSS (S5) | |||
| −0.700 | −0.349 | −0.215 | |
| Eigenvalue | 19.1 | 13.1 | 10.7 |
| % Total variance | 38.3 | 26.26 | 21.4 |
| Cumulative % variance | 38.3 | 64.6 | 86.0 |
Note: Bold values indicate strong loadings; VS4 was not shown due to a small significance of the variable.
4 Discussion
The RPI values obtained during the 2014–2017 monitoring period are presented in Table 6. In general, the RPI values for selected sampling points (S1–S5) show no significant changes in the observed monitoring period 2014–2017 (Table 6), with S1 being characterized as the surface water with no negative impact (RPI = 0.31–0.44), and the last sampling point (S5) classified as highly polluted surface water with strong discharged wastewater influence (RPI = 4.02–5.73), compared to water quality from sampling point S0. The surface water quality of the Krivaja River progressively degrades in downstream sections, as anticipated due to the cumulative introduction of pollutants. S2 is under the influence of urban wastewater and is classified as moderately polluted with some negative impact (RPI = 1.01–1.20). S3 is also classified as moderately polluted, but with somewhat greater negative influence (RPI = 1.62–1.72) of both urban wastewater and wastewater from poultry. Sampling point S4 is characterized by poor water quality, significantly impacted by wastewater discharge (RPI = 2.00–2.04). SD and RSD values are particularly high for RPI values of surface water from sampling points S2 and S5 (Table 6). This is due to significant increases in some parameters and decreases in others, as shown in Table 3.
According to the obtained RPI values for selected sampling points (Table 1), some parameters show no (or very low) wastewater impact on the quality of the Krivaja River, while others show a relatively high negative influence. These parameters vary for different sampling points and periods. At S1, most of the wastewater negative influence is expressed through parameters indicating organic pollution (COD, BOD5, and KMnO4 cons.) and some others (TSS and TDS). Analysis of nitrogen (TN, N-NH3, NO3 −, NO2 −) and phosphorus (TP, OP) compounds suggested that none of these parameters show a significant negative influence of wastewater, except nitrites that show a moderate influence for the year of 2014. The obtained RPI values of individual parameters at S2 indicate two periods that differ in wastewater negative influence: 2014–2015 and 2016–2017. For the monitoring period 2014–2015, the highest wastewater negative impact is expressed through parameters: COD, BOD5, NO2 −, and Cl−, while TSS, TOC, and NO3 − show no (or very low) impact. Parameters that show the most negative impact for the monitoring period 2016–2017 are TSS, COD, and Fe, while the least wastewater influence is observed for TOC, NO3 −, and TP. These changes over time could be an indication of lesser nutrient introduction, as well as a difference in natural concentrations (S0, Table 2). The negative influence of wastewater at sampling points S3 and S4 could be expressed in somewhat the same parameter importance. The greatest negative influence is shown for parameters: BOD5, TN, N-NH3, with the addition of COD and TOC at sampling point S3. The least negative influence is expressed through parameters: TSS, NO3 −, and NO2 −. The negative impact of discharged wastewaters at sampling point S5 is mostly characterized as organic pollution, as the following parameters show the greatest influence: COD, BOD5, TOC, KMnO4 cons., TN, and N-NH3. The least influence of wastewater is observed for parameters: NO3 − and NO2 −. It should be noted that RPI values for individual parameters do not show exact wastewater influence on the Krivaja River sampling point, as other contributors could play relatively significant roles such as additive effects of previous wastewater discharge points (particularly important at sampling points S2–S5), pollution originating from agricultural activities in this region, and pollutant release from sediments into the overlying water [7]. Additionally, changes in metal concentrations (Fe, Mn, Zn, Ni, Pb, Cu, Cr, Cd, and As) did not affect the final score in a significant manner.
WQI values for selected sampling points are given in Table 5, with the lowest values indicating the worst water quality. By comparing WQI with RPI, it can be concluded that WQI has more rigorous criteria for assessing water quality, as it ignores natural (and possibly other influences such as agricultural activities) surface water quality (S0). According to obtained WQI values (Table 4) and classification presented in Banerjee and Srivastava (2009) (Table S5, Supplementary material), natural quality of the Krivaja River (sampling point S0) is generally classified as satisfactory (WQI = 61.3–64.2) with the exception of samples from 2015 that are classified as moderately polluted (WQI = 53.9). This is the result of an increase in nutrient levels (particularly N-NH3) and a decrease in DO that could indicate agricultural pollution, as no other negative influence is observed in the upstream region. S1 is classified as moderately polluted (WQI = 47.1–55.9), indicating transport and unregulated pollution from Bačka Topola City. Surface water from sampling point S2 is classified as moderately polluted (WQI = 40.0–43.9), which is also supported by the RPI classification. For the monitoring period 2014–2015, sampling point S3 is also classified as moderately polluted (WQI = 40.6–43.2), while according to WQI in the period 2016–2017, this surface water has much worse quality and is classified as poor (WQI = 39.0–39.7). Surface water from S4 is classified as moderately polluted for the entire monitoring period (WQI = 43.2–45.2), which is one of the major differences between the characterization of surface water using WQI and RPI. S5, as expected, is classified as poor water quality with the lowest WQI values (WQI = 27.7–33.7). Additionally, no WQI values above 90 and below 10, i.e., no “excellent” or “worst” quality, were observed in the Krivaja River sampling points S0–S5.
The obtained values of the Nemerow index (PIS) (and Single Factor Pollution Index) are presented in Table 5, with higher values indicating higher pollution or lower water quality. The Nemerow index is the only method in this article that uses standard values (Table 3) for water quality assessment. This means that PIS values are strongly dependent on relevant legislative values. In other words, using different regulations (or the same regulations with a different approach) would yield different index values from those presented in this study.
According to Nemerow index values (Table 5) and the classification system adopted from Han et al. [40], the surface water quality at sampling point S0 degraded from “alert” to “slight” over the 2014–2017 monitoring period (PIS = 0.98–1.13). Sampling point S1 is characterized as moderate water quality (PIS = 2.61–2.84), which is also supported by WQI. Surface water from other sampling points (S2–S5) is classified as heavily polluted (PIS = 3.68–17.2), with sampling point S5 characterized as, by far, the worst (PIS = 15.6–17.2). The inadequate classification (grouping) of the last four sampling points (S2–S5) and very high SD (7.70–46.7) and RSD% values (187–285) indicates the need for a different classification approach or usage of different regulation values. For example, PI values 5.00 and 15.00 fall under the same classification, but water with these values obviously has different negative effects, or at least different intensities of negative effects.
