Level Of Water Pollution Research Articles

Multivariate Analysis of Water Quality in the Seybouse River: Implications for Pollution Management

ABSTRACTHeavy metal contamination in water bodies is a pervasive and persistent environmental challenge in many parts of the world, especially in developing countries. This study investigates the use of multivariate analysis methods for monitoring variations in water quality along a spatial gradient and for the interpretation of pollution levels at different sampling sites. We assessed the water quality of the Seybouse River and identified possible sources of pollution using three complementary multivariate analysis techniques (PCA, NMDS, and K‐means clustering). The results indicate a longitudinal gradient in water quality associated with industrial and agricultural activities in the middle and lower Seybouse River. Physico‐chemical and heavy metal analyses show high water turbidity with elevated concentrations of iron and chromium. We show that the contamination stems from four different sources, which can be categorized into different pollution levels. Our results suggest that complementary multivariate methods are a robust approach to identifying and categorizing significant sources of pollution in rivers, enabling the development of future successful water quality management strategies based on water pollution levels. This study highlights the importance of monitoring water quality and taking effective measures to control and mitigate pollution from various sources to ensure the safety of the environment and human health.

Estimating Concentration of Suspended Solids Load in Estuary System Utilizing Sentinel 2A: A Case Study of Cipatujah Beach, Indonesia

Abstract Total Suspended Soil (TSS) and Total Dissolved Solid (TDS) refer to materials in the form of sediment and organic substances suspended and dissolved in water which are used as water quality parameters to understand sediment transport, the health of aquatic ecosystems, and technical problems. Concentrations in significant amounts in water areas can be caused by the dynamics of the earth’s surface and human activities, the resulting negative impacts will reduce the quality of the ecological system and indicate the presence of pollutants. This research was conducted to determine the concentration of Total Suspended Soil (TSS) and Total Dissolved Solid (TDS) in estuary waters at Cipatujah Beach, West Java, Indonesia using remote sensing satellite imagery, namely Sentinel-2A. The research carried out focused on testing samples for Total Dissolved Solid (TDS) content, which is one indicator of the level of water pollution that is often analyzed. From the results of sample tests carried out using a TDS meter, it shows that the TDS content in Cipatujah Beach is above 400 ppm so it is not suitable for use as drinking water, and further laboratory tests need to be carried out to determine the TSS content in Cipatujah Beach water.

Neutrosophic Approach to Water Quality Assessment: A Case Study of Gomati River, the Largest River in Tripura, India

This study addresses the complexity of assessing river water quality, a multifaceted process influenced by numerous water quality parameters (WQPs) characterized by inherent uncertainties and diverse judgment information from decision-makers. These uncertainties and diverse judgment information can be effectively represented and simulated using Neutrosophic sets. In this study, we propose an effective water pollution rating system, the Neutrosophic water quality index (NWQI), to derive a water pollution score (NWQI-score) for rating water pollution levels. We demonstrate the application of our methodology through an assessment of water quality indices for rating pollution in the Gomati River, the largest river in Tripura, India. Using the NWQI, we highlight the NWQI score as a pivotal indicator for evaluating the river's water quality. For assessing water quality, we consider six crucial parameters, namely Total hardness, Biochemical oxygen demand, Total suspended solids, Electrical conductivity, pH, and total dissolved solids, sampled at five strategic sites along the river. Sampling was conducted from January 2024 to July 2024, guided by the distribution of waste discharge points. Finally, we conclude the study, summarizing findings and suggesting future research directions.

