Spatial Distribution Of Pollutants Research Articles

Assessment of the air pollutants on automobiles emission dispersion: the effects of barrier height and the road surface temperature.

In the paper, a mathematical model was constructed that describes the specifications of the wind flow and the dispersion of pollutants, taking into account the variable temperature on the roadway surface, which varies depending on the time for some quarter of the city of Almaty. The impact of the traffic tidal flow was studied based on the data of measuring passing vehicles as a source of pollution by the CFD and on the spatial distribution of pollutants for various types of pollution. A test problem was performed to validate the numerical algorithm and the mathematical model. From the obtained numerical solutions, it was determined that the existing barriers along the road have a positive effect on pedestrian zones regardless of the type of pollution. It was also found that, taking into account the variable temperature on the carriageway, the presence of barriers with a height of 4 m shows favorable behavior on the adjacent areas, in which the average concentration value drops by 6.4 times for the pedestrian zone, which is located on the left side and 2.9 times for the pedestrian zone, which is on the right side compared to the option without a barrier.

Current levels of inorganic and organic pollutants in Romanian marine waters: Implications for ecosystem health

Human activities continue to impact the Romanian Black Sea, with implications for the marine ecosystem. This study presents new data (2023) on the spatial distribution of heavy metals, persistent organic pollutants, and nutrients in Romanian Black Sea waters. Our findings reveal the influence of various pollution sources, including the Danube River, municipal and industrial discharges, and maritime activities. Nutrient loads, primarily from the Danube and diffuse sources, exert pressure on the pelagic habitat. While there has been some improvement in nutrient conditions, the potential for increased eutrophication due to climate change remains a concern. Understanding the spatial distribution of pollutants and identifying their sources is crucial for developing effective management strategies to protect the marine environment.

About Air Quality Assessment using Time Series Analysis Techniques on Air Particles Data

This study investigates the dynamics of air pollution caused by particulate matter (PM10 and PM2.5) in Omsk, Russia. Data collected from the Sensor.Community network between 2021 and 2023 were analyzed using time series analysis methods. The findings reveal a subtle weekly seasonality in the data, potentially attributed to fluctuations in industrial activity and traffic patterns throughout the week. Additionally, a concerning trend of deteriorating air quality was identified, characterized by increasing annual average concentrations of PM10 and PM2.5. The study employed various time series analysis techniques, including Empirical Cumulative Distribution Function (ECDF), Seasonal and Trend decomposition using Loess (STL), and autocorrelation function (ACF) analysis. These methods allowed for the visualization and examination of data distribution, trend identification, and seasonality detection. Furthermore, the Mann-Kendall test confirmed a statistically significant trend of increasing pollution levels over the three-year period. To enhance the accuracy of spatial analysis, data interpolation was performed using kriging with a Gaussian model. This method, compared to simple interpolation, provides a more precise assessment of the spatial distribution of pollutants. The study also implemented Long Short-Term Memory (LSTM) for forecasting hourly pollution values. LSTM exhibited promising results, demonstrating its potential for developing early warning systems, particularly when combined with meteorological data such as wind forecasts. The results highlight the need for effective measures to mitigate air pollution in Omsk and emphasize the importance of utilizing data from diverse sources, including official air quality monitoring stations, to ensure accuracy and reliability in air quality assessments.

Assessment of the urban pollution island intensity in Rome (Italy) from in-situ PM measurements

