Hasil untuk "Environmental pollution"

Pratikshya Sapkota, Umesh Bastola

Fengsheng Chien, Muhammad Sadiq, M. Nawaz et al.

Xiaojie Ma, Y. Chai, Ping Li et al.

Metal-organic frameworks (MOFs), an emerging class of porous hybrid inorganic-organic crystals, exhibit very important application prospects in gas storage and separation, heterogeneous catalysis, sensing, drug release, environmental decontamination, etc., due to their competitive advantages over other traditional porous materials (e.g., activated carbon and zeolite), including high surface areas, adjustable pore size, uniformly distributed metal centers, and tunable functionalities. However, MOF particles are usually difficult to be processed into application-specific devices because of their brittleness, insolubility, difficulty in molding, and low compatibility with other materials. It is an urgent need to shape MOF nanocrystals into various useful configurations by developing effective fabrication methods. Specifically, versatile functional MOF films with robustness and operation flexibility are highly desired. Although an increasing number of MOF films and their diverse applications have been demonstrated, this field is still at an emerging stage with challenging issues. In this Account, we describe our recent research progress on controllable synthesis of MOF films, highlighting postsynthetic polymerization, in situ interweaving, and solvent-free hot-pressing methods. Basically, two main synthesis concepts are involved, including incorporation of the performed MOF particles into polymer matrix and in situ growth of MOF coatings on surface. In MOF/polymer hybrid films, MOF nanocrystals were covalently linked by flexible polymer chains via graft copolymerization, interconnected by functional polymer chains via in situ polymerization, or adhered to polymer matrix via specific interactions at interface, consequently leading to a molecular-level homogeneous membrane or functional coating layer or foam. In these examples, the existence of polymer endows MOF films with favorable features of processability and flexibility, along with new functions. Moreover, we developed an in situ solvent-free hot-pressing method as a general approach for efficient fabrication of MOF coatings on various commercial substrates (e.g., cloth and metal foils), where metal ions or ligands were chemically bonded to the surface functional groups or metal sites at the early stage of nucleation and subsequently assembled into continuous, uniform, and stable MOF layers under confined conditions. We further extended it to a scalable manufacturing method, roll-to-roll production. MOF films severing as filters (MOFilters) have significant applications in air and water purification. They show high and stable performance in PM capture along with a low pressure drop, holding promise of application in both industrial and residential environments. Moreover, MOFilters can remove SO2 and O3 from air by adsorption and catalytic decomposition, respectively. Given the functional diversity of MOFs, mixed pollutants in solution could also be efficiently trapped by multifunctional MOF hollow tubes. We believe this Account will offer new insights for design and preparation of functional MOF films and coatings and accelerate the practical applications of MOFs.

M. Azam, M. Alam, Muhammad Haroon Hafeez

Abstract An increasing number of studies reveal that tourism industry makes a substantial contribution towards socioeconomic growth and development of tourism led economies. However, tourism steered economic growth and development is achieved at the cost of environmental pollution and degradation. The main objective of this study is to examine the effect of tourists’ arrivals on environmental pollution caused by Carbon Dioxide emissions in Malaysia, Thailand and Singapore over the period of 1990–2014. Some other regressors namely energy consumption and income are also used in the multivariate model. The Zivot–Andrews test is employed to determine unit-root and presence of structural break in the data. Fully Modified Ordinary Least Squares estimator is used as an analytical technique for unknown parameters estimation. The empirical results reveal that tourism has a significant positive effect on environmental pollution in Malaysia. However, an inverse relationship between tourism and environmental pollution is observed in Thailand and Singapore. Empirical findings suggest that sustainable economic growth and development should be ensured by implementing prudent public policy where host governments must strive to promote socially and environmentally responsible tourism industries in their respective countries.

Qianqian Liu, Shaojian Wang, Wenzhong Zhang et al.

Abdulkadir Abdulrashid Rafindadi, Ibrahim Muhammad Muye, R. Kaita

P. Chowdhary, A. Raj, R. Bharagava

D. Ma, Hongyu Duan, Jiang-feng Liu et al.

Gangue, produced from coal mining and washing process, is a serious threat to the ground environment. Gangue backfilling mining method can solve this problem and reduce mining-induced hazards, e.g., controlling surface subsidence and preventing water inrush from seeping into goaf by cracks in overlying strata. In this paper, effects of the original particle size distribution (PSD) and water content on the particle crushing behavior and seepage properties of granular gangues were investigated. Experimental results show that the crushing behavior can promote the compaction of gangue particles; the variation of PSD after crushing reveals distinct fractal characteristics. With the increasing compression stress, the particle crushing ratio and fractal dimension increase, while the permeability decreases. Due to the rearrangement of particles and newly generated fine particles filled the gap among larger particles, it is difficult to reduce the permeability by increasing the compressive stress. In addition, the variation of fractal dimensions is similar to the crushing ratio, so the particle crushing can be illustrated by fractal dimensions. The relationship between porosity and permeability established by the Kozeny-Carman equation can model the effect of particle crushing in this research. The reliability of the equation is verified by the comparison of model result and experimental data. To increase the mitigation rate of mining-induced hazards and environmental pollution by GBM method, granular gangues can be crushed into smaller particles and dehydrated before backfilling.