4.1 Cluster and PCA analysis
The results of the CA were used to provide the relationships between S and W sampling sites. The CA rendered a dendrogram (Figure 2), in which all physicochemical parameters were grouped into two statistically significant clusters at (Dlink/Dmax) × 100 < 40, cluster 1, and (Dlink/Dmax) × 100 < 80, cluster 2. It is obvious that W4 (Table 1) is a dominant pollution source that influences all other parameters at related sampling sites. This can also be concluded by the obtained monitoring results (Tables 3 and 4), as well as the obtained Nemerow index values (Table 7), indicating very strong loadings of pollution originating from the W4 wastewater discharge point. The animal feed production industry, which employs a primary wastewater treatment system using aerated lagoons, demonstrated inadequate efficiency (Table 8) and a high impact on the Krivaja River’s water quality. The second cluster implied the dominant influences of W2, associated with slaughterhouses and the meat processing industry, and S2 sampling points that were influenced by W2 discharge. Generally, considering the flow of the Krivaja River, the dominant W4 discharge point of wastewater is followed by W2 and has implications on all other S sampling points, dominantly S2. This relationship is based on the TP variable from VW3 loadings of W2 (0.86) and W4 (0.81) and dominantly affects on S2 (0.92) location from VS3 loading (Tables 9 and 10). A strong negative impact of W2 can also be confirmed by Nemerow index values presented in Table 7 (PI = 2.69–8.20). Pig slaughterhouses and animal meat processing industry (W3), with a primary treatment of wastewater by a settling tank and aerated lagoon, and urban wastewater collection system with a primary treatment of wastewater from the household (W1), show a specific correlation with the S5 location. These describe the fluctuation of river flow and chemistry of substances of interest (dominantly COD, BOD, TN, TP, and TSS). The influence of W4 and W1 on S5 from cluster (Tables 9 and 10 and partly Figure 2) is primarily driven by N-NH3 parameters with significant loadings (W1 (0.96), W4 (0.92), and S5 (0.82)) of these parameters in VW1 and VS1, the first and most important components. S4 and S3 show similar data variation, mostly by the proximity of these locations, similar to S1 and S0, the less polluted locations.
Cluster analysis rendered dendrogram.
The most significant results from the 3D plot of PCA analysis (Figure 3, rectangular marked) indicate that variables EC (S1–S4), TSS (S5), TP (S5), COD (S1), and pH (S5), which load strongly and positively on the primary PCA factor, represent a cohesive group of chemical indicators. Unlike direct biological stress indicators such as DO or BOD5, this group primarily reflects chemical enrichment of the water column. This grouping highlights a transition from relatively unaltered upstream conditions (S1) to a chemically enriched and ecologically stressed downstream environment (S5), reinforcing the need for focused monitoring and management of chemical inputs along the river continuum. The results of the PCA uncover a coherent and alarming pattern of chemical pollution across wastewater discharge points W1 to W4 along the Krivaja River. These sites exhibit a consistent and intensifying presence of key pollutants, notably EC, TDS, TSS, ammonium (N-NH3), and organic load indicators (COD and BOD5). Together, these parameters form a tightly grouped pollution signature that defines the chemical profile of the wastewater and reveals both its source characteristics and potential ecological impact. The PCA results from the 3D plot of Figure 4 (rectangular marked)) confirm that EC, TDS, TSS, N-NH3, COD, and BOD5 form a strong interrelated group representing a combined chemical, organic, and particulate pollution complex across all wastewater sites. The highest impacts are observed at W1 (initial discharge) and W4 (final cumulative point), with additional contributions from W2 (solids and organics) and W3 (alkaline shift). This pattern demonstrates a severe and sustained loading of untreated or partially treated wastewater into the river system, with high risks of eutrophication, oxygen depletion, and aquatic toxicity downstream. Based on tested input, data unequivocally imply that W4 has the highest impact, followed by W2, W3, and W1 discharges, also S2 and S5 locations were under the greatest pollution pressure.
Results from PCA analysis of data from surface water sampling sites (S0–S5).
Results from PCA analysis of data from wastewater sampling sites (W1–W4).
5 Conclusion
This study provides valuable insights into the environmental challenges posed by industrial and urban wastewater pollution in the upper course of the Krivaja River. It highlights the pressing need for standardized water quality parameters at local, national, and EU levels, particularly for complex and heavily polluted water bodies. The current lack of uniform regulatory values, as highlighted in European Union Directive 2013/39/EU [44], complicates the assessment and management of pollution in regions with high industrial activity, such as the Krivaja River basin.
The findings emphasize the necessity for a systematic, multi-pronged approach to addressing wastewater issues. This includes establishing monitoring frameworks, refining key water quality indicators (e.g., ammonia-nitrogen, iron, and chlorides), and considering their inclusion in the Second Watch List under the Water Framework Directive. Such measures are crucial, not only for local water bodies but also for mitigating cross-border impacts on major basins like the Danube River.
Prioritizing remediation and minimizing wastewater discharge are essential for protecting the Krivaja River’s ecosystem. Implementing advanced treatment technologies, such as tertiary treatment, in conjunction with co-treatment facilities for industrial and communal wastewater, is recommended. These solutions should be guided by cost–benefit analyses and operational monitoring to ensure effectiveness and sustainability.
Finally, the study calls for a holistic approach, combining targeted remediation techniques with an evaluation of diffuse pollution sources and the river’s assimilative capacity. By addressing these challenges, stakeholders can significantly improve water quality, safeguard regional ecosystems, and align with broader environmental protection goals.
Acknowledgments
We are grateful to Nada Popsavin for preparing the map in Figure 1.
-
Funding information: The authors gratefully acknowledge the financial support of the Ministry of Science, Technological Development and Innovation of the Republic of Serbia (Grants No. 451-03-137/2025-03/200125 and 451-03-136/2025-03/200125).
-
Author contributions: Conceptualization: R.T., N.G., S.T.; methodology: S.M., N.G., S.T.; formal analysis: B.D., M.B.T.; investigation: S.T., B.D., R.T., M.B.T.; data curation: S.T., B.D., R.T., M.B.T.; writing – original draft preparation: N.G., V.M., R.T.; writing – review and editing: V.M., S.M.
-
Conflict of interest: Authors state no conflicts of interest.