Assessing anthropogenic influences on the water quality of Gomati River using an innovative weighted fuzzy soft set based water pollution rating system

The increasing pollution of river systems poses a significant challenge to water quality management and public health. This study was conducted to evaluate the water quality of the Gomati River in India, focusing on the impact of anthropogenic activities on pollution levels across different seasons. The primary objective was to develop a comprehensive assessment model using an effective weighted fuzzy soft expert system (WFSES) to derive a weighted water pollution score (WWP-score) for rating water pollution levels based on various water quality parameters. To achieve this, we employed a fuzzy soft set (FSS) methodology integrated with a weighted fuzzy soft set (WFSS) model to analyze water quality indices at six sampling stations along the Gomati River, the largest river in Tripura, India. Originating from the Raima and Sarma streams, the river flows westward through key regions, providing 249.39 million cubic meters of water annually, essential for drinking, agriculture, fishing, and transportation. The study utilized crisp data summaries of key water quality parameters, including pH, total dissolved solids, total suspended solids, electrical conductivity, biochemical oxygen demand, dissolved oxygen, total hardness, chloride, total alkalinity, and total coliform, across the pre-monsoon, monsoon, and post-monsoon seasons from May 2022 to April 2023, guided by the distribution of waste discharge points. Fuzzy membership functions were defined for these parameters, and the root mean square (RMSQR) operation was used to combine multiple FSSs. The results revealed that the WWP-scores, a measure of water quality, ranged from 0.744 to 0.872 across the sampling stations, with Srimantapur exhibiting the highest level of pollution and Udaipur showing comparatively better quality. These scores indicate that water quality at all sites falls into the poor category, with Srimantapur experiencing the most severe contamination due to high levels of coliform bacteria and organic pollutants. The analysis highlights the impact of domestic wastewater and agricultural runoff on the river's health. This research offers a novel application of FSS and WFSS theory in environmental management, providing a robust framework for evaluating and ranking water quality across multiple parameters. The findings are valuable to the scientific community as they offer detailed insights into the pollution dynamics of the Gomati River and propose a methodological approach for assessing water quality in other river systems facing similar challenges. The study's results underscore the need for targeted pollution control measures and continuous monitoring to safeguard water resources and public health.

Physical Characterization of Microplastic Pollutants in Groundwater

The rapid urbanization and growth of the population have led to both the ever-increasing demand for water consumption and in tandem the levels of water pollution in Malaysia. Rapid development has produced great amounts of human waste including domestic and industrial which inevitably ends up in the water resources. Contamination of microplastic (MP) pollutants in groundwater has been a huge concern nowadays since groundwater often becomes a source of drinking water. Therefore, these MP (plastic particles < 5 mm) are concerned with having a negative health impact on human health through the consumption of drinking water that has been contaminated. While MPs in groundwater likely affect human health, only a handful of studies have examined the abundance and movement of MPs in groundwater. Because of that, this study was done in two different sampling stations of groundwater that were sources of drinking water. This study indicates a total of 182 microplastics were found in the sampled groundwater. Physical characterization has been done and they were divided into four categories according to their shapes. Fibers clearly prevailed in all water samples. Despite 10 different colors of the microplastics being identified, most of the MPs are comprised of transparent plastics. Moreover, polypropylene (PP) and polyethylene (PE) were found to be predominant in the water samples. This study contributes to filling the knowledge gap in the field of emerging microplastic pollution in drinking water sources specifically groundwater, which is of concern due to the potential exposure of MPs to humans.

Investigation of structural, optical, and magnetic properties of NiFe2O4 for efficient photocatalytic degradation of organic pollutants through photo fenton reactions

