Hasil untuk "Environmental pollution"

Sónia Cristina Almeida Neves, A. Marques, Margarida Patrício

There is no doubt that economic growth is one of the main drivers of pollution. Climate change, caused by increased emissions, has harmful and irreversible effects on economies as a whole. This paper intends to contribute to the current debate on the factors that help reduce emissions, by presenting empirical evidence on the role of environmental regulation in this process. Specifically, this research aims to fill a gap in the literature by focusing on the effects on carbon dioxide emissions of market-based regulations, regulatory policies to incentivize the deployment of renewables, and foreign direct investment. To accomplish this objective, it uses yearly data from 1995 to 2017 for 17 European Union (EU) countries. To control for potential endogeneity, and to study the short- and the long-run effects individually, an Autoregressive Distributed Lag model was used with a Driscoll–Kraay estimator. The main findings show that environmental regulation is effective in cutting CO2 emissions in the long-run. Additionally, policies supporting renewable energy sources tend to reduce CO2 emissions in both the short- and long-run. The effectiveness of these policies is further demonstrated by a reduction in carbon dioxide emissions due to foreign direct investment, suggesting that the EU is successfully attracting high-quality and innovative investment. The pollution halo hypothesis was also validated for EU countries.

S. Hassan, S. Khan, Enjun Xia et al.

Abstract A growing literature has highlighted that institutional quality is an effective tool for ensuring a country’s sustainability. Institutions play a significant role in the country’s development, and specifically in terms of air pollution. How institutional quality enhances or weakens air quality is not been extensively estimated in the literature. This study takes a step forward to investigate the role of institutional quality in CO2 emissions in Pakistan. An autoregressive distributed lag model (ARDL) is used for data spanning from 1984 to 2016 in the context of Pakistan. The result indicates cointegration among variable under consideration. Overall, empirical results infer that institutions result in increasing CO2 emissions in Pakistan. Moreover, institutions quality and CO2 emissions granger cause each other. Further, finding shows that more income reduces CO2 emissions over time, which validates the EKC existence for CO2 emissions. Our findings suggest there is a need to strengthen institutions to mitigate the environmental effect.

Waqas Waqas, Ye Yuan, Yongyi Chen et al.

Heavy metal(loid)s (HMs) represent significant environmental and health threats due to their persistence, bioaccumulation, and ability to induce oxidative stress and lipid peroxidation (LPO) in aquatic organisms. Here, we present the first lipidome analysis of the mud crab Scylla paramamosain to examine associations between lipid profiles and 12 HMs across three contaminated estuaries in Guangdong Province. We observed pronounced sex-specific differences in both HMs concentrations and lipid composition (P < 0.05). The lipidome showed marked dysregulation of membrane lipids, with glycerophospholipids (52.33%) and sphingolipids (11.9%) predominating, alongside elevated energy-storage lipids such as triacylglycerols (24.35%). Major glycerophospholipid classes included phosphatidylcholines (7.7%), phosphatidylethanolamine (7.49%), phosphatidylserine (6.98%), and phosphatidylglycerol (5.31%), while within the sphingolipid fraction, carnitines (4.43%) and ceramides (>2.26%) were abundant, and saccharolipids such as MGDG were present at low levels (0.07%). Female crabs exhibited significantly higher HMs concentrations and lipid levels than males, with copper (Cu), iron (Fe), and zinc (Zn) being the most abundant. Notably, Cu, Fe, and lead (Pb) showed strong positive correlations with all lipid groups. The associated lipid alterations are consistent with mitochondrial dysfunction and oxidative stress-related pathways commonly linked to HMs exposure. Although sex- and site-specific differences cannot be attributed exclusively to HMs under field conditions. These findings demonstrate the sensitivity of lipidomic profiles to environmental contamination and highlight lipidomics as a powerful tool for assessing ecological risks in HM-contaminated estuarine systems.

Yu Ma, Jiahang Zhang, Qixuan Zhou et al.

