Pollution Research Research Articles

A bibliometric analysis of the impact of COVID-19 social lockdowns on air quality: research trends and future directions.

Social lockdowns improved air quality during the COVID-19 pandemic. Governments had previously spent a lot of money addressing air pollution without success. This bibliometric study measured the influence of COVID-19 social lockdowns on air pollution, identified emerging issues, and discussed future perspectives. The researchers examined the contributions of countries, authors, and most productive journals to COVID-19 and air pollution research from January 1, 2020, to September 12, 2022, from the Web of Sciences Core Collection (WoS). The results showed that (a) publications on the COVID-19 pandemic and air pollution were 504 (research articles) with 7495 citations, (b) China ranked first in the number of publications (n = 151; 29.96% of the global output) and was the main country in international cooperation network, followed by India (n = 101; 20.04% of the total articles) and the USA (n = 41; 8.13% of the global output). Air pollution plagues China, India, and the USA, calling for many studies. After a high spike in 2020, research published in 2021 declined in 2022. The author's keywords have focused on "COVID-19," "air pollution," "lockdown," and "PM25." These keywords suggest that research in this area is focused on understanding the health impacts of air pollution, developing policies to address air pollution, and improving air quality monitoring. The COVID-19 social lockdown served as a specified procedure to reduce air pollution in these countries. However, this paper provides practical recommendations for future research and a model for environmental and health scientists to examine the likely impact of COVID-19 social lockdowns on urban air pollution.

A Call to Include Plastics in the Global Environment in the Class of Persistent, Bioaccumulative, and Toxic (PBT) Pollutants.

A Call to Include Plastics in the Global Environment in the Class of Persistent, Bioaccumulative, and Toxic (PBT) Pollutants.

A novel hydrogeochemical approach to delineate the origin of potentially toxic elements in groundwater: Sophisticated molar ratios as environmental tracers.

The occurrence, mobilization, and origin of Potentially Toxic Eelements (PTEs) in the environment is always a difficult research question that has not been fully addressed to date; solving this problem would be a major achievement for environmental science and pollution research, a significant scientific breakthrough, and an important contribution to environmental analysis and monitoring. The lack of a holistic methodology that uses chemical analysis to determine the origin of each PTE in the environment is the main motivation for this project. Therefore, the hypothesis tested here is to develop a scientific approach applied to each PTE to determine whether its origin is geogenic (i.e., water-rock interaction with dominance of silicate or carbonate mineral phases) or anthropogenic (i.e., agricultural practices, wastewater, industrial activities). A total of 47 groundwater samples from the Psachna Basin in central Euboea, Greece, were used and plotted on geochemical mole ratio diagrams (i.e., Si/NO3 vs. Cl/HCO3) and used to perform a robust geochemical modeling analysis. The proposed method showed that elevated groundwater concentrations of various PTEs in groundwater were mainly related to intensive fertilization (e.g., Cr, U), water-rock interaction (e.g., Ni), and saltwater intrusion. (i.e., As, Se). This work highlights that a comprehensive framework with sophisticated molar ratios combined with modern statistical methods, multi-isotope signatures, and geochemical modeling could provide answers to unresolved scientific questions about the origin of PTEs in water resources and improve environmental resilience.

Long-Term Investigation of Emissions and Economic Growth in Developed and Developing Countries: A Bibliometric Analysis

This study seeks to present a better knowledge mapping of emissions and economic growth by referring to literature reviews in developed and developing countries from 2000 to 2022. This study provides overviews of a bibliometric approach, namely: time analysis, journal, co-authorship, citation, country, and institution. The literature survey uses the causality’s direction between (i) emissions and economic growth, and (ii) emissions in developed and developing countries. The degree of research contribution in developed and developing countries has an upward trend in recent years. Journal analysis reveals that Environmental Science and Pollution Research is the journal with the most contributions to analyses in developed and developing countries. Furthermore, the citation analysis shows that "Growth in Emission Transfers via International Trade from 1990 to 2008" has the highest author citations. This study suggests that research collaboration between developed and developing countries can be promoted in producing sustainable green economic growth.