References
[1] Balcerowska-Czerniak G, Gorczyca B. Rapid assessment of surface water quality using statistical multivariate analysis approach: Oder River system case study. Sci Total Env. 2024;912:168754.10.1016/j.scitotenv.2023.168754Search in Google Scholar PubMed
[2] Council Directive 96/61/EC of 24 September 1996 concerning Integrated Pollution Prevention and Control (IPPC). European Environmental Agency. Official Journal L 257.Search in Google Scholar
[3] Malaj E, Von der Ohe PC, Grote M, Kühne R, Mondy CP, Usseglio-Polatera P, et al. Organic chemicals jeopardize the health of freshwater ecosystems on the continental scale. Proc Natl Sci. 2014;111:9549–54. 10.1073/pnas.1321082111.Search in Google Scholar PubMed PubMed Central
[4] Krčmar D, Dubovina M, Grba N, Pešić V, Watson M, Tričković J, et al. Distribution of organic and inorganic substances in the sediments of the “Great Bačka Canal,” a European environmental hotspot. Sci Total Env. 2017;601–602:833–44. 10.1016/j.scitotenv.2017.05.251.Search in Google Scholar PubMed
[5] Dalmacija M, Dalmacija B, Krčmar D, Prica M, Rajić LJ, Rončević S, et al. Solidification/stabilization of metal polluted sediment of the Krivaja River. Hemijskaindustrija. 2012;66:469–78. 10.2298/HEMIND111031110D.Search in Google Scholar
[6] Tubić A, Leovac A, Hrubik J, Glišić B, Kaišarević S, Ivančev-Tumbas I, et al. Toxicological profiles assessment of the water and sediments from the Krivaja and Jegrička Rivers, Serbia. J Env Sci Health A Tox Hazard Subst Env Eng. 2013;48(10):1201–15. 10.1080/10934529.2013.776848.Search in Google Scholar PubMed
[7] Tomašević D, Dalmacija M, Prica M, Dalmacija B, Kerkez DJ, Bečelić-Tomin M, et al. Use of fly ash for remediation of metals polluted sediment – Green remediation. Chemosphere. 2013;92:1490–7. 10.1016/j.chemosphere.2013.03.063.Search in Google Scholar PubMed
[8] Pešić V, Bečelić-Tomin M, Kerkez DJ, Dalmacija B, Krčmar D, Maletić S, et al. Identification of significant pressures and assessment of wastewater discharge on the Krivaja River water quality. Hemijskaindustrija. 2017;71:407–18. 10.2298/HEMIND160603002P.Search in Google Scholar
[9] Grba N, Krčmar D, Maletić S, Bečelić-Tomin M, Grgić M, Pucar G, et al. Organic and inorganic priority substances in sediments of Ludaš Lake, a cross-border natural resource on the Ramsar list. Env Sci Pollut Res. 2017;24:1938–52. 10.1007/s11356-016-7904-6.Search in Google Scholar PubMed
[10] Dalmacija B, Prica M, Ivančev-Tumbas I, van der Kooij A, Rončević S, Krčmar D, et al. Pollution of the Begej Canal sediment - metals, radioactivity and toxicity assessment. Env Int. 2006;32:606–15. 10.1016/j.envint.2006.01.006.Search in Google Scholar PubMed
[11] Pesce SF, Wunderlin DA. Use of water quality indices to verify the impact of Córdoba city (Argentina) on Suqíla river. Wat Res. 2000;34:2915–26. 10.1016/S0043-1354(00)00036-1.Search in Google Scholar
[12] Fu J, Mai B, Sheng G, Zhang G, Wang X, Peng P, et al. Persistent organic pollutants in environment of the Pearl River Delta, China: an overview. Chemosphere. 2003;52(9):1411–22. 10.1016/s0045-6535(03)00477-6.Search in Google Scholar PubMed
[13] Angyal Z, Sárközi E, Gombás Á, Kardos L. Effects of land use on chemical water quality of three small streams in Budapest. Open Geosci. 2016;8(1):133–42.10.1515/geo-2016-0012Search in Google Scholar
[14] Rizo-Decelis LD, Andreo B. Water quality assessment of the Saintago river and attenuation capacity of pollutants downstream Guadalajara city, Mexico. River Res Applic. 2015;32(7):1505–16. 10.1002/rra.2988.Search in Google Scholar
[15] Zhao Y, Zou X, Gao J, Xu X, Wang X, Tang D, et al. Quantifying the anthropogenic and climatic contributions to changes in water discharge and sediment load into the sea: A case study of the Yangtze River, China. Sci Total Environ. 2015;536:803–12. 10.1016/j.scitotenv.2015.07.119.Search in Google Scholar PubMed
[16] Zhang L. Different methods for the evaluation of surface water quality: The Case of the Liao River, Liaoning Province, China. Int Rev Spat Plan Sustain Dev. 2017;4:4–18. 10.14246/irspsd.5.4_4.Search in Google Scholar
[17] Zhao Y, Zou X, Liu Q, Yao Y, Li Y, Wu X, et al. Assessing natural and anthropogenic influences on water discharge and sediment load in the Yangtze River, China. Sci Total Environ. 2017;607–608:920–32. 10.1016/j.scitotenv.2017.07.002.Search in Google Scholar PubMed
[18] Garg T, Hamilton SE, Hochard JP, Kresch EP, Talbot J. (Not so) gently down the stream: River pollution and health in Indonesia. J Environ Econ Manag. 2018;92:35–53. 10.1016/j.jeem.2018.08.011.Search in Google Scholar
[19] Ngwira L, Lakudzala D. Assessment of the quality of SOBO industrial wastewater and its impact on water quality in Nankhaka River. Elsevier Phys Chem Earth, Parts A/B/C. 2018;108:9–12. 10.1016/j.pce.2018.04.002.Search in Google Scholar
[20] Wu Z, Wang X, Chen Y, Cai Y, Deng Y. Assessing river water quality using water quality index in Lake Taihu Basin, China. Sci Total Env. 2018;612:914–22. 10.1016/j.scitotenv.2017.08.293.Search in Google Scholar PubMed
[21] Mayer AM, Klein C, Fünfrocken E, Kautenburger R, Beck HP. Real-time monitoring of water quality to identify pollution pathways in small and middle scale rivers. Sci Total Env. 2019;651:2323–33. 10.1016/j.scitotenv.2018.10.069.Search in Google Scholar PubMed
[22] RS Official Journal No 67/2011, 48/2012, 1/2016, 2016. Regulation on emission limit values of pollutants into water and deadlines for their achievement. Official Gazette of RS (67/2011, 48/2012, 1/2016) (in Serbian).Search in Google Scholar
[23] BREF FDM. Integrated Pollution Prevention and Control Reference Document on Best Available Techniques in the Food, Drink and Milk Industries, European Commission, 2006.Search in Google Scholar
[24] Talalaj IA. Assessment of groundwater quality near the landfill site using the modified water quality index. Env Monit Assess. 2014;186:3673–83. 10.1007/s10661-014-3649-1.Search in Google Scholar PubMed
[25] Krčmar D, Tenodi S, Grba N, Kerkez D, Watson M, Rončević S, et al. Preremedial assessment of the urban landfill pollution impact on soil and shallow groundwater in Subotica, Serbia. Sci Total Env. 2018;615:1341–54. 10.1016/j.scitotenv.2017.09.283.Search in Google Scholar PubMed
[26] SRPS EN ISO 5667-1:2008. Water quality - Sampling - Part 1: Guidance on the design of sampling programmes and sampling techniques, 2008.Search in Google Scholar
[27] SRPS ISO 5667-6:1997. Water quality -- Sampling Guidance on sampling of rivers and streams, 1997.Search in Google Scholar
[28] SRPS EN ISO 5667-3:2007. Sampling - Part 3: Preservation and Handling of Water Samples, 2007.Search in Google Scholar
[29] RS Official Journal No 50/2012. Regulation on emission limit values of polluting substances in surface and groundwaters and deadlines for their achievement. Official Gazette of the RS (50/2012) (in Serbian).Search in Google Scholar
[30] RS Official Journal No 24/2014. Regulation on emission limit values of priority and priority hazardous substances which pollute surface waters and deadlines for their achievement. Official Gazette of RS (24/2014), 2014 (in Serbian).Search in Google Scholar
[31] Schaumlöffel D. Nickel species: Analysis and toxic effects. J Trace Elem Med Biol. 2012;26:1–6. 10.1016/j.jtemb.2012.01.002.Search in Google Scholar PubMed
[32] Bordalo AA, Teixeira R, Wiebe WJ. A water quality index applied to an International Shared River Basin: the case of the Douro River. Env Manage. 2006;38:910–20. 10.1007/s00267-004-0037-6.Search in Google Scholar PubMed
[33] Banerjee S, Srivastava RK. Application of water quality index for assessment of surface water quality surrounding integrated industrial estate-Pantnagar. Water Sci Technol. 2009;60(8):2041–53. 10.2166/wst.2009.537.Search in Google Scholar PubMed
[34] Kannel PR, Lee S, Lee YS, Kanel SR, Khan SP. Application of water quality indices and dissolved oxygen as indicators for river water classification and urban impact assessment. Env Monit Assess. 2007;132:93–110. 10.1007/s10661-006-9505-1.Search in Google Scholar PubMed