Rotenone, a widely used agricultural pesticide has been linked to an increased risk of Parkinson’s disease (PD) following chronic exposure. Our previous study found that rotenone not only caused motor deficits in mice, but was also able to cause cognitive deficits, a common non-motor symptom in PD. This study aimed to explore whether activation of the SIRT1-Nrf2 axis confers neuroprotection against rotenone-induced cognitive impairments. We found that rotenone significantly decreased the expression and activation of SIRT1 and Nrf2 in the hippocampus of mice. Pharmacological activation of SIRT1 and Nrf2 using resveratrol and tert-butylhydroquinone (TBHQ), respectively, markedly ameliorated rotenone-induced learning and memory impairments and neuronal damage. Mechanistically, resveratrol and TBHQ suppressed microglial activation and the expression of proinflammatory genes (iNOS, TNFα, IL-1β), attenuated C3-CR3 signaling, and restored the levels of synaptic proteins (PSD95, mBDNF, TrkB), indicating suppression of abnormal glia-mediated synaptic pruning. Furthermore, SIRT1-Nrf2 axis activation reduced iron accumulation and lipid peroxidation in the hippocampus, accompanied by increased GPX4 and decreased COX2 and ACSL4 expression, suggesting suppression of neuronal ferroptosis. Collectively, our findings demonstrate that activation of the SIRT1-Nrf2 axis alleviates rotenone-induced cognitive deficits by inhibiting microglia-mediated synaptic pruning and neuronal ferroptosis. These results provide mechanistic insight and a potential therapeutic target for pesticide-induced neurotoxicity and PD-related cognitive dysfunction.

Jing Zhao, Ziru Zhao, Huanbo Zhang

Abstract This research examines data from 30 Chinese provinces from 1999 to 2017 to investigate the impacts of economic growth, energy consumption, and financial development on environmental pollution. Using spatial econometrics, we find that energy consumption has increased environmental pollution. Moreover, the direct effects of two indicators measuring financial development—financial depth and financial efficiency—on environmental pollution are directly negative and positive, respectively. Moreover, these two financial development indicators moderate the effects of technical progress and industry structure on environmental pollution differently. The “inverted N”-shaped environmental Kuznets curve relationship is well supported for SO2 and solid waste.

Lina Liu, M. Bilal, X. Duan et al.

Industries are the paramount driving force for the economic and technological development of society. However, the flourishing industrialization and unimpeded growth of current production unit's result in widespread environmental pollution due to increased discharge of wastes loaded with baleful, hazardous, and carcinogenic contaminants. Physicochemical-based remediation means are costly, create a secondary disposal problem and remain inadequate for pollution mitigating because of the continuous emergence of new recalcitrant pollutants. Due to eco-friendly, social acceptance, and lesser health hazards, microbial bioremediation has received considerable global attention for pollution abatement. Moreover, with the recent advancement in biotechnology and microbiology, genetically engineered bacteria with high ability to remove environmental pollutants are widely used in the fields of environmental restoration, resulting in the bioremediation in a more viable and eco-friendly way. This review summarized the advantages of genetically engineered bacteria and their application in the treatment of a wide variety of environmental contaminants such as synthetic dyestuff, heavy metal, petroleum hydrocarbons, polychlorinated biphenyls, phenazines and agricultural chemicals which will include herbicides, pesticides, and fertilizers. Considering the risk of genetic material exchange by using genetically engineered bacteria, the challenges and limitations associated with the application of recombinant bacteria on contaminated sites are also discussed. An integrated microbiological, biological and ecological acquaintance accompanied by field engineering designs are the desired features for effective in situ bioremediation of hazardous waste polluted sites by recombinant bacteria.

Kunming Li, Liting Fang, Lerong He

Abstract This paper examines the impact of population and energy price on China's environmental pollution through both industrial and residential channels. Our theoretical models reveal that the influences of population on environmental pollution are contingent on wage stickiness and wage elasticity. We also predict that environment pollution decreases with rising energy price, but downward energy price distortion intensifies environmental pollution. We test these hypotheses using panel data of 30 Chinese provinces from 2001 to 2016 and employing both constant coefficient and time-varying coefficient panel data models to conduct analyses. Our empirical results show that population growth, increased urbanization, and energy price distortion all intensify environmental pollution, while population aging and rising energy price tend to alleviate environmental pollution. Our results also reveal significant regional and time heterogeneity on the impact of population factors and energy price on China's environmental pollution.

Xuehui Yang, Shanlang Lin, Yan Li et al.

Abstract Using balanced panel data of 285 prefecture-level cities in China from 2003 to 2013, this paper studies the impact of High-speed rail (HSR) on environmental pollution. The results by Difference-in-Difference (DID) method show that HSR significantly reduces environmental pollution by 7.35% in China. In addition, we use Propensity Score Matching and DID (PSM-DID) method, as well as the instrumental variable method to deal with the endogenous problem, and we find that the results remain robust. HSR will not automatically reduce environmental pollution, but through the technical effect, allocation effect and substitution effect, which are the main factors to reduce environmental pollution. In addition, we also make further robustness tests for the conclusions of this paper. Further analysis shows that in cities with larger city scale, higher level of economic development, more abundant human capital or stricter environmental supervision, the pollution reduction effect of HSR is more significant. This paper provides some policy implications for improving city environmental quality and building an environment-friendly society.