Applying water environment capacity to assess the non-point source pollution risks in watersheds

Comprehension of the spatial and temporal characteristics of non-point source (NPS) pollution risk in watersheds is essential for NPS pollution research and scientific management. Although the concept of water functional zones (WFZ) has been considered in the NPS pollution risk assessment process. However, no comprehensive study of the NPS pollution risk has been conducted to effectively protect water quality in watersheds with different water environment capacity. Therefore, this study proposes a new NPS pollution risk assessment method that integrates water functional zoning, receiving water body environmental capacity, and space-time distribution of pollution load for quantifying the impact of pollution discharge from sub-catchment on nearby water body quality. Based on the NPS nutrient loss process modeled by the Soil and Water Assessment Tool (SWAT), this method was used to assess the NPS pollution risk in the Le 'an River Watershed at annual and monthly scales. The results showed that the NPS pollution risk is characterized by seasonal and spatial variability and is influenced clearly by the water environment capacity. High NPS pollution loads are not necessarily high pollution risks. Conversely, a low NPS nutrient pollution load does not represent a low regional risk sensitivity. In addition, NPS risk assessment based on the water environment capacity could also distinguish the differences in risk levels that were masked by similar NPS pollutant loss and the same water function zoning to achieve accurate control of NPS pollution management in watersheds.

Discipline Hopping for Environmental Solutions: An Art-Science Collaboration

The two disciplines of art and science have long been identified as separate research fields, often presumed as adopting strikingly different working practices. It can be considered however, that notions of process, experimentation, attention to detail, rigour and curiosity are all commonly shared components. The sharing of ideas and methodologies between artists and scientists have allowed for significant insights, innovation, and research engagement. Air pollution is an invisible killer, with much of the world's population breathing polluted air every day without being able to see it. The visualisation of air pollution is therefore of great importance for public understanding and motivation to improve air quality policies. This paper will uncover what an art-science collaboration can bring to both creative and academic research of air pollution and why interdisciplinary practice should be encouraged. Additionally, we can consider how effective an art and science hybrid is for engaging and affecting audiences. 

Bioanalytical and chemical characterization of organic micropollutant mixtures in long-term exposed passive samplers from the Joint Danube Survey 4: Setting a baseline for water quality monitoring

Monitoring methodologies reflecting the long-term quality and contamination of surface waters are needed to obtain a representative picture of pollution and identify risk drivers. This study sets a baseline for characterizing chemical pollution in the Danube River using an innovative approach, combining continuous three-months use of passive sampling technology with comprehensive chemical (747 chemicals) and bioanalytical (seven in vitro bioassays) assessment during the Joint Danube Survey (JDS4). This is one of the world's largest investigative surface-water monitoring efforts in the longest river in the European Union, which water after riverbank filtration is broadly used for drinking water production. Two types of passive samplers, silicone rubber (SR) sheets for hydrophobic compounds and AttractSPETM HLB disks for hydrophilic compounds, were deployed at nine sites for approximately 100 days. The Danube River pollution was dominated by industrial compounds in SR samplers and by industrial compounds together with pharmaceuticals and personal care products in HLB samplers. Comparison of the Estimated Environmental Concentrations with Predicted No-Effect Concentrations revealed that at the studied sites, at least one (SR) and 4–7 (HLB) compound(s) exceeded the risk quotient of 1. We also detected AhR-mediated activity, oxidative stress response, peroxisome proliferator-activated receptor gamma-mediated activity, estrogenic, androgenic, and anti-androgenic activities using in vitro bioassays. A significant portion of the AhR-mediated and estrogenic activities could be explained by detected analytes at several sites, while for the other bioassays and other sites, much of the activity remained unexplained. The effect-based trigger values for estrogenic and anti-androgenic activities were exceeded at some sites. The identified drivers of mixture in vitro effects deserve further attention in ecotoxicological and environmental pollution research. This novel approach using long-term passive sampling provides a representative benchmark of pollution and effect potentials of chemical mixtures for future water quality monitoring of the Danube River and other large water bodies.