The Urban Pollution Island (UPI), describing the temporal and spatial distribution of pollutants’ concentration attributed to the presence of urban features and activities, is one of the major problems affecting urban areas and has become more severe with rapid urbanization. To correctly evaluate the UPI Intensity (UPII), i.e., the difference in pollution concentration between the urban agglomeration and its rural surroundings, it is crucial to carefully select rural and, above all, urban reference stations, as local factors such as orography, location of the air quality monitoring stations, and street orientation can significantly impact UPII values. In this work, the UPII in Rome (Italy) is determined using daily-averaged concentrations of PM10 and PM2.5 collected by in-situ stations over the period 2018–2023. Three different methods for the assessment of UPII are tested and compared by varying the sub-set of selected urban stations, according to their environmental classification. The approach proposed will have significant implications on the management of urban environment and on the tailored design of urban air quality improvement strategies.Results show slight differences in the monthly-averaged concentrations of both PM10 and PM2.5 between the “urban traffic” and “urban background” stations, suggesting that the proximity of the emission sources to the monitoring stations moderately influences the concentrations, potentially due to limited ventilation within street canyons, which can inhibit mixing processes. The annual variation of UPII reveals that PM10 is more sensitive to the selection of the stations particularly during winter, when the differences between the three assessment methods reach 100%. Our findings also indicate that, in the case of Rome, using the largest number of stations available in the urban area could enhance the UPII evaluation, taking into consideration the urban structure and the specific characteristics of local emission sources. The results presented here, although related to a single city, demonstrate that the selection of urban stations for the evaluation of UPII is not straightforward and requires further investigation.

Quality evaluation and health risk assessment of karst groundwater in Southwest China

Groundwater in karst regions is of immense value due to its vital support for regional ecosystems and residents' livelihoods. However, it is simultaneously threatened by multi-source pollution from agricultural non-point sources, industrial and domestic point sources, and mining activities. This study focuses on the Guangxi of China, which features typical karst topography, aiming to thoroughly assess the groundwater quality and related health risks in Guangxi, especially identifying the impacts of various key pollution sources on the groundwater environment. A total of 1912 groundwater samples were collected, covering an area of approximately 237,600 km2. The spatial distribution of pollutants was analysed using the Nemeroww index method and Kriging interpolation, while multivariate statistical and cluster analysis methods were employed to identify the main types of pollution sources. Furthermore, based on the human health risk assessment model of the U.S. Environmental Protection Agency (US EPA), a risk assessment was conducted for key pollutants. The results revealed widespread heavy metal contamination in Guangxi's groundwater, particularly with concentrations of Mn, As, Al, Pb reaching up to 9.4 mg/L, 2.483 mg/L, 37.95 mg/L, 4.761 mg/L, respectively, significantly exceeding China's national Class III groundwater quality standards. Cluster analysis indicated that mining and industrial activities are the primary sources of pollution. The health risk assessment demonstrated that these activities pose a significant risk to public health. The aim of this study is to provide a scientific basis for the protection of the groundwater environment in Guangxi and other karst areas, the formulation of pollution prevention and control strategies, and the optimization of urban and industrial land use layouts. Future research should focus on advanced isotopic and molecular biological techniques to trace pollution sources more precisely and evaluate the effectiveness of pollution control measures.

Driving Factors Analysis for Spatial-Temporal Distribution of Organic Pollutants in the Yangtze River Delta Based on the Optimal-scale Geographical Detector

Site contamination has caused serious harm to human health and the ecological environment, so understanding its spatial and temporal distribution patterns is the basis for contamination assessment and site remediation. For this reason, this study analyzed the spatial-temporal distribution patterns of organic pollutants and their driving factors in the Yangtze River Delta based on site sampling data using the optimal-scale geographical detector. The analysis results showed that:① There was a significant scale effect in the spatial distribution of organic pollutants in the Yangtze River Delta, and its optimal geographic detection scale grid was 8 000 meters. ② The main control factor of the spatial distribution of pollutants in the Yangtze River Delta originated mostly from the biological field, followed by the chemical field. ③ At the depth of 0-20 cm of soil, the explanatory power of sucrase content, urease content, microbial nitrogen amount, total nitrogen content, and cation exchange amount were stronger for the spatial distribution of organic pollutants. At the soil depth of 20-40 cm, the factors with stronger explanatory power on the spatial distribution of organic pollutants were soil moisture, population, and total nitrogen content. With the deepening of soil depth, the explanatory power of the factors of the hydrodynamic field increased. ④ Population, total nitrogen content, and polyphenol oxidase content had stronger explanatory power for the spatial distribution of organic pollutants in the spring. The spatial distribution of organic pollutants was more complex in autumn, and the factors showed stronger enhanced-nonlinear and enhanced-bi phenomena.