Mona Kaamoush, Mohi El-Sayeh, Mohamed Y. Omar

Abstract The COVID-19 pandemic has significantly affected global society, influencing public health, economies, and the environment. This study examines the environmental impact of the pandemic on Alexandria Port, a key maritime hub in Egypt. By analyzing Automatic Identification System (AIS) data from the port area and multi-temporal satellite imagery from the Sentinel-5 Precursor (Sentinel-5p) satellite, the study investigates the changes in shipping activities and pollution emissions from 2018 to 2022. The aim was to assess the effect of the COVID-19 preventive measures on air quality in the vicinity of Alexandria Port, using satellite data provided by the European Space Agency’s geospatial processing engine. The study focused on several air quality parameters, including carbon monoxide (CO), nitrogen dioxide (NO₂), sulfur dioxide (SO₂), ozone (O₃), and aerosol properties such as Aerosol Optical Depth (AOD) and Absorbing Aerosol Index (AAI). The results revealed varying degrees of reduction in air pollutants during the COVID-19 lockdown, with each pollutant showing a distinct change in levels. Specifically, the AAI and AOD reached their lowest mean values in 2020, recording -1.2 and 214 mol/m2, respectively, which represents a significant reduction. Likewise, NO₂ and SO₂ concentrations dropped to their lowest mean values of 0.000048 and 0.000125 mol/m2 during the lockdown period, reflecting a decrease of approximately 30% compared to pre-lockdown levels in 2018–2019. Notably, CO and O₃ levels showed considerable reductions as well, with CO decreasing to 0.015 mol/m2 and O₃ reaching 0.125 mol/m2, both of which represented decreases of around 10% and 15%, respectively, compared to their 2019 levels. However, following the resumption of full-capacity maritime operations at Alexandria Port, pollution levels returned to pre-lockdown values, indicating that the environmental benefits of the lockdown were short-term. The study concludes that the COVID-19 lockdown had a positive short-term impact on air quality, particularly in reducing harmful pollutants like NO₂, SO₂, and aerosols. However, these improvements were transient, with pollution levels rebounding to pre-lockdown levels once maritime activities resumed. This highlights the importance of continuous monitoring and enforcement of environmental regulations to ensure long-term improvements in air quality. Effective pollution management strategies must be implemented to sustain the environmental gains observed during the pandemic lockdown.

Dongyu Cui, Yike Kang, Beidou Xi et al.

Organic pollutants remain a persistent threat to ecosystems and human health. In soils, humification gradually converts these compounds into stable humic substances and attenuates their toxicity, but the transformation can take decades—far too slow to match current pollution loads. In this Perspective, we argue that mature compost offers a pragmatic means to accelerate this process: it delivers partially humified intermediates that can “seed” soil humification and shorten its timescale from decades to seasons. Spectroscopic evidence shows that compost-derived humus is enriched in aromatic backbones and reactive functional groups (–COOH, –OH) that both catalyze further condensation of organic matter and immobilise pollutants through π–π stacking, hydrogen bonding and covalent coupling. By merging these catalytic and sorptive functions, compost amendments provide a scalable, low-cost route to the long-term stabilization of organic contaminants. We outline the key mechanistic questions that now need resolution—particularly the reactivity of specific intermediates in situ—to guide field trials and unlock the full potential of compost-driven accelerated humification as an environmental remediation platform.

Md Rashidunnabi, Fahmida Faiza Ananna, Kailash Hambarde et al.

Air pollution poses a critical health threat in cities worldwide, with nitrogen dioxide levels in Cork, Ireland exceeding World Health Organization safety standards by up to $278\%$. This study leverages artificial intelligence to predict air pollution with unprecedented accuracy, analyzing nearly ten years of data from five monitoring stations combined with 30 years of weather records. We evaluated 17 machine learning algorithms, with Extra Trees emerging as the optimal solution, achieving $77\%$ prediction accuracy and significantly outperforming traditional forecasting methods. Our analysis reveals that meteorological conditions particularly temperature, wind speed, and humidity are the primary drivers of pollution levels, while traffic patterns and seasonal changes create predictable pollution cycles. Pollution exhibits dramatic seasonal variations, with winter levels nearly double those of summer, and daily rush-hour peaks reaching $120\%$ above normal levels. While Cork's air quality shows concerning violations of global health standards, our models detected an encouraging $31\%$ improvement from 2014 to 2022. This research demonstrates that intelligent forecasting systems can provide city planners and environmental officials with powerful prediction tools, enabling life-saving early warning systems and informed urban planning decisions. The technology exists today to transform urban air quality management. All research materials and code are freely available at: https://github.com/MdRashidunnabi/Air-Pollution-Analysis.git