Zehao Lin

Global climate warming and air pollution pose severe threats to economic development and public safety, presenting significant challenges to sustainable development worldwide. Corporations, as key players in resource utilization and emissions, have drawn increasing attention from policymakers, researchers, and the public regarding their environmental strategies and practices. This study employs a two-way fixed effects panel model to examine the impact of environmental information disclosure on corporate environmental performance, its regional heterogeneity, and the underlying mechanisms. The results demonstrate that environmental information disclosure significantly improves corporate environmental performance, with the effect being more pronounced in areas of high population density and limited green space. These findings provide empirical evidence supporting the role of environmental information disclosure as a critical tool for improving corporate environmental practices. The study highlights the importance of targeted, region-specific policies to maximize the effectiveness of disclosure, offering valuable insights for promoting sustainable development through enhanced corporate transparency.

Ofek Aloni, Gal Perelman, Barak Fishbain

Synthetic datasets are widely used in many applications, such as missing data imputation, examining non-stationary scenarios, in simulations, training data-driven models, and analyzing system robustness. Typically, synthetic data are based on historical data obtained from the observed system. The data needs to represent a specific behavior of the system, yet be new and diverse enough so that the system is challenged with a broad range of inputs. This paper presents a method, based on discrete Fourier transform, for generating synthetic time series with similar statistical moments for any given signal. The suggested method makes it possible to control the level of similarity between the given signal and the generated synthetic signals. Proof shows analytically that this method preserves the first two statistical moments of the input signal, and its autocorrelation function. The method is compared to known methods, ARMA, GAN, and CoSMoS. A large variety of environmental datasets with different temporal resolutions, and from different domains are used, testing the generality and flexibility of the method. A Python library implementing this method is made available as open-source software.

Agata Galkiewicz

Random disturbances such as air pollution may affect cognitive performance, which, particularly in high-stakes settings, may have severe consequences for an individual's productivity and well-being. This paper examines the short-term effects of air pollution on school leaving exam results in Poland. I exploit random variation in air pollution between the days on which exams are held across three consecutive school years. I aim to capture this random variation by including school and time fixed effects. The school-level panel data is drawn from a governmental program where air pollution is continuously measured in the schoolyard. This localized hourly air pollution measure is a unique feature of my study, which increases the precision of the estimated effects. In addition, using distant and aggregated air pollution measures allows me for the comparison of the estimates in space and time. The findings suggest that a one standard deviation increase in the concentration of particulate matter PM2.5 and PM10 decreases students' exam scores by around 0.07-0.08 standard deviations. The magnitude and significance of these results depend on the location and timing of the air pollution readings, indicating the importance of the localized air pollution measure and the distinction between contemporaneous and lingering effects. Further, air pollution effects gradually increase in line with the quantiles of the exam score distribution, suggesting that high-ability students are more affected by the random disturbances caused by air pollution.

Tin Lai, Farnaz Farid, Yueyang Kuan et al.

Detecting heavy metal pollution in soils and seaports is vital for regional environmental monitoring. The Pollution Load Index (PLI), an international standard, is commonly used to assess heavy metal containment. However, the conventional PLI assessment involves laborious procedures and data analysis of sediment samples. To address this challenge, we propose a deep-learning-based model that simplifies the heavy metal assessment process. Our model tackles the issue of data scarcity in the water-sediment domain, which is traditionally plagued by challenges in data collection and varying standards across nations. By leveraging transfer learning, we develop an accurate quantitative assessment method for predicting PLI. Our approach allows the transfer of learned features across domains with different sets of features. We evaluate our model using data from six major ports in New South Wales, Australia: Port Yamba, Port Newcastle, Port Jackson, Port Botany, Port Kembla, and Port Eden. The results demonstrate significantly lower Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) of approximately 0.5 and 0.03, respectively, compared to other models. Our model performance is up to 2 orders of magnitude than other baseline models. Our proposed model offers an innovative, accessible, and cost-effective approach to predicting water quality, benefiting marine life conservation, aquaculture, and industrial pollution monitoring.