Vertical distributions of VOCs in the Tibetan Plateau background region

Exploring the vertical variation in volatile organic compounds (VOCs) in background regions can provide information on the spatial distribution of pollutants, providing a scientific basis for atmospheric pollution prevention and control strategies. From 15 August to 5 September 2023, at the Southeast Tibet Mountain Comprehensive Environmental Observation Station (SETS), a tethered balloon was used to sample VOCs every 100 m from the ground to 1000 m. A total of 403 air bag samples were collected, and 39 vertical profiles of VOCs were obtained. Ninety-two VOC species were detected. The VOC concentration at the SETS did not change significantly vertically, and the average VOC concentration was 11.1 ± 2.4 ppbv. The main components were alkanes (51.4 %), alkenes (18.7 %), and halohydrocarbons (18.1 %). There was no obvious diurnal change in VOCs and no significant difference between the different layers. When the surface VOC concentration was less than 10 ppbv, the concentrations, components, and sources of VOCs were evenly distributed vertically, and the main sources of VOCs at different heights were vehicle exhaust and background. When the surface VOC concentration exceeded 10 ppbv, the VOC concentration gradually decreased with height. The proportion of alkanes in surface VOCs increased, and the source was mainly vehicle exhaust. This study confirmed that VOCs are vertically homogeneous in the background of the Tibetan Plateau, emphasizing the importance of vehicle emissions as a potential source of VOCs.摘要研究背景区域挥发性有机物 (VOCs) 的垂直变化规律, 可以提供污染物的空间分布信息, 为制定大气污染防治策略提供科学依据. 2023年夏季, 利用系留气艇对藏东南背景区域VOCs垂直分布规律进行研究, 共获取92种VOC的39条垂直廓线. 结果表明, VOC浓度垂直分布均一, 平均浓度为11.1 ± 2.4 ppbv; 主要成分为烷烃 (51.4 %), 烯烃 (18.7 %) 和卤代烃 (18.1 %). 地表VOC浓度小于10 ppbv时, 不同高度VOCs的主要来源为机动车尾气和背景贡献. 而地表VOC浓度超过10 ppbv时, 烷烃占比增加, 来源以机动车尾气为主. 该研究证实了青藏高原VOC在垂直方向上分布均一, 强调了机动车排放作为VOCs潜在来源的重要性.

Residual neural network with spatiotemporal attention integrated with temporal self-attention based on long short-term memory network for air pollutant concentration prediction

Accurate prediction of air pollutant concentrations can be effective in controlling and preventing air pollution, which is crucial for both the government's policy response and the public's reasonable ability to avoid air pollution. In this study, we propose a model based on the principle of big data correlation and deep learning techniques for predicting the concentration of air pollutant concentrations. The proposed model, named STARes-SaLSTM, consists of a residual neural network with spatiotemporal attention (STAResNet) and a temporal self-attention based on long short-term memory network (SaLSTM). The spatiotemporal attention module in STAResNet enhances the original input information by weighting it in both spatial and temporal dimensions, meanwhile the features of the spatial distribution of pollutant and meteorological information across many cities are deeply extracted using ResNet. A novel self-attention method known as temporal self-attention is developed to extract the temporal dependency of air pollutants. LSTM is used as a sequence encoder in this temporal attention method to calculate queries, keys, and values to acquire a more comprehensive temporal dependence than is possible with normal self-attention. The STARes-SaLSTM model successfully predicts air pollution concentrations in the target city for the future by extracting spatiotemporal correlation of feature sequence. In comparison to previous neural network models and conventional methods, the model increases the accuracy of pollutant concentration prediction. The suggested prediction model works well for the one to 3 h prediction job, with a root mean square error (RMSE) ranging from 6.716 to 11.648. Furthermore, even for the 1–24 h prediction assignment, we executed multi-scale prediction in the target city and obtained a reasonable performance, with an average RMSE value of 20.576.