Nowadays, water pollution generated from textile effluents is one of the major problems for the human race and ecology. Hence, development of sustainable strategies to lower the water pollution level has become a burning need. In this regard, the present study focuses on the preparation of nano catalyst NiFe2O4 to catalyze the chemical reactions on industrial organic dyes for their fast cleansing from water. By sol-gel auto-combustion technique, NiFe2O4 nanoparticles were synthesized and exposed to thermal process at temperatures of 400, 600, and 800 °C. Highly crystalline phase with spinel cubic structured NiFe2O4 was formed with a crystal size of 18.71 nm, which was confirmed by XRD analysis. The FTIR spectra showed two fundamental absorption bands in the range 597.80–412.59 cm−1, which are the characteristics of tetrahedral M − O and octahedral M − O bond in NiFe2O4. The surface morphology of calcined NiFe2O4 was investigated by scanning electron microscope (SEM). The nanoparticle size analyzer exhibited that the synthesized NiFe2O4 nanoparticles had an average particle size of ∼ 291.3 nm. Three stage decomposition patterns were observed for NiFe2O4, which was analyzed by a temperature programmed STA. Zeta potential analyzer showed that the synthesized sample S1 and S2 were stable in the dispersion medium. Also, NiFe2O4 exhibited optical band gap energies for direct band transitions within the visible spectrum measured to be 1.43–1.45 eV, rendering them effective as photocatalysts under sunlight. The samples showed magnetic measurements by VSM with saturation magnetization, coercivity, remnant magnetization value of 66.81 emu/g, 4.13 Oe and 12.94 emu/g, respectively. The synthesized photocatalyst, NiFe2O4, at 400 °C, significantly degraded three toxic organic pollutants—Methylene blue, Rhodamine B, and Congo Red—under visible light through ‘Photo-Fenton’ reaction mechanisms. Among the three dyes, Methylene Blue exhibited the highest degradation percentage with a rate constant of 0.0149 min−1 and followed pseudo-first-order kinetic model.

Ecological and Health Risk Assessment of Heavy Metals in Groundwater within an Agricultural Ecosystem Using GIS and Multivariate Statistical Analysis (MSA): A Case Study of the Mnasra Region, Gharb Plain, Morocco

Assessing groundwater quality is essential for ensuring the sustainability of agriculture and ecosystems. This study evaluates groundwater contamination by heavy metals (HMs) using GIS approaches, multivariate statistical analysis (MSA), pollution indices (heavy metal pollution index (HPI), metal index (MI), degree of contamination (Cd), ecological risk index (ERI), and pollution index (PI)), and human health risk assessment (HHRA). The results revealed significant variations in heavy metal concentrations across the study area, with the highest concentrations found in the southern and southeastern parts, characterized by intense agricultural activities and uncontrolled landfills. Statistical analyses indicated both natural and anthropogenic sources of contamination. Pollution indices showed medium to high water pollution levels, with HPI values ranging from 20.23 to 128.60, MI values from 3.34 to 12.17, and Cd values from 2.90 to 11.73, indicating varying degrees of contamination. ERI values suggested a low ecological risk across all samples. However, health risk assessments highlighted significant non-carcinogenic and carcinogenic risks, particularly for children, with TCR values for some heavy metals like Ni and Cr exceeding safe limits, indicating potential health hazards. The findings provide a valuable framework for policymakers to develop targeted strategies for mitigating groundwater contamination and ensuring sustainable water quality management.

Spatial-temporal distribution characteristics of surface water pollutants and their potential sources in Ngari, China.

The Ngari region has many important rivers and is critical to water resource security and water resource continuity in China and even in adjoining Asian countries. However, the spatial distribution and monthly variation in local water quality have been poorly understood until recently. In this study, the spatial-temporal variations of 12 water quality parameters, including pH, dissolved oxygen (DO), permanganate index (IMn), chemical oxygen demand (COD), five-day biochemical oxygen demand (BOD5), ammonia nitrogen (NNH3), total nitrogen (Ntotal), total phosphorus (Ptotal), copper (Cu), fluoride (F), arsenic (As) and cadmium (Cd), were determined from samples collected monthly at 22 water cross-sectional sites in the Ngari region in 2020. The surface water pollution in the southern Ngari region was the most serious, and the water pollution level in winter was higher than that in the other seasons. As (0.0781 ~ 0.6154 mg/L) and F (1.05 ~ 4.64 mg/L) were the main exceedance factors derived from the recharge of high arsenic and fluoride geothermal water and weathering of As and F-bearing minerals. The hazard quotient and carcinogenic risk for As and F at the five contaminated sampling sites indicated potential health risks and even carcinogenicity to local populations. The hydrochemistry types of the lakes and rivers in the Ngari region were mainly chloride water and carbonate water. The results from this study can provide a scientific basis for the prevention and control of surface water pollution in the Ngari region and contribute to subsequent research on the ecology of water bodies.