Microplastic Pollution Research Based on the VOS Viewer Software: Research Trends, Ecological Effects, and Testing Methods

Microplastics (MPs) are receiving increasing attention because of their potential harm to the environment and human health. This research aims to summarize the abundance, toxicological effects, and analysis methods of MPs, as well as present their current status and trends in scientific research. Bibliometric analysis confirmed a substantial rise in annual research papers on MPs, predominantly over the previous nine years. The central research areas relating to MPs include distribution, sources, toxic effects, analytical approaches, and adsorption of MPs with other pollutants. Airborne MPs are a primary source of microplastic pollution in remote areas. Humans may inhale and ingest MPs, leading to the accumulation of these particles in their bodies. Additionally, microplastics can have biological toxicity that poses a potential threat to human health. Standard procedures for sampling and both qualitative and quantitative analysis of microplastics in various environmental media must be established urgently to enable effective comparison of experimental conclusions.

A State-of-the-Science Review on High-Resolution Metabolomics Application in Air Pollution Health Research: Current Progress, Analytical Challenges, and Recommendations for Future Direction.

Understanding the mechanistic basis of air pollution toxicity is dependent on accurately characterizing both exposure and biological responses. Untargeted metabolomics, an analysis of small-molecule metabolic phenotypes, may offer improved estimation of exposures and corresponding health responses to complex environmental mixtures such as air pollution. The field remains nascent, however, with questions concerning the coherence and generalizability of findings across studies, study designs and analytical platforms. We aimed to review the state of air pollution research from studies using untargeted high-resolution metabolomics (HRM), highlight the areas of concordance and dissimilarity in methodological approaches and reported findings, and discuss a path forward for future use of this analytical platform in air pollution research. We conducted a state-of-the-science review to a) summarize recent research of air pollution studies using untargeted metabolomics and b) identify gaps in the peer-reviewed literature and opportunities for addressing these gaps in future designs. We screened articles published within Pubmed and Web of Science between 1 January 2005 and 31 March 2022. Two reviewers independently screened 2,065 abstracts, with discrepancies resolved by a third reviewer. We identified 47 articles that applied untargeted metabolomics on serum, plasma, whole blood, urine, saliva, or other biospecimens to investigate the impact of air pollution exposures on the human metabolome. Eight hundred sixteen unique features confirmed with level-1 or -2 evidence were reported to be associated with at least one or more air pollutants. Hypoxanthine, histidine, serine, aspartate, and glutamate were among the 35 metabolites consistently exhibiting associations with multiple air pollutants in at least 5 independent studies. Oxidative stress and inflammation-related pathways-including glycerophospholipid metabolism, pyrimidine metabolism, methionine and cysteine metabolism, tyrosine metabolism, and tryptophan metabolism-were the most commonly perturbed pathways reported in of studies. More than 80% of the reported features were not chemically annotated, limiting the interpretability and generalizability of the findings. Numerous investigations have demonstrated the feasibility of using untargeted metabolomics as a platform linking exposure to internal dose and biological response. Our review of the 47 existing untargeted HRM-air pollution studies points to an underlying coherence and consistency across a range of sample analytical quantitation methods, extraction algorithms, and statistical modeling approaches. Future directions should focus on validation of these findings via hypothesis-driven protocols and technical advances in metabolic annotation and quantification. https://doi.org/10.1289/EHP11851.

Evolution of neural network to deep learning in prediction of air, water pollution and its Indian context.