[35] Koçer MAT, Sevgili H. Parameters selection for water quality index in the assessment of the environmental impacts of land-based trout farms. Ecol Indic. 2014;36:672–81. 10.1016/j.ecolind.2013.09.034.Search in Google Scholar
[36] Li Y, Li Y, Zhang T, Meng X, Zhao X. Water Quality Assessment in Different Water Periods of Beitang Drainage River in Tianjin. China Water Wastewater. 2008;24:102–5.Search in Google Scholar
[37] Xu G, Xie J, Zhang YK, Zhao C, Wu Q. Application of Nemerow pollution index in landscape river water quality assessment of Tianjin. 4th International Conference on Bioinformatics and Biomedical Engineering, June 18-20. Chengdu, China: 2010.10.1109/ICBBE.2010.5514830Search in Google Scholar
[38] Al-Othman AA. Evaluation of the suitability of surface water from Riyadh Mainstream Saudi Arabia for a variety of uses. Arab J Chem. 2015;12:2104–10. 10.1016/j.arabjc.2015.01.001.Search in Google Scholar
[39] Musalaiah M, Sowjanya SSG, Reddy SLK. Application of Nemerow’s pollution index (NPI) for water quality assessment of Amaravathi city, the new capital of Andhra Pradesh, India. Glob J Eng Sci Researches. 2017;4:102–9.Search in Google Scholar
[40] Han Z, Ma H, Shi G, He L, Wei L, Shi Q. A review of groundwater contamination near urban solid waste landfill sites in China. Sci Total Env. 2016;569–570:1255–64. 10.1016/j.scitotenv.2016.06.201.Search in Google Scholar PubMed
[41] Simeonov V, Massart DL, Andreev G, Tsakovski S. Assessment of metal pollution based on multivariate statistical modelling of “hot spot” sediments from the Black Sea. Chemosphere. 2000;41:1411–7. 10.1016/S0045-6535(99)00540-8.Search in Google Scholar PubMed
[42] Liu Y, Chen L, Huang QH, Li WY, Tang YJ, Zhao JF. Source apportionment of polycyclic aromatic hydrocarbons (PAHs) in surface sediments of the Huangpu River, Shanghai, China. Sci Total Env. 2009;407:2931–8. 10.1016/j.scitotenv.2008.12.046.Search in Google Scholar PubMed
[43] Zhiyuan W, Dengfeng W, Huiping Z, Zhiping Q. Assessment of soil heavy metal pollution with principal component analysis and geoaccumulation index. Procedia Env Sci. 2011;10:1946–52. 10.1016/j.proenv.2011.09.305.Search in Google Scholar
[44] Directive 2013/39/EU of the European Parliament and of the Council of 12 August 2013 amending Directives 2000/60/EC and 2008/105/EC as regards priority substances in the field of water policy Text with EEA relevance.Search in Google Scholar
[45] Reid BJ, Stokes JD, Jones KC, Semple KT. Non-exhaustive cyclodextrine-based extraction technique for the evaluation of PAH bioavailability. Env Sci Technol. 2000;34:3174.10.1021/es990946cSearch in Google Scholar
[46] Micić V, Kruge MA, Koster J, Hofmann T. Natural, anthropogenic and fossil organic matter in river sediments and suspended particulate matter: a multimolecular marker approach. Sci Total Env. 2011;409(5):905–19. 10.1016/j.scitotenv.2010.11.009.Search in Google Scholar PubMed
© 2025 the author(s), published by De Gruyter
This work is licensed under the Creative Commons Attribution 4.0 International License.
Articles in the same Issue
- Research Articles
- Seismic response and damage model analysis of rocky slopes with weak interlayers
- Multi-scenario simulation and eco-environmental effect analysis of “Production–Living–Ecological space” based on PLUS model: A case study of Anyang City
- Remote sensing estimation of chlorophyll content in rape leaves in Weibei dryland region of China
- GIS-based frequency ratio and Shannon entropy modeling for landslide susceptibility mapping: A case study in Kundah Taluk, Nilgiris District, India
- Natural gas origin and accumulation of the Changxing–Feixianguan Formation in the Puguang area, China
- Spatial variations of shear-wave velocity anomaly derived from Love wave ambient noise seismic tomography along Lembang Fault (West Java, Indonesia)
- Evaluation of cumulative rainfall and rainfall event–duration threshold based on triggering and non-triggering rainfalls: Northern Thailand case
- Pixel and region-oriented classification of Sentinel-2 imagery to assess LULC dynamics and their climate impact in Nowshera, Pakistan
- The use of radar-optical remote sensing data and geographic information system–analytical hierarchy process–multicriteria decision analysis techniques for revealing groundwater recharge prospective zones in arid-semi arid lands
- Effect of pore throats on the reservoir quality of tight sandstone: A case study of the Yanchang Formation in the Zhidan area, Ordos Basin
- Hydroelectric simulation of the phreatic water response of mining cracked soil based on microbial solidification
- Spatial-temporal evolution of habitat quality in tropical monsoon climate region based on “pattern–process–quality” – a case study of Cambodia
- Early Permian to Middle Triassic Formation petroleum potentials of Sydney Basin, Australia: A geochemical analysis
- Micro-mechanism analysis of Zhongchuan loess liquefaction disaster induced by Jishishan M6.2 earthquake in 2023
- Prediction method of S-wave velocities in tight sandstone reservoirs – a case study of CO2 geological storage area in Ordos Basin
- Ecological restoration in valley area of semiarid region damaged by shallow buried coal seam mining
- Hydrocarbon-generating characteristics of Xujiahe coal-bearing source rocks in the continuous sedimentary environment of the Southwest Sichuan
- Hazard analysis of future surface displacements on active faults based on the recurrence interval of strong earthquakes
- Structural characterization of the Zalm district, West Saudi Arabia, using aeromagnetic data: An approach for gold mineral exploration
- Research on the variation in the Shields curve of silt initiation
- Reuse of agricultural drainage water and wastewater for crop irrigation in southeastern Algeria
- Assessing the effectiveness of utilizing low-cost inertial measurement unit sensors for producing as-built plans
- Analysis of the formation process of a natural fertilizer in the loess area
- Machine learning methods for landslide mapping studies: A comparative study of SVM and RF algorithms in the Oued Aoulai watershed (Morocco)
- Chemical dissolution and the source of salt efflorescence in weathering of sandstone cultural relics
- Molecular simulation of methane adsorption capacity in transitional shale – a case study of Longtan Formation shale in Southern Sichuan Basin, SW China
- Evolution characteristics of extreme maximum temperature events in Central China and adaptation strategies under different future warming scenarios
- Estimating Bowen ratio in local environment based on satellite imagery
- 3D fusion modeling of multi-scale geological structures based on subdivision-NURBS surfaces and stratigraphic sequence formalization
- Comparative analysis of machine learning algorithms in Google Earth Engine for urban land use dynamics in rapidly urbanizing South Asian cities
- Study on the mechanism of plant root influence on soil properties in expansive soil areas
- Simulation of seismic hazard parameters and earthquakes source mechanisms along the Red Sea rift, western Saudi Arabia
- Tectonics vs sedimentation in foredeep basins: A tale from the Oligo-Miocene Monte Falterona Formation (Northern Apennines, Italy)
- Investigation of landslide areas in Tokat-Almus road between Bakımlı-Almus by the PS-InSAR method (Türkiye)
- Predicting coastal variations in non-storm conditions with machine learning
- Cross-dimensional adaptivity research on a 3D earth observation data cube model
- Geochronology and geochemistry of late Paleozoic volcanic rocks in eastern Inner Mongolia and their geological significance
- Spatial and temporal evolution of land use and habitat quality in arid regions – a case of Northwest China
- Ground-penetrating radar imaging of subsurface karst features controlling water leakage across Wadi Namar dam, south Riyadh, Saudi Arabia
- Rayleigh wave dispersion inversion via modified sine cosine algorithm: Application to Hangzhou, China passive surface wave data
- Fractal insights into permeability control by pore structure in tight sandstone reservoirs, Heshui area, Ordos Basin