Predicting Coastal Water Quality with Machine Learning, A Case Study of Beibu Gulf, China

Coastal ecosystems are facing critical water quality deterioration, while the most convenient passage to the South China Sea, Beibu Gulf, has been under considerable pressure to its ecological environment due to rapid development and urbanization. In this study, we characterized the spatiotemporal change in the water quality in Beibu Gulf and proposed a machine learning approach to predict the water pollution level in Beibu Gulf on the basis of 5-year (2018–2022) observation data of ten water quality parameters from ten selected sites. Random forest (rf) and linear algorithms were utilized. Results show that a high frequency of exceedance of water quality parameters was observed particularly in summer and autumn, e.g., the exceeding rate of Dissolved Inorganic Nitrogen (DIN) at GX01, GX03, GX06, and GX07 station were 28.2~78.1% (average is 52.0%), 6.0~21.7% (average is 52.0%), 23.0~44.7% (average is 31.9%), and 5.2~33.4% (average is 21.2%), respectively. With regard to the spatial distribution, the pH, Water Salinity (WS), and Dissolved Oxygen (DO) values of stations inside the bay were overall lower than those of corresponding stations at the mouth of the bay and stations outside the bay. The concentrations of Chlorophyll-a concentration (except QZB) and nutrient salts showed a clearly opposite trend compared with the above concerned three parameters. For instance, the average Chl-a value of station GX09 was 22.5% higher than that of GX08 and GX10 between 2018 and 2022. Correlation analysis among water quality factors shows a significant positive correlation (r > 0.85) between Dissolved Inorganic Nitrogen (DIN) and NO3-N, followed by NO2-N and NH4-N, indicating that the main component of DIN is NO3-N. The forecasting results with machine learning also demonstrate the possibility to estimate the water quality parameters, such as chl-a concentration, DIN, and NH4-N in a cost-effective manner with prediction accuracy of approximately 60%, and thereby could provide near-real-time information to monitor the water quality of the Beibu Gulf. Predicting models initiated in this study could be of great interest for local authorities and the tourism and fishing industries.

Assessment of heavy metal pollution level, ecological and human health risks in surface water of Narmada River, India

Assessment of heavy metal pollution level, ecological and human health risks in surface water of Narmada River, India

A new sol-gel fluorescent sensor to track carbonyl compounds

Carbonyl compounds are ubiquitous quality trackers that provide information about food product degradation as well as air and water pollution levels. In addition, they are used as biomarkers for medical diagnoses. With more user-friendly sensors, their fast detection and easy quantification are highly relevant. The synthesis, characterization, and performance assessment of a new sensor based on aniline fluorescence to monitor carbonyls in real time is reported. A cost-effective synthesis using a straightforward sol-gel process led to the construction of a nontoxic silica-based material with high porosity, which can be used with almost no sample preparation. The material exhibits a rapid (< 1 min) fluorescence decrease upon interaction with carbonyl groups. The limit of detection is as low as ca. 5 × 10−4 mol·L−1 for hexanal, while fluorescence extinction occurs at much higher concentrations (5 × 10−1·mol L−1), which enables the sensor to be used with a very broad range of detection. Real-time monitoring is possible since the fluorescence loss correlates with the concentration of carbonyl moieties. The performance was validated in simulating as well as in real media, making this sensor suitable for use in a wide range of applications.

Incidence, identification and antibiotic resistance of Salmonella spp. in the well waters of Tadla Plain, Morocco

