Ioannis Manisalidis, E. Stavropoulou, Agathangelos Stavropoulos
et al.
One of our era's greatest scourges is air pollution, on account not only of its impact on climate change but also its impact on public and individual health due to increasing morbidity and mortality. There are many pollutants that are major factors in disease in humans. Among them, Particulate Matter (PM), particles of variable but very small diameter, penetrate the respiratory system via inhalation, causing respiratory and cardiovascular diseases, reproductive and central nervous system dysfunctions, and cancer. Despite the fact that ozone in the stratosphere plays a protective role against ultraviolet irradiation, it is harmful when in high concentration at ground level, also affecting the respiratory and cardiovascular system. Furthermore, nitrogen oxide, sulfur dioxide, Volatile Organic Compounds (VOCs), dioxins, and polycyclic aromatic hydrocarbons (PAHs) are all considered air pollutants that are harmful to humans. Carbon monoxide can even provoke direct poisoning when breathed in at high levels. Heavy metals such as lead, when absorbed into the human body, can lead to direct poisoning or chronic intoxication, depending on exposure. Diseases occurring from the aforementioned substances include principally respiratory problems such as Chronic Obstructive Pulmonary Disease (COPD), asthma, bronchiolitis, and also lung cancer, cardiovascular events, central nervous system dysfunctions, and cutaneous diseases. Last but not least, climate change resulting from environmental pollution affects the geographical distribution of many infectious diseases, as do natural disasters. The only way to tackle this problem is through public awareness coupled with a multidisciplinary approach by scientific experts; national and international organizations must address the emergence of this threat and propose sustainable solutions.
Global warming, climate change, and environmental pollution present plants with unique combinations of different abiotic and biotic stresses. Although much is known about how plants acclimate to each of these individual stresses, little is known about how they respond to a combination of many of these stress factors occurring together, namely a multifactorial stress combination. Recent studies revealed that increasing the number of different co-occurring multifactorial stress factors causes a severe decline in plant growth and survival, as well as in the microbiome biodiversity that plants depend upon. This effect should serve as a dire warning to our society and prompt us to decisively act to reduce pollutants, fight global warming, and augment the tolerance of crops to multifactorial stress combinations.
Landfilling is one of the most common waste management methods employed in all countries alike, irrespective of their developmental status. The most commonly used types of landfills are (a) municipal solid waste landfill, (b) industrial waste landfill, and (c) hazardous waste landfill. There is, also, an emerging landfill type called “green waste landfill” that is, occasionally, being used. Most landfills, including those discussed in this review article, are controlled and engineered establishments, wherein the waste ought to abide with certain regulations regarding their quality and quantity. However, illegal and uncontrolled “landfills” (mostly known as open dumpsites) are, unfortunately, prevalent in many developing countries. Due to the widespread use of landfilling, even as of today, it is imperative to examine any environmental- and/or health-related issues that have emerged. The present study seeks to determine the environmental pollution and health effects associated with waste landfilling by adopting a desk review design. It is revealed that landfilling is associated with various environmental pollution problems, namely, (a) underground water pollution due to the leaching of organic, inorganic, and various other substances of concern (SoC) contained in the waste, (b) air pollution due to suspension of particles, (c) odor pollution from the deposition of municipal solid waste (MSW), and (d) even marine pollution from any potential run-offs. Furthermore, health impacts may occur through the pollution of the underground water and the emissions of gases, leading to carcinogenic and non-carcinogenic effects of the exposed population living in their vicinity.
The complexity and dynamics of the environment make it extremely difficult to directly predict and trace the temporal and spatial changes in pollution. In the past decade, the unprecedented accumulation of data, the development of high-performance computing power, and the rise of diverse machine learning (ML) methods provide new opportunities for environmental pollution research. The ML methodology has been used in satellite data processing to obtain ground-level concentrations of atmospheric pollutants, pollution source apportionment, and spatial distribution modeling of water pollutants. However, unlike the active practices of ML in chemical toxicity prediction, advanced algorithms such as deep neural networks in environmental process studies of pollutants are still deficient. In addition, over 40% of the environmental applications of ML go to air pollution, and its application range and acceptance in other aspects of environmental science remain to be increased. The use of ML methods to revolutionize environmental science and its problem-solving scenarios has its own challenges. Several issues should be taken into consideration, such as the tradeoff between model performance and interpretability, prerequisites of the machine learning model, model selection, and data sharing.
Jonathan Awewomom, Felicia Dzeble, Yaw Doudu Takyi
et al.