- Debris flow hazard characteristic and mitigation in Yusitong Gully, Hengduan Mountainous Region
- Research on community characteristics of vegetation restoration in hilly power engineering based on multi temporal remote sensing technology
- Identification of radial drainage networks based on topographic and geometric features
- Trace elements and melt inclusion in zircon within the Qunji porphyry Cu deposit: Application to the metallogenic potential of the reduced magma-hydrothermal system
- Pore, fracture characteristics and diagenetic evolution of medium-maturity marine shales from the Silurian Longmaxi Formation, NE Sichuan Basin, China
- Study of the earthquakes source parameters, site response, and path attenuation using P and S-waves spectral inversion, Aswan region, south Egypt
- Source of contamination and assessment of potential health risks of potentially toxic metal(loid)s in agricultural soil from Al Lith, Saudi Arabia
- Regional spatiotemporal evolution and influencing factors of rural construction areas in the Nanxi River Basin via GIS
- An efficient network for object detection in scale-imbalanced remote sensing images
- Effect of microscopic pore–throat structure heterogeneity on waterflooding seepage characteristics of tight sandstone reservoirs
- Environmental health risk assessment of Zn, Cd, Pb, Fe, and Co in coastal sediments of the southeastern Gulf of Aqaba
- A modified Hoek–Brown model considering softening effects and its applications
- Evaluation of engineering properties of soil for sustainable urban development
- The spatio-temporal characteristics and influencing factors of sustainable development in China’s provincial areas
- Application of a mixed additive and multiplicative random error model to generate DTM products from LiDAR data
- Gold vein mineralogy and oxygen isotopes of Wadi Abu Khusheiba, Jordan
- Prediction of surface deformation time series in closed mines based on LSTM and optimization algorithms
- 2D–3D Geological features collaborative identification of surrounding rock structural planes in hydraulic adit based on OC-AINet
- Spatiotemporal patterns and drivers of Chl-a in Chinese lakes between 1986 and 2023
- Land use classification through fusion of remote sensing images and multi-source data
- Nexus between renewable energy, technological innovation, and carbon dioxide emissions in Saudi Arabia
- Analysis of the spillover effects of green organic transformation on sustainable development in ethnic regions’ agriculture and animal husbandry
- Factors impacting spatial distribution of black and odorous water bodies in Hebei
- Large-scale shaking table tests on the liquefaction and deformation responses of an ultra-deep overburden
- Impacts of climate change and sea-level rise on the coastal geological environment of Quang Nam province, Vietnam
- Reservoir characterization and exploration potential of shale reservoir near denudation area: A case study of Ordovician–Silurian marine shale, China
- Seismic prediction of Permian volcanic rock reservoirs in Southwest Sichuan Basin
- Application of CBERS-04 IRS data to land surface temperature inversion: A case study based on Minqin arid area
- Geological characteristics and prospecting direction of Sanjiaoding gold mine in Saishiteng area
- Research on the deformation prediction model of surrounding rock based on SSA-VMD-GRU
- Geochronology, geochemical characteristics, and tectonic significance of the granites, Menghewula, Southern Great Xing’an range
- Hazard classification of active faults in Yunnan base on probabilistic seismic hazard assessment
- Characteristics analysis of hydrate reservoirs with different geological structures developed by vertical well depressurization
- Estimating the travel distance of channelized rock avalanches using genetic programming method
- Landscape preferences of hikers in Three Parallel Rivers Region and its adjacent regions by content analysis of user-generated photography
- New age constraints of the LGM onset in the Bohemian Forest – Central Europe
- Characteristics of geological evolution based on the multifractal singularity theory: A case study of Heyu granite and Mesozoic tectonics
- Soil water content and longitudinal microbiota distribution in disturbed areas of tower foundations of power transmission and transformation projects
- Oil accumulation process of the Kongdian reservoir in the deep subsag zone of the Cangdong Sag, Bohai Bay Basin, China
- Investigation of velocity profile in rock–ice avalanche by particle image velocimetry measurement
- Optimizing 3D seismic survey geometries using ray tracing and illumination modeling: A case study from Penobscot field
- Sedimentology of the Phra That and Pha Daeng Formations: A preliminary evaluation of geological CO2 storage potential in the Lampang Basin, Thailand
- Improved classification algorithm for hyperspectral remote sensing images based on the hybrid spectral network model
- Map analysis of soil erodibility rates and gully erosion sites in Anambra State, South Eastern Nigeria
- Identification and driving mechanism of land use conflict in China’s South-North transition zone: A case study of Huaihe River Basin
- Evaluation of the impact of land-use change on earthquake risk distribution in different periods: An empirical analysis from Sichuan Province
- A test site case study on the long-term behavior of geotextile tubes
- An experimental investigation into carbon dioxide flooding and rock dissolution in low-permeability reservoirs of the South China Sea
- Detection and semi-quantitative analysis of naphthenic acids in coal and gangue from mining areas in China
- Comparative effects of olivine and sand on KOH-treated clayey soil
- YOLO-MC: An algorithm for early forest fire recognition based on drone image
- Earthquake building damage classification based on full suite of Sentinel-1 features
- Potential landslide detection and influencing factors analysis in the upper Yellow River based on SBAS-InSAR technology
- Assessing green area changes in Najran City, Saudi Arabia (2013–2022) using hybrid deep learning techniques
- An advanced approach integrating methods to estimate hydraulic conductivity of different soil types supported by a machine learning model
- Hybrid methods for land use and land cover classification using remote sensing and combined spectral feature extraction: A case study of Najran City, KSA
- Streamlining digital elevation model construction from historical aerial photographs: The impact of reference elevation data on spatial accuracy
- Analysis of urban expansion patterns in the Yangtze River Delta based on the fusion impervious surfaces dataset
- A metaverse-based visual analysis approach for 3D reservoir models
- Late Quaternary record of 100 ka depositional cycles on the Larache shelf (NW Morocco)
- Integrated well-seismic analysis of sedimentary facies distribution: A case study from the Mesoproterozoic, Ordos Basin, China
- Study on the spatial equilibrium of cultural and tourism resources in Macao, China
- Urban road surface condition detecting and integrating based on the mobile sensing framework with multi-modal sensors
- Application of improved sine cosine algorithm with chaotic mapping and novel updating methods for joint inversion of resistivity and surface wave data
- The synergistic use of AHP and GIS to assess factors driving forest fire potential in a peat swamp forest in Thailand
- Dynamic response analysis and comprehensive evaluation of cement-improved aeolian sand roadbed
- Rock control on evolution of Khorat Cuesta, Khorat UNESCO Geopark, Northeastern Thailand
- Gradient response mechanism of carbon storage: Spatiotemporal analysis of economic-ecological dimensions based on hybrid machine learning
- Comparison of several seismic active earth pressure calculation methods for retaining structures
- Mantle dynamics and petrogenesis of Gomer basalts in the Northwestern Ethiopia: A geochemical perspective
- Study on ground deformation monitoring in Xiong’an New Area from 2021 to 2023 based on DS-InSAR