Concerns about challenges with water availability in the Tadla Plain region of Morocco have grown as a result of groundwater contamination brought on by human activity, climate change, and insufficient groundwater management. The objective of the study is to measure the number of resistant bacteria in the groundwater of Beni Moussa and Beni Aamir, as well as to evaluate the level of water pollution in this area. 200 samples were therefore gathered from 43 wells over the course of four seasonal campaigns in 2017 and 2018. Additionally, the samples were examined to determine whether Salmonella species were present and if they were resistant to the 16 antibiotics that were tested. Salmonella spp. have been identified in 31 isolated strains in total, accounting for 18.02% of all isolated strains. Data on antibiotic resistance show that 58.1% of Salmonella spp. strains are multidrug-resistant (MDR); 38.7% of Salmonella strains are tolerant to at least six antibiotics, 19.4% to at least nine antibiotics, 9.7% to four to seven antibiotics, 6.5% to at least eleven antibiotics, and the remaining 3.2% to up to twelve antibiotics. A considerable level of resistance to cefepime (61.29%), imipenem (54.84%), ceftazidime (45.16%), ofloxacin (70.97%), and ertapenem (74.19%) was found in the data. Consequently, it is important to monitor and regulate the growth of MDR in order to prevent the groundwater's quality from declining.

Comprehensive river water quality monitoring using convolutional neural networks and gated recurrent units: A case study along the Vaigai River

Effective monitoring of river water quality is required for long-term water resource management. Convolutional Neural Networks and Gated Recurrent Unit-based water quality monitoring (CNGRU-WQM) were used in this investigation to develop a comprehensive monitoring system along the Vaigai River. The system was designed to collect real-time data on several crucial water quality parameters. The collected characteristics encompassed factors like water pollution levels, turbidity, pH readings, temperature, and total dissolved solids, offering a comprehensive view of river water quality. The monitoring system was methodically set up, with sensors strategically positioned at various locations along the river. This ensured that data collection would take place at regular intervals. The CNGRU-WQM model achieved a validation accuracy of 97.86%, surpassing the performance of other state-of-the-art approaches. Particularly noteworthy is the fact that the actual use of this system incorporates real-time warnings, which enable stakeholders to be instantly informed if water quality measurements surpass pre-set criteria. The study's contributions include its efficient river water quality monitoring system, which encompasses a variety of indicators, and its ability to significantly affect environmental conservation efforts by offering a helpful tool for informed decision-making and timely interventions.

Exploring seasonal variability in water quality of Nyabarongo River in Rwanda via water quality indices and DPSIR modelling.

Understanding seasonal variations in water quality is crucial for effective management of freshwater rivers amidst changing environmental conditions. This study employed water quality index (WQI), metal index (MI), and pollution indices (PI) to comprehensively assess water quality and pollution levels in Nyabarongo River of Rwanda. A dynamic driver-pressure-state-impact-response model was used to identify factors influencing quality management. Over 4years (2018-2021), 69 samples were collected on a monthly basis from each of the six monitoring stations across the Nyabarongo River throughout the four different seasons. Maximum WQI values were observed during dry long (52.90), dry short (21.478), long rain (93.66), and short rain (37.4) seasons, classified according to CCME 2001 guidelines. Ion concentrations exceeded WHO standards, with dominant ions being and . Variations in water quality were influenced by factors such as calcium bicarbonate dominance in dry seasons and sodium sulfate dominance in rainy seasons. Evaporation and precipitation processes primarily influenced ionic composition. Metal indices (MI) exhibited wide ranges: long dry (0.2-433.0), short dry (0.1-174.3), long rain (0.1-223.7), and short rain (0.3-252.5). The hazard index values for Cu2+, Mn4+, Zn2+, and Cr3+ were below 1, ranging from 8.89E - 08 to 7.68E - 07 for adults and 2.30E - 07 to 5.02E - 06 for children through oral ingestion, and from 6.68E - 10 to 5.07E - 07 for adults and 6.61E - 09 to 2.54E - 06 for children through dermal contact. With a total carcinogenic risk of less than 1 for both ingestion and dermal contact, indicating no significant health risks yet send strong signals to Governmental management of the Nyabarongo River. Overall water quality was classified as marginal in long dry, poor in short dry, good in long rain, and poor again in short rain seasons.