Global environmental pollution presents formidable obstacles to the long-term viability of the planet. This study synthesized current relevant literature with statistical snapshots from pollution statistics and reports and presented feasible recommendations to address the ramifications of global environmental pollution. A central focus is laid on the importance of preventive environmental management (PEM) and the strategic enforcement of environmental policies (EP), with a detailed exploration of history evolution and current application challenges. Specifically, the study centers on the significance of environmental policy and preventive environmental management in combatting global pollution. The examination encompasses an overview of environmental pollution and its implications for the environment and human health. It explores the role of environmental policy in mitigating environmental pollution, scrutinizes the principles underlying preventive environmental management, and evaluates the effectiveness of environmental management systems in curbing pollution. Furthermore, the study identifies and analyzes the challenges of implementing environmental control techniques, offering recommendations to overcome these obstacles. The outcomes of this research contribute to a more comprehensive understanding of the potential of environmental control methods in tackling global environmental pollution. The study underscores the crucial nature of robust environmental policies and proactive approaches to prevent pollution and foster sustainable development. Additionally, it offers insights into the necessity for collaboration and cooperation among stakeholders at various levels to attain effective pollution control and environmental management.
Caicheng Long, Zixin Jiang, Jingfang Shangguan
et al.
Abstract With the development of carbon-based nanomaterials, carbon dots (CDs) have recently received increasing attention owing to their unique optical properties, low toxicity, facile synthesis, abundance and inexpensive precursors. Along with the demonstrated applications in bioimaging, photocatalysis, and biochemical sensing, CDs are expected to have novel applications in different environmental fields. This work aims to review the recent developments in the use of CDs for environmental pollution control and remediation, such as sensing of environmental pollutants, contaminant adsorption, membrane separation, pollutant degradation, and as antimicrobial agents. Different synthesis methods of CDs as well as properties relevant to environmental applications are also discussed. Compared with other carbon-based nanomaterials, the unique nanostructures and properties of CDs enable exceptional environmental capabilities. Moreover, the challenges and research direction for future environmental applications of CDs are also highlighted. We believe this review will provide new direction to the development of environmental pollution control and remediation using CDs.
The environmental and health impacts from the massive discharge of chemicals and subsequent pollution have been gaining increasing public concern. The unintended exposure to different pollutants, such as heavy metals, air pollutants and organic chemicals, may cause diverse deleterious effects on human bodies, resulting in the incidence and progression of different diseases. The article reviewed the outbreak of environmental pollution-related public health emergencies, the epidemiological evidence on certain pollution-correlated health effects, and the pathological studies on specific pollutant exposure. By recalling the notable historical life-threatening disasters incurred by local chemical pollution, the damning evidence was presented to criminate certain pollutants as the main culprit for the given health issues. The epidemiological data on the prevalence of some common diseases revealed a variety of environmental pollutants to blame, such as endocrine-disrupting chemicals (EDCs), fine particulate matters (PMs) and heavy metals. The retrospection of toxicological studies provided illustrative clues for evaluating ambient pollutant-induced health risks. Overall, environmental pollution, as the hidden culprit, should answer for the increasing public health burden, and more efforts are highly encouraged to strive to explore the cause-and-effect relationships through extensive epidemiological and pathological studies.
Increasing plastic waste is a critical global challenge to ecological and human health requiring focused solutions to reduce omnipresent plastic pollution in the environment. While recycling has been touted as one solution to counter plastic waste and resource utilization, it has been largely ineffective in offsetting the impact of rising global plastic production of more than 400 million metric tonnes annually, due to low global recycling rates of only 9%. Over three decades since implementing plastic resin codes, recycling has favoured thermoplastics, neglecting thermoset plastics. There is a constant need to enhance overall recycling efficiency by exploring advanced methods, as enormous gaps exist in fully unlocking the potential of plastic recycling. We identify critical gaps associated with plastic waste recycling and its potential environmental impacts. We discuss substantial progress in recycling technology, designs-for-recyclability with controlled chemical use, and economic incentives to expand markets for recycled plastics and to curb plastic leakage into the environment. Additionally, we highlight some emerging strategies and legally binding international policy instruments, such as the Global Plastics Treaty that require further development to reduce plastic waste and improve plastic recyclability.
Abstract Environmental problem is a much discussed issue worldwide. It is a more serious issue for Bangladesh, which is vulnerable to frequent natural disasters. Therefore, this work aims to identify the short-run and long-run causal relationships between clean energy, population density, urbanization, economic development, trade openness and environmental pollution in Bangladesh using the data of 1973 - 2014. Different time series related econometric techniques such as Augmented Dickey-Fuller test, Phillips-Perron test, Autoregressive Distributive Lag (ARDL) bounds test and the Toda-Yamamoto Granger causality test are applied to find out outcomes. Our findings are: the use of clean energy improved the environmental quality, but population density, urbanization and economic growth are found to be detrimental to the environment; a unidirectional causality of CO2 emissions with clean energy, economic growth and urbanization is also revealed. Therefore, the use of more clean energy for reducing the environmental pollution is to be ensured.