- Paleoenvironmental characteristics of continental shale and its significance to organic matter enrichment: Taking the fifth member of Xujiahe Formation in Tianfu area of Sichuan Basin as an example
- Equipping the integral approach with generalized least squares to reconstruct relict channel profile and its usage in the Shanxi Rift, northern China
- InSAR-driven landslide hazard assessment along highways in hilly regions: A case-based validation approach
- Attribution analysis of multi-temporal scale surface streamflow changes in the Ganjiang River based on a multi-temporal Budyko framework
- Maps analysis of Najran City, Saudi Arabia to enhance agricultural development using hybrid system of ANN and multi-CNN models
- Hybrid deep learning with a random forest system for sustainable agricultural land cover classification using DEM in Najran, Saudi Arabia
- Long-term evolution patterns of groundwater depth and lagged response to precipitation in a complex aquifer system: Insights from Huaibei Region, China
- Remote sensing and machine learning for lithology and mineral detection in NW, Pakistan
- Spatial–temporal variations of NO2 pollution in Shandong Province based on Sentinel-5P satellite data and influencing factors
- Numerical modeling of geothermal energy piles with sensitivity and parameter variation analysis of a case study
- Stability analysis of valley-type upstream tailings dams using a 3D model
- Variation characteristics and attribution analysis of actual evaporation at monthly time scale from 1982 to 2019 in Jialing River Basin, China
- Investigating machine learning and statistical approaches for landslide susceptibility mapping in Minfeng County, Xinjiang
- Investigating spatiotemporal patterns for comprehensive accessibility of service facilities by location-based service data in Nanjing (2016–2022)
- A pre-treatment method for particle size analysis of fine-grained sedimentary rocks, Bohai Bay Basin, China
- Study on the formation mechanism of the hard-shell layer of liquefied silty soil
- Comprehensive analysis of agricultural CEE: Efficiency assessment, mechanism identification, and policy response – A case study of Anhui Province
- Simulation study on the damage and failure mechanism of the surrounding rock in sanded dolomite tunnels
- Towards carbon neutrality: Spatiotemporal evolution and key influences on agricultural ecological efficiency in Northwest China
- Review Articles
- Humic substances influence on the distribution of dissolved iron in seawater: A review of electrochemical methods and other techniques
- Applications of physics-informed neural networks in geosciences: From basic seismology to comprehensive environmental studies
- Ore-controlling structures of granite-related uranium deposits in South China: A review
- Shallow geological structure features in Balikpapan Bay East Kalimantan Province – Indonesia
- A review on the tectonic affinity of microcontinents and evolution of the Proto-Tethys Ocean in Northeastern Tibet
- Advancements in machine learning applications for mineral prospecting and geophysical inversion: A review
- Special Issue: Natural Resources and Environmental Risks: Towards a Sustainable Future - Part II
- Depopulation in the Visok micro-region: Toward demographic and economic revitalization
- Special Issue: Geospatial and Environmental Dynamics - Part II
- Advancing urban sustainability: Applying GIS technologies to assess SDG indicators – a case study of Podgorica (Montenegro)
- Spatiotemporal and trend analysis of common cancers in men in Central Serbia (1999–2021)
- Minerals for the green agenda, implications, stalemates, and alternatives
- Spatiotemporal water quality analysis of Vrana Lake, Croatia
- Functional transformation of settlements in coal exploitation zones: A case study of the municipality of Stanari in Republic of Srpska (Bosnia and Herzegovina)
- Hypertension in AP Vojvodina (Northern Serbia): A spatio-temporal analysis of patients at the Institute for Cardiovascular Diseases of Vojvodina
- Regional patterns in cause-specific mortality in Montenegro, 1991–2019
- Spatio-temporal analysis of flood events using GIS and remote sensing-based approach in the Ukrina River Basin, Bosnia and Herzegovina
- Flash flood susceptibility mapping using LiDAR-Derived DEM and machine learning algorithms: Ljuboviđa case study, Serbia
- Geocultural heritage as a basis for geotourism development: Banjska Monastery, Zvečan (Serbia)
- Assessment of groundwater potential zones using GIS and AHP techniques – A case study of the zone of influence of Kolubara Mining Basin
- Impact of the agri-geographical transformation of rural settlements on the geospatial dynamics of soil erosion intensity in municipalities of Central Serbia
- Where faith meets geomorphology: The cultural and religious significance of geodiversity explored through geospatial technologies
- Applications of local climate zone classification in European cities: A review of in situ and mobile monitoring methods in urban climate studies
- Complex multivariate water quality impact assessment on Krivaja River
- Ionization hotspots near waterfalls in Eastern Serbia’s Stara Planina Mountain
- Shift in landscape use strategies during the transition from the Bronze age to Iron age in Northwest Serbia
- Assessing the geotourism potential of glacial lakes in Plav, Montenegro: A multi-criteria assessment by using the M-GAM model
- Flash flood potential index at national scale: Susceptibility assessment within catchments
- SWAT modelling and MCDM for spatial valuation in small hydropower planning
Articles in the same Issue
- Research Articles
- Seismic response and damage model analysis of rocky slopes with weak interlayers
- Multi-scenario simulation and eco-environmental effect analysis of “Production–Living–Ecological space” based on PLUS model: A case study of Anyang City
- Remote sensing estimation of chlorophyll content in rape leaves in Weibei dryland region of China
- GIS-based frequency ratio and Shannon entropy modeling for landslide susceptibility mapping: A case study in Kundah Taluk, Nilgiris District, India
- Natural gas origin and accumulation of the Changxing–Feixianguan Formation in the Puguang area, China
- Spatial variations of shear-wave velocity anomaly derived from Love wave ambient noise seismic tomography along Lembang Fault (West Java, Indonesia)
- Evaluation of cumulative rainfall and rainfall event–duration threshold based on triggering and non-triggering rainfalls: Northern Thailand case
- Pixel and region-oriented classification of Sentinel-2 imagery to assess LULC dynamics and their climate impact in Nowshera, Pakistan
- The use of radar-optical remote sensing data and geographic information system–analytical hierarchy process–multicriteria decision analysis techniques for revealing groundwater recharge prospective zones in arid-semi arid lands
- Effect of pore throats on the reservoir quality of tight sandstone: A case study of the Yanchang Formation in the Zhidan area, Ordos Basin
- Hydroelectric simulation of the phreatic water response of mining cracked soil based on microbial solidification
- Spatial-temporal evolution of habitat quality in tropical monsoon climate region based on “pattern–process–quality” – a case study of Cambodia
- Early Permian to Middle Triassic Formation petroleum potentials of Sydney Basin, Australia: A geochemical analysis
- Micro-mechanism analysis of Zhongchuan loess liquefaction disaster induced by Jishishan M6.2 earthquake in 2023
- Prediction method of S-wave velocities in tight sandstone reservoirs – a case study of CO2 geological storage area in Ordos Basin
- Ecological restoration in valley area of semiarid region damaged by shallow buried coal seam mining
- Hydrocarbon-generating characteristics of Xujiahe coal-bearing source rocks in the continuous sedimentary environment of the Southwest Sichuan
- Hazard analysis of future surface displacements on active faults based on the recurrence interval of strong earthquakes
- Structural characterization of the Zalm district, West Saudi Arabia, using aeromagnetic data: An approach for gold mineral exploration