A drone-based approach to enhance spatial insight into surrounding air pollutant distributions for healthier indoor environments

Current investigations into the interplay between indoor and outdoor air quality in buildings often rely on limited measurements of a small subset of air pollutants taken outside the structure. Implicit in this methodology is the assumption of a homogeneous distribution of air pollutants around the building, a premise that may not accurately reflect real-world conditions. This study introduces a more accurate and consistent approach to comprehending air pollutant distributions around buildings through systematic measurements conducted using a drone platform equipped with essential air quality sensors. Employing a fully autonomous drone, the study adopts a parametric approach to collect numerous air pollutant measurements around buildings with unconventional shapes. The resulting air pollutant maps offer comprehensive insights into the spatial distribution of pollutants around the building. A detailed analysis, achieved by dividing the building surroundings into different zones, revealed that concentrations of NO2, CO, PM10, and PM2.5 on the inward facade of the ¾ torus-shaped building were 12.64%, 14.49%, 7.66%, and 15.77% higher, respectively, than in the other measured areas around the building. These findings highlight that air pollutants around the building are notably influenced by the building's geometry, challenging the assumption of homogeneity. Instead, pollutants tend to accumulate at specific points, underscoring the significance of considering these factors in air quality assessments. Furthermore, the noted uneven distribution of pollutants around the building has distinct effects on various sections within the structure, as revealed by indoor measurements. Bureaus on the inward facade, where higher levels of outdoor pollutants are measured, can exhibit up to 44% more indoor air pollutants than those on the other side. This study not only contributes to a nuanced understanding of air pollutant distributions but also showcases the superior potential of drone platforms in advancing such research.

Health risk assessment of municipal solid waste incineration emissions based on regression analysis

This study examined the potential health risks posed by the operation of 96 waste-to-energy (WtE) plants in 30 cities in the Bohai Rim of China. Utilizing a sophisticated simulation approach, the Weather Research and Forecasting (WRF) model coupled with the California Puff (CALPUFF) model, we obtained the spatial distribution of pollutants emitted by WtE plants in the atmosphere. Hazard indices (HI) and cancer risks (CR) were calculated for each plant using the United States Environmental Protection Agency's recommended methodologies. The results indicated that both HIs and CRs were generally low, with values below the accepted threshold of 1.0 and 1.0 × 10−6, respectively. Specifically, the average HI and CR values for the entire study area were 2.95 × 10−3 and 3.43 × 10−7, respectively. However, some variability in these values was observed depending on the location and type of WtE plant. A thorough analysis of various parameters, such as waste composition, moisture content, and operating conditions, was conducted to identify the factors that influence the health risks associated with incineration. The findings suggest that proper waste sorting and categorization, increased cost of construction, and elevated height of chimneys are effective strategies for reducing the health risks associated with incineration. Overall, this study provides valuable insights into the potential health risks associated with WtE plants in the Bohai Rim region of China. The findings can serve as useful guidelines for law enforcement wings and industry professionals seeking to minimize the risks associated with municipal solid waste (MSW) management and promote sustainable development.

Implementation of a GEOAI model to assess the impact of agricultural land on the spatial distribution of PM2.5 concentration