Detecting hazardous substances in the environment is crucial for protecting human wellbeing and ecosystems. As technology continues to advance, artificial intelligence (AI) has emerged as a promising tool for creating sensors that can effectively detect and analyze these hazardous substances. The increasing advancements in information technology have led to a growing interest in utilizing this technology for environmental pollution detection. AI-driven sensor systems, AI and Internet of Things (IoT) can be efficiently used for environmental monitoring, such as those for detecting air pollutants, water contaminants, and soil toxins. With the increasing concerns about the detrimental impact of legacy and emerging hazardous substances on ecosystems and human health, it is necessary to develop advanced monitoring systems that can efficiently detect, analyze, and respond to potential risks. Therefore, this review aims to explore recent advancements in using AI, sensors and IOTs for environmental pollution monitoring, taking into account the complexities of predicting and tracking pollution changes due to the dynamic nature of the environment. Integrating machine learning (ML) methods has the potential to revolutionize environmental science, but it also poses challenges. Important considerations include balancing model performance and interpretability, understanding ML model requirements, selecting appropriate models, and addressing concerns related to data sharing. Through examining these issues, this study seeks to highlight the latest trends in leveraging AI and IOT for environmental pollution monitoring.
In the era of information economy, the integration of the internet and traditional industries is pushing the rapid transformation of the world economy in a more innovative, smarter, and greener direction. Based on the panel data for 30 Chinese provinces for the 2006–2017 period, the level of China’s internet development is comprehensively evaluated using the full array polygon graphic index method. The spatial Durbin model and threshold model are used to empirically analyze the impact of internet development on environmental quality. The results indicate that China’s environmental pollution has a significant spatial spillover effect. Internet development can not only significantly reduce local environmental pollution, but also environmental pollution in neighboring areas. The regression results of the mediation effect indicate that internet development mainly affects environmental pollution by improving technological innovation, industrial upgrading, human capital and financial development. Finally, policy suggestions are proposed from the aspects of strengthening collaborative environmental governance and increasing internet infrastructure investment.
Pursuing ecological sustainability while mitigating the effects of environmental pollution has become a global pursuit. Moreover, the issue of how emerging economies like Mexico, Indonesia, Turkey, and Nigeria (MINT) economies can significantly reduce environmental pollution (EVP) remains elusive. This study sought to investigate the interplay between economic growth, green finance, renewable energy use, natural resource rent, energy innovation, urbanization and environmental pollution by analyzing panel data from 1990 to 2020. This research employed the novel econometrics approach CS-ARDL to examine the short and long-term relationships among the series. The research outcome disclosed that economic growth, natural resource rent and urbanization increase environmental pollution. In contrast, the empirical findings of this study revealed that environmental pollution could be neutralized through effective mechanisms such as green finance, renewable energy consumption, and the promotion of energy innovation. This research provides a fresh insight from the MINT economies and contributes to the existing literature by examining factors contributing to environmental pollution. This research also provides a benchmark for policy-makers and governments to invest in environmentally-friendly technologies to exploit the natural resources in these countries to mitigate the effect of environmental pollution.
The digital economy and the green economy are two major issues for economic recovery in the post epidemic era. From spatial interaction spillover, we analyze and measure the relationships between the digital economy and environmental pollution in 287 prefecture-level cities in China from 2008 to 2018 using simultaneous spatial equations and the generalized 3-stage least square (GS3SLS) method. The results show that: (1) there is a reverse and complex spatio-temporal evolution of the digital economy and environmental pollution in Chinese cities. (2) There is a spatial interaction spillover effect between the digital economy and environmental pollution. Local digital economy and environmental pollution inhibit each other. The digital economy and environmental pollution have a significant spatial spillover. The digital economy of surrounding regions has a suppressive effect on local environmental pollution. The environmental pollution of surrounding cities has a crowding-out effect on the local digital economy. (3) Digital economy suppresses environmental pollution through the green development effect and innovative development effect; environmental pollution suppresses the digital economy through the talent crowding out effect and the policy tightening effect. The conclusion of this paper provides evidence for the coupling and coordinated development between the digital and green economy, which is of great significance for promoting the transformation of economic development modes and realizing green and high-quality development.