The scenario of developed and developing countries nowadays is disturbed due to modern living style which affects environment, wildlife and natural habitat. Environmental quality has become or is a subject of major concern as it is responsible for health hazard of mankind and animals. Measurements and prediction of hazardous parameters in different fields of environment is a recent research topic for safety and betterment of people as well as nature. Pollution in nature is an after-effect of civilization. To combat the damage already happened, some processes should be evolved for measurement and prediction of pollution in various fields. Researchers of all over the world are active to find out ways of predicting such hazard. In this paper, application of neural network and deep learningalgorithms is chosen for air pollution and water pollution cases. The purpose of this review isto reveal how family of neural network algorithms has applied on these two pollution parameters. In this paper, importance is given on algorithm, and datasets used for air and water pollution as well as the predicted parameters have also been noted for ease of future development. One major concern of this paper is Indian context of air and water pollution research, and the research potential presents in this area using Indian dataset. Another aspect for including both air and water pollutions in one review paper is to generate an idea of artificial neural network and deep learning techniques which can be cross applicable for future purpose.

Research Progress and Trend of Agricultural Non-Point Source Pollution from Non-Irrigated Farming Based on Bibliometrics

The agricultural non-point source (NPS) pollution caused by non-irrigated farming, such as heavy metals, nitrogen and phosphorus, has posed an extreme threat to the security of agricultural product quality and watershed ecology. Thus, it is urgent to sort out the latest research progress and future development trend to effectively guide future scientific research and technological updates in this field. This study integrates the relevant literature of the Web of Science from 1976 to 2021 and analyzes the research hotspots and development trends in the field of agricultural NPS pollution from non-irrigated farming in combination with CiteSpace. The results showed that the proportion of publications from the United States and China accounted for 58.4%. Science of the Total Environment, Water Science and Technology and Journal of the American Water Resources Association were the most published journals. The research topics and hotspots mainly involve agricultural NPS pollution prevention technology, pollution source identification, pollution load and management and landscape pattern evolution. In the future, agricultural NPS pollution research in non-irrigated farming should combine agricultural big data platforms, spectroscopic methods, artificial intelligence technology, etc. and focus on strengthening soil testing formula fertilization management, the efficient use of livestock and poultry breeding manure, climate change and risk early warning.

Toxic Effects of Several Common Heavy Metals in Soils on Terrestrial Invertebrates

In this paper, the ecotoxicology research method combines field investigation and indoor simulation experiment. Sampling and analysis of heavy metal pollution of solid soil samples from lead-acid battery factory, identified Pb, Cd and Cr as the pollution research objects, discussed the biological reaction mechanism of Eiseniafetida under the stress of heavy metals Pb, Cd and Cr, and obtained the results. The results of the lethal and sublethal effects of Pb, Cd and Cr on Pb, Cd and Cr and the effect of biomarkers. The main research results are as follows: In the acute toxicity test of Aisheng worm, the theoretical half-lethal concentrations (LC50) of single Pb, Cd, and Cr exposure for 14 days were 2620.93, 362.83, and 229.44 mg/kg, respectively, and their toxicity was Cr>Cd>Pb .

The application of machine learning to air pollution research: A bibliometric analysis

Machine learning (ML) is an advanced computer algorithm that simulates the human learning process to solve problems. With an explosion of monitoring data and the increasing demand for fast and accurate prediction, ML models have been rapidly developed and applied in air pollution research. In order to explore the status of ML applications in air pollution research, a bibliometric analysis was made based on 2962 articles published from 1990 to 2021. The number of publications increased sharply after 2017, comprising approximately 75% of the total. Institutions in China and United States contributed half of all publications with most research being conducted by individual groups rather than global collaborations. Cluster analysis revealed four main research topics for the application of ML: chemical characterization of pollutants, short-term forecasting, detection improvement and optimizing emission control. The rapid development of ML algorithms has increased the capability to explore the chemical characteristics of multiple pollutants, analyze chemical reactions and their driving factors, and simulate scenarios. Combined with multi-field data, ML models are a powerful tool for analyzing atmospheric chemical processes and evaluating the management of air quality and deserve greater attention in future.