Daria Sakhanda, Joshué Helí Ricalde-Guerrero

This paper is devoted to developing a unified framework for stochastic growth models with environmental risk, in which rare but catastrophic shocks interact with capital accumulation and pollution. The analysis is based upon a general Poisson point process formulation, leading to non-local Hamilton-Jacobi-Bellman (HJB) equations that admit closed-form candidate solutions and yield a composite state variable capturing exposure to rare shocks. We consider cases where disaster risk is endogenized through a pollution-dependent intensity and, in the more general cases, it also accommodates for state-dependent events of varying magnitude. Our formulation captures how environmental degradation amplifies macroeconomic vulnerability and strengthens incentives for abatement. From a technical perspective, it provides tractable jump-diffusion control problems whose HJB equation decomposes naturally into capital and pollution components under power-type value function.

Candida Moffa, Alessandro Curcio, Camilla Merola et al.

This study details the development and validation of an airborne THz spectrometer designed for real-time, remote detection of atmospheric pollutants. The platform couples a stabilized unmanned aerial system (UAS) with a high-resolution terahertz continuous-wave (THz-CW) laser source and detector, enabling flexible, in situ spectroscopic analysis of dispersed atmospheric vapours. This proof-of-concept investigation confirms the feasibility of UAS-THz-CW systems for spatially resolved environmental pollutant monitoring. The demonstrated capability of this terahertz sensor for remote, multi-component detection of atmospheric contaminants holds significant potential for advancing air pollutant monitoring technologies, providing a pathway for more effective and portable detection. This advancement can contribute to the necessary alert actions for minimizing contaminants impacting public and environmental health, thereby safeguarding human and ecosystem health.

Sourish Das, Sudeep Shukla, Alka Yadav et al.

Air pollution poses significant health and environmental challenges, particularly in rapidly urbanizing regions. Delhi-National Capital Region experiences air pollution episodes due to complex interactions between anthropogenic emissions and meteorological conditions. Understanding the causal drivers of key pollutants such as $PM_{2.5}$ and ground $O_3$ is crucial for developing effective mitigation strategies. This study investigates the causal links of anthropogenic emissions on $PM_{2.5}$ and $O_3$ concentrations using predictive modeling and causal inference techniques. Integrating high-resolution air quality data from Jan 2018 to Aug 2023 across 32 monitoring stations, we develop predictive regression models that incorporate meteorological variables (temperature and relative humidity), pollutant concentrations ($NO_2, SO_2, CO$), and seasonal harmonic components to capture both diurnal and annual cycles. Here, we show that reductions in anthropogenic emissions lead to significant decreases in $PM_{2.5}$ levels, whereas their effect on $O_3$ remains marginal and statistically insignificant. To address spatial heterogeneity, we employ Gaussian Process modeling. Further, we use Granger causality analysis and counterfactual simulation to establish direct causal links. Validation using real-world data from the COVID-19 lockdown confirms that reduced emissions led to a substantial drop in $PM_{2.5}$ but only a slight, insignificant change in $O_3$. The findings highlight the necessity of targeted emission reduction policies while emphasizing the need for integrated strategies addressing both particulate and ozone pollution. These insights are crucial for policymakers designing air pollution interventions in other megacities, and offer a scalable methodology for tackling complex urban air pollution through data-driven decision-making.

Ivan Chuprov, Denis Derkach, Dmitry Efremenko et al.

This paper investigates the application of Physics-Informed Neural Networks (PINNs) for solving the inverse advection-diffusion problem to localize pollution sources. The study focuses on optimizing neural network architectures to accurately model pollutant dispersion dynamics under diverse conditions, including scenarios with weak and strong winds and multiple pollution sources. Various PINN configurations are evaluated, showing the strong dependence of solution accuracy on hyperparameter selection. Recommendations for efficient PINN configurations are provided based on these comparisons. The approach is tested across multiple scenarios and validated using real-world data that accounts for atmospheric variability. The results demonstrate that the proposed methodology achieves high accuracy in source localization, showcasing the stability and potential of PINNs for addressing environmental monitoring and pollution management challenges under complex weather conditions.

Yan Song, Tingting Yang, Zhenran Li et al.