- Research on the variation in the Shields curve of silt initiation
- Reuse of agricultural drainage water and wastewater for crop irrigation in southeastern Algeria
- Assessing the effectiveness of utilizing low-cost inertial measurement unit sensors for producing as-built plans
- Analysis of the formation process of a natural fertilizer in the loess area
- Machine learning methods for landslide mapping studies: A comparative study of SVM and RF algorithms in the Oued Aoulai watershed (Morocco)
- Chemical dissolution and the source of salt efflorescence in weathering of sandstone cultural relics
- Molecular simulation of methane adsorption capacity in transitional shale – a case study of Longtan Formation shale in Southern Sichuan Basin, SW China
- Evolution characteristics of extreme maximum temperature events in Central China and adaptation strategies under different future warming scenarios
- Estimating Bowen ratio in local environment based on satellite imagery
- 3D fusion modeling of multi-scale geological structures based on subdivision-NURBS surfaces and stratigraphic sequence formalization
- Comparative analysis of machine learning algorithms in Google Earth Engine for urban land use dynamics in rapidly urbanizing South Asian cities
- Study on the mechanism of plant root influence on soil properties in expansive soil areas
- Simulation of seismic hazard parameters and earthquakes source mechanisms along the Red Sea rift, western Saudi Arabia
- Tectonics vs sedimentation in foredeep basins: A tale from the Oligo-Miocene Monte Falterona Formation (Northern Apennines, Italy)
- Investigation of landslide areas in Tokat-Almus road between Bakımlı-Almus by the PS-InSAR method (Türkiye)
- Predicting coastal variations in non-storm conditions with machine learning
- Cross-dimensional adaptivity research on a 3D earth observation data cube model
- Geochronology and geochemistry of late Paleozoic volcanic rocks in eastern Inner Mongolia and their geological significance
- Spatial and temporal evolution of land use and habitat quality in arid regions – a case of Northwest China
- Ground-penetrating radar imaging of subsurface karst features controlling water leakage across Wadi Namar dam, south Riyadh, Saudi Arabia
- Rayleigh wave dispersion inversion via modified sine cosine algorithm: Application to Hangzhou, China passive surface wave data
- Fractal insights into permeability control by pore structure in tight sandstone reservoirs, Heshui area, Ordos Basin
- Debris flow hazard characteristic and mitigation in Yusitong Gully, Hengduan Mountainous Region
- Research on community characteristics of vegetation restoration in hilly power engineering based on multi temporal remote sensing technology
- Identification of radial drainage networks based on topographic and geometric features
- Trace elements and melt inclusion in zircon within the Qunji porphyry Cu deposit: Application to the metallogenic potential of the reduced magma-hydrothermal system
- Pore, fracture characteristics and diagenetic evolution of medium-maturity marine shales from the Silurian Longmaxi Formation, NE Sichuan Basin, China
- Study of the earthquakes source parameters, site response, and path attenuation using P and S-waves spectral inversion, Aswan region, south Egypt
- Source of contamination and assessment of potential health risks of potentially toxic metal(loid)s in agricultural soil from Al Lith, Saudi Arabia
- Regional spatiotemporal evolution and influencing factors of rural construction areas in the Nanxi River Basin via GIS
- An efficient network for object detection in scale-imbalanced remote sensing images
- Effect of microscopic pore–throat structure heterogeneity on waterflooding seepage characteristics of tight sandstone reservoirs
- Environmental health risk assessment of Zn, Cd, Pb, Fe, and Co in coastal sediments of the southeastern Gulf of Aqaba
- A modified Hoek–Brown model considering softening effects and its applications
- Evaluation of engineering properties of soil for sustainable urban development
- The spatio-temporal characteristics and influencing factors of sustainable development in China’s provincial areas
- Application of a mixed additive and multiplicative random error model to generate DTM products from LiDAR data
- Gold vein mineralogy and oxygen isotopes of Wadi Abu Khusheiba, Jordan
- Prediction of surface deformation time series in closed mines based on LSTM and optimization algorithms
- 2D–3D Geological features collaborative identification of surrounding rock structural planes in hydraulic adit based on OC-AINet
- Spatiotemporal patterns and drivers of Chl-a in Chinese lakes between 1986 and 2023
- Land use classification through fusion of remote sensing images and multi-source data
- Nexus between renewable energy, technological innovation, and carbon dioxide emissions in Saudi Arabia
- Analysis of the spillover effects of green organic transformation on sustainable development in ethnic regions’ agriculture and animal husbandry
- Factors impacting spatial distribution of black and odorous water bodies in Hebei
- Large-scale shaking table tests on the liquefaction and deformation responses of an ultra-deep overburden
- Impacts of climate change and sea-level rise on the coastal geological environment of Quang Nam province, Vietnam
- Reservoir characterization and exploration potential of shale reservoir near denudation area: A case study of Ordovician–Silurian marine shale, China
- Seismic prediction of Permian volcanic rock reservoirs in Southwest Sichuan Basin
- Application of CBERS-04 IRS data to land surface temperature inversion: A case study based on Minqin arid area
- Geological characteristics and prospecting direction of Sanjiaoding gold mine in Saishiteng area
- Research on the deformation prediction model of surrounding rock based on SSA-VMD-GRU
- Geochronology, geochemical characteristics, and tectonic significance of the granites, Menghewula, Southern Great Xing’an range
- Hazard classification of active faults in Yunnan base on probabilistic seismic hazard assessment
- Characteristics analysis of hydrate reservoirs with different geological structures developed by vertical well depressurization
- Estimating the travel distance of channelized rock avalanches using genetic programming method
- Landscape preferences of hikers in Three Parallel Rivers Region and its adjacent regions by content analysis of user-generated photography
- New age constraints of the LGM onset in the Bohemian Forest – Central Europe
- Characteristics of geological evolution based on the multifractal singularity theory: A case study of Heyu granite and Mesozoic tectonics
- Soil water content and longitudinal microbiota distribution in disturbed areas of tower foundations of power transmission and transformation projects
- Oil accumulation process of the Kongdian reservoir in the deep subsag zone of the Cangdong Sag, Bohai Bay Basin, China
- Investigation of velocity profile in rock–ice avalanche by particle image velocimetry measurement
- Optimizing 3D seismic survey geometries using ray tracing and illumination modeling: A case study from Penobscot field
- Sedimentology of the Phra That and Pha Daeng Formations: A preliminary evaluation of geological CO2 storage potential in the Lampang Basin, Thailand
- Improved classification algorithm for hyperspectral remote sensing images based on the hybrid spectral network model
- Map analysis of soil erodibility rates and gully erosion sites in Anambra State, South Eastern Nigeria
- Identification and driving mechanism of land use conflict in China’s South-North transition zone: A case study of Huaihe River Basin
- Evaluation of the impact of land-use change on earthquake risk distribution in different periods: An empirical analysis from Sichuan Province
- A test site case study on the long-term behavior of geotextile tubes
- An experimental investigation into carbon dioxide flooding and rock dissolution in low-permeability reservoirs of the South China Sea