Pollution impact on metal and biomarker responses in intestinal cytosol of freshwater fish.

In the present study, essential and nonessential metal content and biomarker responses were investigated in the intestine of fish collected from the areas polluted by mining. Our objective was to determine metal and biomarker levels in tissue responsible for dietary intake, which is rarely studied in water pollution research. The study was conducted in the Bregalnica River, reference location, and in the Zletovska and Kriva Rivers (the Republic of North Macedonia), which are directly influenced by the active mines Zletovo and Toranica, respectively. Biological responses were analyzed in Vardar chub (Squalius vardarensis; Karaman, 1928), using for the first time intestinal cytosol as a potentially toxic cell fraction, since metal sensitivity is mostly associated with cytosol. Cytosolic metal levels were higher in fish under the influence of mining (Tl, Li, Cs, Mo, Sr, Cd, Rb, and Cu in the Zletovska River and Cr, Pb, and Se in the Kriva River compared to the Bregalnica River in both seasons). The same trend was evident for total proteins, biomarkers of general stress, and metallothioneins, biomarkers of metal exposure, indicating cellular disturbances in the intestine, the primary site of dietary metal uptake. The association of cytosolic Cu and Cd at all locations pointed to similar pathways and homeostasis of these metallothionein-binding metals. Comparison with other indicator tissues showed that metal concentrations were higher in the intestine of fish from mining-affected areas than in the liver and gills. In general, these results indicated the importance of dietary metal pathways, and cytosolic metal fraction in assessing pollution impacts in freshwater ecosystems.

How do we envision the sustainable society we want to create?

How do we envision the sustainable society we want to create? Professor Masahide Sakamoto uses Japanese pollution research to discuss how we can implement a better, more sustainable society. He uses two ideas that we should choose in order to realize a sustainable society: 1. If the inequality structure of the capitalist economy remains unchanged, each person should seek happiness in his or her own way within that structure. 2. Assuming that it is natural that there are differences in individual abilities, we should build a society in which there are no differences in human values, and we can respect each other as human beings. While there is no single definition of a sustainable society, he argues the introduction of renewable energy has less environmental impact than fossil fuel-based power generation systems. Fossil fuels can only be extracted in certain places, and disputes over ownership are likely to arise, whereas renewable energy can be used in any country with the right technology. This entails wars and conflicts over oil can be avoided, leading to the peaceful use of energy, and reducing post-disaster damage. Ultimately, the realization of a sustainable society will depend on what kind of socioeconomic system is designed by governments.

The future of ocean plastics: designing diverse collaboration frameworks

Abstract This paper aims to guide the stakeholder engagement process related to plastic pollution research in marine environments. We draw on advice identified during an online workshop (Ocean Plastic Workshop 2022) organized by Early Career Ocean Professionals (ECOPs) from 11 countries, held in April 2022. International experts and workshop participants discussed their experiences in the collaborative development and implementation of ocean plastic pollution projects held worldwide, guided by three main questions: (i) What is the role of scientists in a multi-stakeholder project? (ii) How should scientists communicate with other stakeholders? (iii) Which stakeholders are missing in collaborative projects, and why are they missing? This multidisciplinary, co-learning approach highlights the value of stakeholder engagement for ocean plastic projects with an end goal to identify and implement ocean plastic solutions via innovative technologies, informing policy, community engagement, or a combination of all three approaches. The target outcomes of the workshop described in this paper include the identification of transdisciplinary (academic-stakeholder) engagement frameworks and specific suggestions that can serve as guidelines for the development of future plastic pollution projects.