Responses of zebra and quagga mussels to copper and tribytiltin exposure: Bioconcentration, metabolic and cardiac biomarkers

One of the top ecological priorities is to find sensitive indicators for pollution monitoring. This study focuses on the bioconcentration and responses (condition index, survival, oxygen consumption, heart rates, and oxidative stress and neurotoxic effect biomarkers) of mussels from the Volga River basin, Dreissena polymorpha and Dreissena bugensis, to long-term exposure to toxic chemicals such as tributyltin (TBT, 25 and 100 ng/L) and copper (Cu, 100 and 1000 μg/L). We found that TBT was present in the tissues of zebra and quagga mussels in comparable amounts, whereas the bioconcentration factor of Cu varied depending on its concentration in water. Differences in responses between the two species were revealed. When exposed to high Cu concentrations or a Cu-TBT mixture, quagga mussels had a lower survival rate and a longer heart rate recovery time than zebra mussels. TBT treatment caused neurotoxicity (decreased acetylcholinesterase activity) and oxidative stress (increased levels of thiobarbituric acid reactive substances) in both species. TBT and Cu levels in mussel tissues correlated positively with the condition index, but correlated with the level of acetylcholinesterase in the mussel gills. The principal component analysis revealed three main components: the first consists of linear combinations of 14 variables reflecting TBT water pollution, TBT and Cu levels in mussel tissues, and biochemical indicators; the second includes Cu water concentration, cardiac tolerance, and mussel size; and the third combines weight, metabolic rate, and heart rates. Quagga mussels are less tolerable to contaminants than zebra mussels, so they may be used as a sensitive indicator.

Assessing seasonal changes in water quality using various indices in Chenab River and its tributaries (J&amp;K)

The present study involves the analysis of thirteen physiochemical parameters (pH, temperature, electrical conductivity, total dissolved solids, turbidity, alkalinity, hardness, calcium, magnesium, dissolved oxygen, biological oxygen demand, sulphate and nitrate) and five heavy metals (chromium, zinc, arsenic, lead and cadmium) from Chenab River and its tributaries (Neeru and Bichleri) at Ramban and Doda district, J&amp;K. The analysis was done for two different seasons i.e. summer (June) and winter (December), 2022. The current investigation indicated that all the physiochemical parameters were in the permissible limit in both the seasons except few parameters (Ca, Mg, turbidity). In the analysis of heavy metals in both the seasons, it was found that there was no heavy metal contamination in all sampling sites except the Cr concentration which was found to be higher in all sites (except SXII) in summer season. The detailed analysis involved the calculation of various water quality indices which graded water quality under ‘good’ category as per WQI value whereas CPI and HPI value showed moderate to high pollution level in water during summer season. The study showed the seasonal variation in water quality parameters and thus encourage the need for regular monitoring of water quality to reduce pollution level.

Monitoring of Low Chl-a Concentration in Hulun Lake Based on Fusion of Remote Sensing Satellite and Ground Observation Data

China’s northern Hulun Lake is a significant body of water internationally. The issue of eutrophication has gained prominence in recent years. The achievement of precise chlorophyll-a (Chl-a) monitoring is crucial for safeguarding Hulun Lake’s ecosystem. The machine learning-based remote sensing inversion method has been shown to be effective in capturing the intricate relationship between independent and dependent variables; however, it lacks a priori knowledge and is limited by the quality of remote sensing data sources. The relationship between independent and dependent variables can be more accurately simulated with the use of suitable auxiliary variables. Therefore, three machine learning models—random forest (RF), adaptive boosting (AdaBoost), and extreme gradient boosting (XGBoost)—were established in this study using meteorological observation parameters as auxiliary variables combined with Sentinel-2 satellite image remote sensing band combinations as independent variables and measured Chl-a data as dependent variables. The estimation effects before and after the fusion of meteorological ground observation data were compared, and the best model was used to estimate the spatial–temporal variation trend of Chl-a in the regional water body. The results show that (1) the addition of meteorological parameters as auxiliary variables improved the precision of the three machine models; the decision coefficient (R2) rose by 7.25%, 5.71%, and 7.20%, respectively, to 0.76, 0.66, and 0.73. (2) The concentration of Chl-a in the lake region was projected from June to October 2019 to October 2021 using the RF optimal estimating model of meteorological fusion. The northeast, southwest, and south of the lake were where the comparatively high concentration values of Chl-a were located, whereas the lake’s center had a generally low concentration of the substance. Chromatically, Chl-a typically peaked in August after initially increasing and then declining. (3) The three rivers that feed into the river have varying levels of water pollution, with chemical oxygen demand (COD) and total nitrogen (TN) pollution being the most severe. This is what primarily caused the higher levels of Chl-a in the northeast, southwest, and south. This study is crucial for the preservation and restoration of Hulun Lake’s natural ecosystem and offers some technical support for the monitoring of the lake’s concentration of Chl-a.