Air pollution is considered one of the major environmental risks to health worldwide. Researchers are making significant efforts to study it, thanks to state-of-art technologies in data collection and processing, and to mitigate its effect. In this context, while a lot is known about the role of urbanization, industries, and transport, the impact of agricultural activities on the spatial distribution of pollution is less studied, despite knowledge about emissions suggest it is not a secondary factor. Therefore, the aim of this study was to assess this impact, and to compare it with that of traditional polluting sources, harvesting the capabilities of GEOAI (Geomatics and Earth Observation Artificial Intelligence). The analysis targeted the highly polluted territory of Lombardy, Italy, considering fine particulate matter (PM2.5). PM2.5 data were obtained from the Copernicus-Atmosphere-Monitoring-Service and processed to infer time-invariant spatial parameters (frequency, intensity and exposure) of concentration across the whole period. An ensemble architecture was implemented, with three blocks: correlation-based features selection, Multiscale-Geographically-Weighted-Regression for spatial enhancement, and a final random forest classifier. Finally, the SHapley Additive exPlanation algorithm was applied to compute the relevance of the different land-use classes on the model. The impact of land-use classes was found significantly higher compared to other published models, showing that the insignificant correlations found in the literature are probably due to an unfit experimental setup. The impact of agricultural activities on the spatial distribution of PM2.5 concentration was comparable to the other considered sources, even when focusing only on the most densely inhabited urban areas. In particular, the agriculture's contribution resulted in pollution spikes rather than in a baseline increase. These results allow to state that public policymakers should consider also agricultural activities for evidence-based decision-making about pollution mitigation.

New insight into the spatiotemporal distribution and ecological risk assessment of endocrine-disrupting chemicals in the Minjiang and Tuojiang rivers: perspective of watershed landscape patterns.

This study evaluated the pollution characteristics, spatiotemporal distribution, and ecological risks of eight endocrine-disrupting chemicals (EDCs) in the Minjiang and Tuojiang rivers. Utilizing 3S technology (ArcGIS, remote sensing, GPS) and Fragstats, the research calculated eight landscape pattern indices related to land use types along the Minjiang river and established correlations between landscape factors and EDC distribution through stepwise multiple regression. The results indicated that bisphenol A (BPA) and nonylphenol (NP) were the most concerning EDCs, with detection frequencies of 97-100% and peak concentrations up to 63.35 ng L-1, primarily located in the middle and lower reaches of the Minjiang river and the upper reaches of the Tuojiang river. There was a significant correlation between the spatial distribution of pollutants and landscape patterns, where increased fragmentation, a higher number of patches, and complex patch shapes within a 10-kilometer buffer zone were associated with elevated levels of river pollution. By integrating four classical mathematical models to fit curves for acute and chronic toxicity data of BPA and NP, the findings suggested that BPA posed a higher ecological risk. This interdisciplinary research provided essential theoretical insights for investigating river pollution and its influencing factors, offering a new perspective on simultaneous river pollution control, urban functional zoning, and adjustment of watershed landscape spatial patterns from an urban planning standpoint.

Spatial Distribution of Pollutants and Risk Assessment of Heavy Metals in Farmland Groundwater around a Traditional Industrial Park—A Case Study of Shifang City, Southwestern China

Spatial Distribution of Pollutants and Risk Assessment of Heavy Metals in Farmland Groundwater around a Traditional Industrial Park—A Case Study of Shifang City, Southwestern China

The development and application of a novel helicopter-based airborne platform for near-surface monitoring and sampling of atmospheric pollutants

This study presents a novel approach utilising helicopters as platforms for real-time monitoring of air pollutants and routine canister sampling to enhance our understanding of air pollution in the Greater Bay Area (GBA). By employing this method, local spatial and vertical distribution of various air pollutants (including CO, NO, NO2, O3, PM2.5 and VOCs) in Hong Kong could be obtained within hours for fast snapshots of air pollution.The investigation involved hovering ascent and descent operations above Tap Mun Island, revealing no indication of self-contamination from engine exhaust during the hovering process. The analysis of air pollutant spatial distribution in and around Hong Kong through multiple trips showed higher concentrations of O3 and PM2.5 in the northern region on days with episodic pollution, while urban areas exhibited elevated levels of NO2 and CO concentration. In contrast, Tap Mun Island as the background area demonstrated relatively low pollutant concentrations. The vertical distribution of concentrations of VOC species with high ozone formation potential revealed minor variations at sampling altitudes of 500 ft and 1000 ft over the waters around Hong Kong. The novel monitoring protocol using helicopters as platforms proves to be an effective approach for pollution episode investigations, offering valuable insights in the formation mechanisms and transport pathways of O3. The findings of the heterogenous spatial distribution of O3 and its precursors underscore the significance of ongoing monitoring efforts and policy development to combat air pollution in the dynamic GBA and similar regions worldwide.