- Detection and semi-quantitative analysis of naphthenic acids in coal and gangue from mining areas in China
- Comparative effects of olivine and sand on KOH-treated clayey soil
- YOLO-MC: An algorithm for early forest fire recognition based on drone image
- Earthquake building damage classification based on full suite of Sentinel-1 features
- Potential landslide detection and influencing factors analysis in the upper Yellow River based on SBAS-InSAR technology
- Assessing green area changes in Najran City, Saudi Arabia (2013–2022) using hybrid deep learning techniques
- An advanced approach integrating methods to estimate hydraulic conductivity of different soil types supported by a machine learning model
- Hybrid methods for land use and land cover classification using remote sensing and combined spectral feature extraction: A case study of Najran City, KSA
- Streamlining digital elevation model construction from historical aerial photographs: The impact of reference elevation data on spatial accuracy
- Analysis of urban expansion patterns in the Yangtze River Delta based on the fusion impervious surfaces dataset
- A metaverse-based visual analysis approach for 3D reservoir models
- Late Quaternary record of 100 ka depositional cycles on the Larache shelf (NW Morocco)
- Integrated well-seismic analysis of sedimentary facies distribution: A case study from the Mesoproterozoic, Ordos Basin, China
- Study on the spatial equilibrium of cultural and tourism resources in Macao, China
- Urban road surface condition detecting and integrating based on the mobile sensing framework with multi-modal sensors
- Application of improved sine cosine algorithm with chaotic mapping and novel updating methods for joint inversion of resistivity and surface wave data
- The synergistic use of AHP and GIS to assess factors driving forest fire potential in a peat swamp forest in Thailand
- Dynamic response analysis and comprehensive evaluation of cement-improved aeolian sand roadbed
- Rock control on evolution of Khorat Cuesta, Khorat UNESCO Geopark, Northeastern Thailand
- Gradient response mechanism of carbon storage: Spatiotemporal analysis of economic-ecological dimensions based on hybrid machine learning
- Comparison of several seismic active earth pressure calculation methods for retaining structures
- Mantle dynamics and petrogenesis of Gomer basalts in the Northwestern Ethiopia: A geochemical perspective
- Study on ground deformation monitoring in Xiong’an New Area from 2021 to 2023 based on DS-InSAR
- Paleoenvironmental characteristics of continental shale and its significance to organic matter enrichment: Taking the fifth member of Xujiahe Formation in Tianfu area of Sichuan Basin as an example
- Equipping the integral approach with generalized least squares to reconstruct relict channel profile and its usage in the Shanxi Rift, northern China
- InSAR-driven landslide hazard assessment along highways in hilly regions: A case-based validation approach
- Attribution analysis of multi-temporal scale surface streamflow changes in the Ganjiang River based on a multi-temporal Budyko framework
- Maps analysis of Najran City, Saudi Arabia to enhance agricultural development using hybrid system of ANN and multi-CNN models
- Hybrid deep learning with a random forest system for sustainable agricultural land cover classification using DEM in Najran, Saudi Arabia
- Long-term evolution patterns of groundwater depth and lagged response to precipitation in a complex aquifer system: Insights from Huaibei Region, China
- Remote sensing and machine learning for lithology and mineral detection in NW, Pakistan
- Spatial–temporal variations of NO2 pollution in Shandong Province based on Sentinel-5P satellite data and influencing factors
- Numerical modeling of geothermal energy piles with sensitivity and parameter variation analysis of a case study
- Stability analysis of valley-type upstream tailings dams using a 3D model
- Variation characteristics and attribution analysis of actual evaporation at monthly time scale from 1982 to 2019 in Jialing River Basin, China
- Investigating machine learning and statistical approaches for landslide susceptibility mapping in Minfeng County, Xinjiang
- Investigating spatiotemporal patterns for comprehensive accessibility of service facilities by location-based service data in Nanjing (2016–2022)
- A pre-treatment method for particle size analysis of fine-grained sedimentary rocks, Bohai Bay Basin, China
- Study on the formation mechanism of the hard-shell layer of liquefied silty soil
- Comprehensive analysis of agricultural CEE: Efficiency assessment, mechanism identification, and policy response – A case study of Anhui Province
- Simulation study on the damage and failure mechanism of the surrounding rock in sanded dolomite tunnels
- Towards carbon neutrality: Spatiotemporal evolution and key influences on agricultural ecological efficiency in Northwest China
- Review Articles
- Humic substances influence on the distribution of dissolved iron in seawater: A review of electrochemical methods and other techniques
- Applications of physics-informed neural networks in geosciences: From basic seismology to comprehensive environmental studies
- Ore-controlling structures of granite-related uranium deposits in South China: A review
- Shallow geological structure features in Balikpapan Bay East Kalimantan Province – Indonesia
- A review on the tectonic affinity of microcontinents and evolution of the Proto-Tethys Ocean in Northeastern Tibet
- Advancements in machine learning applications for mineral prospecting and geophysical inversion: A review
- Special Issue: Natural Resources and Environmental Risks: Towards a Sustainable Future - Part II
- Depopulation in the Visok micro-region: Toward demographic and economic revitalization
- Special Issue: Geospatial and Environmental Dynamics - Part II
- Advancing urban sustainability: Applying GIS technologies to assess SDG indicators – a case study of Podgorica (Montenegro)
- Spatiotemporal and trend analysis of common cancers in men in Central Serbia (1999–2021)
- Minerals for the green agenda, implications, stalemates, and alternatives
- Spatiotemporal water quality analysis of Vrana Lake, Croatia
- Functional transformation of settlements in coal exploitation zones: A case study of the municipality of Stanari in Republic of Srpska (Bosnia and Herzegovina)
- Hypertension in AP Vojvodina (Northern Serbia): A spatio-temporal analysis of patients at the Institute for Cardiovascular Diseases of Vojvodina
- Regional patterns in cause-specific mortality in Montenegro, 1991–2019
- Spatio-temporal analysis of flood events using GIS and remote sensing-based approach in the Ukrina River Basin, Bosnia and Herzegovina
- Flash flood susceptibility mapping using LiDAR-Derived DEM and machine learning algorithms: Ljuboviđa case study, Serbia
- Geocultural heritage as a basis for geotourism development: Banjska Monastery, Zvečan (Serbia)
- Assessment of groundwater potential zones using GIS and AHP techniques – A case study of the zone of influence of Kolubara Mining Basin
- Impact of the agri-geographical transformation of rural settlements on the geospatial dynamics of soil erosion intensity in municipalities of Central Serbia
- Where faith meets geomorphology: The cultural and religious significance of geodiversity explored through geospatial technologies
- Applications of local climate zone classification in European cities: A review of in situ and mobile monitoring methods in urban climate studies
- Complex multivariate water quality impact assessment on Krivaja River
- Ionization hotspots near waterfalls in Eastern Serbia’s Stara Planina Mountain
- Shift in landscape use strategies during the transition from the Bronze age to Iron age in Northwest Serbia
- Assessing the geotourism potential of glacial lakes in Plav, Montenegro: A multi-criteria assessment by using the M-GAM model
- Flash flood potential index at national scale: Susceptibility assessment within catchments
- SWAT modelling and MCDM for spatial valuation in small hydropower planning