In the context of the goal of "carbon neutrality", an economic development model that achieves emission reduction goals and ensures stable economic growth is currently being advocated by China. Based on provincial panel data in China from 2005 to 2016, we analyse the impact of economic growth target (EGT) constraints on environmental pollution using a spatial econometric method. The results indicate that EGT constraints significantly exacerbate environmental pollution in local regions and adjacent areas. Local governments are motivated to achieve economic growth goals at the expense of the ecological environment. The positive effects are attributed to a reduction in environmental regulation (ER), industrial structure upgrading and technological innovation and an increase in foreign direct investment (FDI). Moreover, environmental decentralization (ED) plays a positive regulatory role and can weaken the adverse influences of EGT constraints on environmental pollution. Interestingly, the nonlinear impact of EGT constraints on environmental pollution relies on different types of ED. Environmental administration decentralization (EDA) and environmental supervision decentralization (EDS) can reduce the positive effect of EGT constraints on environmental pollution, while an improvement in environmental monitoring decentralization (EDM) can increase the promoting influences of the constraints of economic growth goals on environmental pollution. The above conclusions still hold under a series of robustness tests. Based on the above findings, we suggest that local governments set scientific growth targets, establish scientific assessment indicators for officials, and optimize the ED management institution.
Sabrina Chiodo, Sonia M. Grandi, Jessica Gronsbell
et al.
Introduction
Perinatal outcomes are shaped by clinical, social, and environmental factors, yet Canada lacks a nationally representative pregnancy cohort capturing these influences at the individual-level. This gap has limited the ability to address multifactorial drivers of maternal and fetal health. To fill this need, we established a linked cohort integrating survey, clinical, and contextual data to support equity-focused, precision public health research in maternal health.
Methods
We linked the Canadian Community Health Survey (CCHS; 2000--2017) to the Discharge Abstract Database (DAD) using Statistics Canada's Social Data Linkage Environment. Eligible participants were female (as defined by the binary CCHS sex variable), aged 15-49 years, with a hospital delivery within two years of their CCHS interview. We excluded multifetal gestations and retained only the first delivery per individual. Area-level and environmental exposures (i.e., neighbourhood inequity, pollution, greenspace, neighbourhood walkability, etc.) were appended via residential postal codes using the Postal Code Conversion File Plus (PCCF+).
Results
The cohort includes 13,360 singleton births. Pre-pregnancy data include sociodemographics, health behaviours, chronic conditions, psychosocial factors, and reproductive history. Contextual measures capture neighbourhood marginalization, air pollution, greenness, and built environment characteristics. In the CCHS, individuals who reported being pregnant at interview and those who did not (but later delivered) had similar characteristics (SMDs < 0.1), except for age and marital status. Data quality is supported by Statistics Canada's survey protocols, CIHI's hospital validation processes, and standardised geocoding.
Conclusion
Approved researchers can recreate this dataset within Statistics Canada's Research Data Centres using reproducible R code, which will become openly available on GitHub. The cohort enables research across descriptive epidemiology, causal inference, predictive modelling, and health equity evaluation, supporting investigations into multilevel determinants of maternal health. Future work should prioritise national mother--child linkages to expand life course research.
Compared with other countries, China's local governments often adopt the land supply strategy of "low price and sufficient supply" for industrial land and "high price and limited supply" for commercial land in the allocation of land resources. The allocation of land resources is an important means to promote the rapid development of China's economy, and the impacts of land resource misallocation (LRM) on environmental pollution are increasingly apparent. This paper uses panel data from 30 provinces in China from 2009 to 2018 to discuss the relationship between LRM and environmental pollution. The ratio of the average price of commercial land to the average price of industrial land is used to measure the degree of LRM. The Ordinary Least Squares (OLS), spatial Durbin model (SDM), threshold model, and mediation effect model are used to study the direct effect, spatial spillover effect, nonlinear relationship, and conduction mechanism of LRM on environmental pollution. The results show that LRM significantly aggravated environmental pollution. This conclusion still holds after robustness tests including the substitution of dependent variables and IV estimates. The LRM aggravates environmental pollution through industrial structure and technological progress. Interestingly, the impact of LRM on environmental pollution also has a significant positive spatial spillover effect in adjacent regions. In addition, there is also evidence that the adverse effect of LRM on environmental pollution is nonlinear at different levels of industrial structure and technological progress. The threshold model shows that with the optimization of the industrial structure, the impact of LRM on environmental pollution shows a weakening trend of "inverted V-shaped", and with the advancement of technology, the impact of LRM on environmental pollution presents an "S-shaped" changing trend of "strong-weak-strong".
With the growing emphasis on sustainable development, green policies have become a crucial factor influencing both environmental pollution and the career progression of officials in China and other countries. However, the mechanisms behind this relationship remain unclear. This paper aims to enhance the understanding of how environmental pollution impacts official promotion by analyzing the performance of provincial leaders in China and their incentives to address pollution. Using provincial panel data from 1998 to 2020 and a probit model, our study uncovers significant findings. We demonstrate that the intensified green attention by China's central government has notably reduced the promotion prospects for provincial officials with poor environmental protection records, particularly since 2013. Furthermore, our research extends the analysis of micro-level mechanisms, illustrating how the central government's political incentives effectively influence local environmental governance. This study underscores the central government's capability to leverage its personnel system to achieve desired outcomes in sustainable development.