Research on the Transport of Typical Pollutants in the Yellow Sea with Flow and Wind Fields

In this study, we developed a transport model for typical pollutants in the Yellow Sea using the Lagrangian particle tracking method to analyze the trajectories of fish feed, a common pollutant in the Yellow Sea. The model incorporates the influence of ocean currents and surface winds on pollutant transport and utilizes a series of numerical experiments to simulate pollutant transport. Through statistical analysis of the numerical experiment results, we identified characteristic circles that represent the pollutant distribution patterns. Furthermore, based on the current and wind information within these characteristic circles, we derived an empirical formula to describe pollutant distribution. This formula enables us to predict the spatial distribution of pollutants using available current and wind data. Using this empirical formula, we designed an effective path to avoid pollutant contamination. This approach not only optimizes the utilization of computational resources within the study area but also contributes to the rational planning of navigation routes for aquaculture vessels. Overall, our study provides valuable insights into the transport behavior of fish feed pollutants in the Yellow Sea. The establishment of the empirical formula and the design of effective routes to avoid pollution contribute to the efficient management of pollution and facilitate the planning of marine activities in the region.

Spatial distribution and source identification of phthalates and organochlorine compounds in Galician mussels (NW Spain)

The spatial distribution of five phthalate esters (PAEs) and 17 organochlorine compounds (OCs) in wild and raft mussels from Galician littoral collected in 2020, and its relationship with anthropogenic activities was studied in this work. The Rías de Foz and Muros-Noia were the most polluted by PAEs, while the Rías de Ferrol and Barqueiro by PCBs. The highest levels of all contaminants were present in wild mussel, except DEHP that were predominant in raft mussel. The levels of most PAEs were negatively correlated with levels of the lower chlorinated PCBs and OCPs. The spatial distribution of pollutants confirmed by PCA was affected by the proximity to anthropogenic sources, phthalates by urban wastewater and PCBs by industrial inputs, mainly. The study of human exposure assessment suggests that Risk Quotient values were < 1, so the consumption of Galician raft mussels did not pose a risk to human health.

Physical informed neural network improving the WRF-CHEM results of air pollution using satellite-based remote sensing data

Accurate measurement of air gases concentration are crucial for effective air quality management and minimizing the harm caused by pollution events, as well as providing guidance for healthy travel. We developed develop a physical informed deep learning model that combines a traditional atmospheric chemical transport model and a data-based deep learning model. The model uses multisource observation data to constrain and optimize the results of atmospheric pollution gases concentrations. The accuracy and spatial resolution in central and eastern China (20–45°N, 100–125°E) have been improved compared to the original Weather Research and Forecasting model coupled to Chemistry (WRF-CHEM) results by incorporating constraints from ground-based and satellite observations. Compared with the WRF-CHEM results, our model increases the verification Pearson coefficient (R) of NO2 and CNEMC sites from 0.56 to 0.8, and the RMSE decreases from 25.33 to 15.54 μg cm−3. For O3 results, the model increased the verification R of the CNEMC from 0.57 to 0.76, and the RMSE decreased from 24.55 to 20.22 cm−3. We employed independent MAXDOAS monitoring to authenticate the HCHO results. The R was 0.78, while the validation results for satellite HCHO and MAXDOAS exhibited a R of 0.63 and the validation results for WRF-CHEM and MAXDOAS demonstrated a R of 0.11. Additionally, the RMSE was 6.97E15 mole cm−2, whereas the validation results for satellite HCHO and MAX-DOAS had an RMSE of 7.16E15 mole cm−2. By utilizing satellite remote sensing to effectively capture the spatial distribution of pollutants, and by combining satellite data with other sources of monitoring data and optimizing our models, we have improved the accuracy of our results and have overcome the limitation of cloud coverage. As a result, we are now able to better understand the space-time distribution of pollutants in different regions.