Abstract Environmental regulation not only has a direct impact on environmental pollution, but also has an indirect impact on air pollution. This paper collects the panel data of 253 prefecture-level cities in China between 2004 and 2016. First, we used the Two-Stage Least Squares (2SLS) method to empirically analyze the direct and indirect effects of environmental regulation on environmental pollution. Second, this paper uses the panel threshold regression model to explore the non-linear effects of environmental regulation on environmental pollution. The results of our analysis indicate: (1) environmental regulation can directly alleviate environmental pollution. Additionally, technology innovation and industrial structure adjustment due to environmental regulation are also conducive to improving China’s environmental situation. The direct effects of environmental regulation are greater than the indirect effects; (2) the influence of environmental regulation on environmental pollution has a single threshold characteristic; (3) we also found that some control variables can significantly impact environmental pollution. The innovation of this article is mainly to explore the direct and indirect effects of environmental regulation on environmental pollution.

Yuanchao Bian, Kaiyi Song, Junhong Bai

Abstract Based on a review of previous literature and an investigation of the empirical facts of market segmentation and environmental pollution in China, this study mainly analyses the impact of market segmentation on environmental pollution from the perspective of resource misallocation. The results generated by the Dynamic Panel Econometric Model show that market segmentation had a significant deteriorating effect on environmental pollution during the investigated period. In addition, market segmentation has significantly aggravated the misallocation of labour and capital resources, which is also an important factor leading to environmental pollution. As for the heterogeneity of different pollutants, market segmentation has a significant negative impact on sulpfur dioxide (SO2), smoke and dust, suspended particles (PM2.5), while for wastewater and solid waste, the impact is not significant. Furthermore, the effect of market segmentation on environmental pollution was significant in the period 2002–2007, and in the Eastern area, the impact was not significant due to the relatively high degree of market integration. The conclusions will provide a reference for optimizing the relationship between local governments, and improving the environmental quality of China.

This study draws the link between COVID-19 and air pollution (ground ozone O3) from 29 February 2020 to 10 July 2020 in the top 10 affected States of the US. Utilizing quantile-on-quantile (QQ) estimation technique, we examine in what manner the quantiles of COVID-19 affect the quantiles of air pollution and vice versa. The primary findings confirm overall dependence between COVID-19 and air pollution. Empirical results exhibit a strong negative effect of COVID-19 on air pollution in New York, Texas, Illinois, Massachusetts, and Pennsylvania; especially at medium to higher quantiles, while New Jersey, Illinois, Arizona, and Georgia show strong negative effect mainly at lower quantiles. Contrarily, COVID-19 positively affects air pollution in Pennsylvania at extreme lower quantiles. On the other side, air pollution predominantly caused to increase in the intensity of COVID-19 cases across all states except lower quantiles of Massachusetts, and extreme higher quantiles of Arizona and New Jersey, where this effect becomes less pronounced or negative. Concludingly, a rare positive fallout of COVID-19 is reducing environmental pressure, while higher environmental pollution causes to increase the vulnerability of COVID-19 cases. These findings imply that air pollution is at the heart of chronic diseases, therefore the state government should consider these asymmetric channels and introduce appropriate policy measures to reset and control atmospheric emissions.

N. Mahmood, Zhaohua Wang, Bin Zhang

Abstract The global warming phenomenon emerges from the issue of climate change, which attracts the attention of intellectuals towards clean energy sources from dirty energy sources. Among clean sources, nuclear energy is getting immense attention among policymakers. However, the role of nuclear energy in pollution emissions reduction has remained inconclusive and demand for further investigation. Therefore, the current study contributes to extend knowledge by investigating the nexus between nuclear energy, economic growth, and CO2 emissions in a developing country context such as Pakistan for the period between 1973 and 2017. The auto-regressive distributive lag model summarizes the nuclear energy has negative effect on environmental pollution as it releases carbon emission in the environment. Moreover, vector error correction Granger causality provides evidence for bidirectional causality between nuclear energy and carbon emissions. These interesting findings provide new insight, and policy guidelines provided based on these results.

Cong Cao, Ramit Debnath, R. Michael Alvarez

Policymakers frequently analyze air quality and climate change in isolation, disregarding their interactions. This study explores the influence of specific climate factors on air quality by contrasting a regression model with K-Means Clustering, Hierarchical Clustering, and Random Forest techniques. We employ Physics-based Deep Learning (PBDL) and Long Short-Term Memory (LSTM) to examine the air pollution predictions. Our analysis utilizes ten years (2009-2018) of daily traffic, weather, and air pollution data from three major cities in Norway. Findings from feature selection reveal a correlation between rising heating degree days and heightened air pollution levels, suggesting increased heating activities in Norway are a contributing factor to worsening air quality. PBDL demonstrates superior accuracy in air pollution predictions compared to LSTM. This paper contributes to the growing literature on PBDL methods for more accurate air pollution predictions using environmental variables, aiding policymakers in formulating effective data-driven climate policies.