Predicting lake water quality index with sensitivity-uncertainty analysis using deep learning algorithms

Regular monitoring and assessment of water quality is essential to maintain the quality of lake water. A commonly used method for assessing water quality is the Water Quality Index (WQI). Artificial intelligence (AI) can predict the WQI rapidly and more accurately than conventional methods. In this study, a stacking ensemble model based on Deep Learning (DL) was proposed to predict the WQI by integrating three models: Gradient Boosting Machine (GBM), Generalized Linear Model (GLM) and Neural Network (NN). The performance of this model was compared with that of Deep Dense Neural Network (DNN) and Convolutional Neural Network (CNN). The inclusion of a DNN model, which was used for the first time in water pollution research to perform sensitivity and uncertainty analysis in predicting WQI, added a new dimension to the workflow. The mean square error (MSE), mean absolute error (MAE), root mean square error (RMSE) and correlation coefficient (R2) were used to evaluate the performance of the WQI prediction models. The results showed that the WQI of the water samples ranged from 90.75 to 145.29, indicating poor to very poor water quality. The validation results showed that the stack model was superior to all other models in terms of prediction accuracy, with MSE of 25.77, RMSE of 5.07, MAE of 3.5 and R2 of 0.98. The DNN-based sensitivity analysis showed that pH and turbidity significantly affect the WQI and should be monitored to minimise water pollution. The uncertainty analysis indicated that electrical conductivity and total dissolved solids have the highest uncertainty in predicting WQI. This study provides researchers, decision makers and water scientists with accurate information on water quality and enables the implementation of clean production processes to reduce pollution and improve water quality.

Plastic waste management and safety disinfection processes for reduced the COVID-19 Hazards

ABSTRACT In response to the pandemic of COVID-19, various unexpected environmental impacts in many countries have been rising. Millions of gloves and masks are used and thrown away daily around the globe. Incorrect disposal of COVID-19 waste without disinfection preparation could expose people and healthcare personnel to the possibility of spreading the infection of coronaviruses. This article finds an appropriate way to disinfect the waste of coronavirus-infected items by involving various physical factors, chemical and biological or physiological factors. Policymakers must immediately adopt disinfection technology to achieve green recovery of covid-19 waste that encourages development and sustains climate change. Regarding previously published papers and research results, this article intends to investigate the plastic pollution research status before and during the COVID-19 pandemic and outline safely disinfecting COVID-19 plastic waste.

Health effects of air pollutant mixtures (volatile organic compounds, particulate matter, sulfur and nitrogen oxides)- a review of the literature.

The health risks associated with individual air pollutant exposures have been studied and documented, but in real-life, the population is exposed to a multitude of different substances, designated as mixtures. A body of literature on air pollutants indicated that the next step in air pollution research is investigating pollutant mixtures and their potential impacts on health, as a risk assessment of individual air pollutants may actually underestimate the overall risks. This review aims to synthesize the health effects related to air pollutant mixtures containing selected pollutants such as: volatile organic compounds, particulate matter, sulfur and nitrogen oxides. For this review, the PubMed database was used to search for articles published within the last decade, and we included studies assessing the associations between air pollutant mixtures and health effects. The literature search was conducted according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. A number of 110 studies were included in the review from which data on pollutant mixtures, health effects, methods used, and primary results were extracted. Our review emphasized that there are a relatively small number of studies addressing the health effects of air pollutants as mixtures and there is a gap in knowledge regarding the health effects associated with these mixtures. Studying the health effects of air pollutant mixtures is challenging due to the complexity of components that mixtures may contain, and the possible interactions these different components may have.

Progress and future perspective of epidemiological research of air pollution and climate change in the context of achieving carbon peaking and carbon neutrality goals

Climate change is the great health challenge for human beings in the 21st century. Air pollution is also an important public health problem worldwide. China announced the climate commitment to achieve carbon peaking by 2030 and carbon neutrality by 2060. Achieving these goals would not only have far-reaching effects on air pollution control and climate change, but also improve the population health in China. Air pollution and climate change epidemiology are important aspects of environmental epidemiology. In this paper, we discuss the current status and future development of epidemiological research of air pollution and climate change in the context of achieving carbon peaking and carbon neutrality goals to provide ideas and suggestions for environmental and health studies in the future.