Spatio-Temporal Dynamics of Non-Point Source Pollution in Jiulong River Basin (China) Using the Soil &amp; Water Assessment Tool Model in Combination with the GeoSOS-FLUS Model

Water pollution, particularly non-point source pollution, poses a significant environmental challenge in river basins around the world. This complex and dynamic process is influenced by both human activities and natural processes. In this study, a quantitative analysis of ammonia-N and total phosphorus (TP) levels in the North Stream of the Jiulong River basin, China from 2010 to 2018 was conducted using the Soil &amp; Water Assessment Tool (SWAT) model. The model was able to facilitate the simulation of spatio-temporal dynamics of concerned pollutants. Additionally, the GeoSOS-FLUS model was integrated with SWAT to predict land use patterns in 2040 and assess their impact on pollutant dynamics. The results demonstrated that the SWAT model effectively simulated the spatial and temporal dynamics of concerned pollutants in the study area, with satisfactory R2 and NS values for river discharges and pollutant loads. Notably, 2016 exhibited significant pollution levels, particularly in March. The study revealed distinct sources of ammonia-N and TP, originating from aquatic animal breeding areas and industrial wastewater discharge, respectively. Moreover, land use patterns influenced the spatial distribution of pollutants. These findings serve as a crucial data foundation for future endeavors in controlling and mitigating non-point source pollution in the Jiulong River basin.

Spatial distribution and water quality index of Lahore Canal, Pakistan

Abstract The Lahore Canal (LC) in Lahore city of Pakistan, with the discharge of 402 cusecs, runs along the city's centre. With rapid urbanization and population growth, the LC water is deteriorating. This study determined the water quality index and spatial distribution of pollutants. Three months of sampling from six separate locations were performed. Water quality parameters were analysed. The results indicated that pH, solids, turbidity, hardness, alkalinity, and chlorides were within guidelines, but DO BOD and nitrogen were beyond guidelines. Moderate BOD values (2.24–8.06 mg/L) and low DO values (0.13–3.56 mg/L) indicated a low oxygen environment. Heavy metal concentration was as follows: Fe &amp;gt; Pb &amp;gt; Cr &amp;gt; Cu. The Pearson correlation coefficient indicated ± poor to moderate (0.3–0.7) correlation. The ANOVA result supported the alternative hypothesis, i.e., the pollutants originated from the same source. Principal component analysis and cluster analysis showed three primary sources for the different pollutants based on loading of variance and Euclidean distance, respectively. The WQI of LC at all locations was above 300, indicating that LC water is not suitable for any usage. The spatial distribution of parameters indicated the effects of urbanization and commercialization (small household industries) at Location-4. The poor water quality of LC needs immediate government attention.

Characteristics of the spatial distribution of pollutants in the Shkumbin River, using the Inverse Distance Weighting Method

The Shkumbin River is one of the largest in the country, with a length of 181 km, a catchmentarea of 2444 km2, and an average height of its sources of 753 m. For the specificity of the ShkumbinRiver and the importance of the use of its waters, it is necessary to continuously study the level of waterquality and the level of pollution, especially in its lower reaches, where agricultural and urban activitiesare concentrated and industrial.The study was carried out in the period 2021–2022, in the Shkumbin River watershed, and eight stationswere selected as representative points of the main stream of the Shkumbin River. In the context ofindustrial and urban development, the preservation of the natural environment takes on specialimportance. The solution to this problem requires the hydrochemical study of water sources, which isnecessary for the control of their condition, and the study of compounds and polluting phenomena. Inorder to study the distribution of organic pollutants and heavy metals and obtain pollutant distributionpictures using the ArcGIS system, the Inverse Distance Weighting Method was used.