Analysis of Soil Samples for its Physicochemical Parameters of Soil in Bawal Block of Haryana

Without a question, the oldest occupation on Earth is agriculture. The soil was virgin in the early stages of evolution, and farming was done very differently than it is now. Growing more food in a less amount of space has made it necessary to plant more crops more frequently and to use omnicides and fertilizers frequently. Excessive use of fertilizers and omnicides negatively impacts soil characteristics, air and water pollution levels. The study of agricultural science or soil science has piqued people's curiosity. An attempt has been made to address the aforementioned issues in this work. The soil in the area being studied has been physicochemical characterized. Key Words: Water Holding Capacity, High-performance Liquid Chromatography, Nutritive index, Fertility index

Recent progress in defect-engineered metal oxides for photocatalytic environmental remediation.

Rapid industrial advancement over the last few decades has led to an alarming increase in pollution levels in the ecosystem. Among the primary pollutants, harmful organic dyes and pharmaceutical drugs are directly released by industries into the water bodies which serves as a major cause of environmental deterioration. This warns of a severe need to find some sustainable strategies to overcome these increasing levels of water pollution and eliminate the pollutants before being exposed to the environment. Photocatalysis is a well-established strategy in the field of pollutant degradation and various metal oxides have been proven to exhibit excellent physicochemical properties which makes them a potential candidate for environmental remediation. Further, with the aim of rapid industrialization of photocatalytic pollutant degradation technology, constant efforts have been made to increase the photocatalytic activity of various metal oxides. One such strategy is the introduction of defects into the lattice of the parent catalyst through doping or vacancy which plays a major role in enhancing the catalytic activity and achieving excellent degradation rates. This review provides a comprehensive analysis of defects and their role in altering the photocatalytic activity of the material. Various defect-rich metal oxides like binary oxides, perovskite oxides, and spinel oxides have been summarized for their application in pollutant degradation. Finally, a summary of existing research, followed by the existing challenges along with the potential countermeasures has been provided to pave a path for the future studies and industrialization of this promising field.

Investigating the impact of artificial intelligence development on water pollution in China

Analysing the impacts of AI development on water pollution is of great significance for improving water environment management measures and promoting global sustainable development. In this study, macro and micro panel data of China from 2008 to 2019 were used to measure the development level of AI at the industry and provincial levels in China, and a panel data regression model was constructed to analyse the effects of AI development on water pollution and regional heterogeneity. The measurement results show that the development level of AI is increasing. Among the six industries, the intelligence level of the manufacturing industry has developed the fastest, by approximately 48.74 times compared with the baseline. At the provincial level, Guangdong Province made the greatest progress in intelligence level, increasing by approximately 23.6 times. The regression results show that AI technology is helpful in reducing the level of water pollution, and this conclusion remains valid after the regression of instrumental variables and robustness testing. In terms of regional heterogeneity, AI technology can significantly reduce the degree of water pollution in eastern and western China. However, the development of AI technology in the central region has no significant impact on water pollution, and a risk of increasing the degree of water pollution exists. The reasons may be that the eastern region has high levels of economic vitality and technological innovation and a strong ability to transform AI achievements, which can significantly improve the water environment. Industrial support policies in the western region are strong, and the application of AI can quickly improve production methods and significantly reduce water pollution levels. In comparison, the levels of economic development, technological innovation, and policy support in the central region need to be strengthened, so the environmental effect of AI is not significant.