Tiantian Yang, Richard S. J. Tol

To address the dual environmental challenges of pollution and climate change, China has established multiple environmental markets, including pollution emissions trading, carbon emissions trading, energy-use rights trading, and green electricity trading. Previous empirical studies suffer from known biases arising from time-varying treatment and multiple treatments. To address these limitations, this study adopts a dynamic control group design and combines Difference-in-Difference (DiD) and Artificial Counterfactual (ArCo) empirical strategies. Using panel data on A-share listed companies from 2000 to 2024, this study investigates the marginal effects and interactive impacts of multiple environmental markets implemented in staggered and overlapping phases. Existing pollution emissions trading mitigates the negative effects of carbon emission trading. Carbon trading suppresses (improves) financial performance (if implemented alongside energy-use rights trading). The addition of energy-use rights or green electricity trading in regions already covered by carbon or pollution markets has no significant effects.

Isabella Trierweiler, Konstantin Gerbig, Malena Rice

Von Zeipel-Lidov-Kozai (ZLK) oscillations, induced by bound, perturbative companions to white dwarfs, have been suggested as a dynamical mechanism that may contribute to white dwarf pollution. To trigger ZLK oscillations, however, a 3-body system must reach a sufficiently large mutual inclination between orbits. The occurrence of these high-mutual-inclination configurations can be curtailed by dissipative precession at the protoplanetary disk stage, which pushes exoplanet-hosting close binary systems toward preferential orbit-orbit alignment. In this work, we constrain the fraction of white dwarfs with binary companions that can undergo ZLK-driven pollution given the effects of dissipative precession. To accrete pollution via ZLK oscillations, a white dwarf binary system must be sufficiently inclined and the characteristic timescale of the oscillations must be sufficiently short to perturb material within the white dwarf's cooling age. Considering a sample of 4400 known white dwarf/main sequence binaries, we find that $50-70\%$ have favorable parameters for ZLK pollution, depending on the orbital separation of the polluting body. While the conditions for oscillations are favorable, the tendency for ZLK to result in massive but more infrequent polluters likely restricts the rates of ZLK-induced pollution among the observed population. In general, dissipative precession is a limiting factor in pollution rates for more closely separated binaries (initial separations $<500-800$~au), while ZLK timescale constraints are most limiting for wider binaries.

Zhineng Liu, Xiaolan Lao, Dongjing Zhou et al.

Pesticides are essential for crop protection and agricultural yield enhancement. However, their entry into water bodies, particularly drinking water sources, poses threats to human health and aquatic ecosystems. The seasonal variation, and potential risks of pesticides in drinking water sources in Guangdong, China were investigated. The total pesticide concentrations were significantly elevated during the dry season compared to the wet season (p < 0.01; |r| = 0.77; 95 % CI for difference in medians: [92.6, 315] ng/L). Neonicotinoid pesticides predominated in more than half of the samples. Across both seasons, river water sources displayed significantly higher total pesticide concentrations than reservoir sources (dry season: p < 0.05, |r| =0.60, 95 % CI [106,359] ng/L; wet season: p < 0.01, |r| =0.66, 95 % CI [70.3, 412] ng/L). Health risk assessments indicated that pesticides in drinking water sources pose non-carcinogenic and carcinogenic risks associated with long-term direct consumption, particularly for children aged 3–4 years. Ecological risk assessments revealed high potential risks to aquatic organisms (summed risk quotient > 1), particularly from neonicotinoid pesticides. These findings highlight the necessity of seasonally adaptive controls for pesticides and water quality to reduce risks to public health and ecosystems.

Yuyao Zhang, Ke Guo, Xiao Zhou

Artificial light plays an integral role in modern cities, significantly enhancing human productivity and the efficiency of civilization. However, excessive illumination can lead to light pollution, posing non-negligible threats to economic burdens, ecosystems, and human health. Despite its critical importance, the exploration of its causes remains relatively limited within the field of artificial intelligence, leaving an incomplete understanding of the factors contributing to light pollution and sustainable illumination planning distant. To address this gap, we introduce a novel framework named Causally Aware Generative Adversarial Networks (CAGAN). This innovative approach aims to uncover the fundamental drivers of light pollution within cities and offer intelligent solutions for optimal illumination resource allocation in the context of sustainable urban development. We commence by examining light pollution across 33,593 residential areas in seven global metropolises. Our findings reveal substantial influences on light pollution levels from various building types, notably grasslands, commercial centers and residential buildings as significant contributors. These discovered causal relationships are seamlessly integrated into the generative modeling framework, guiding the process of generating light pollution maps for diverse residential areas. Extensive experiments showcase CAGAN's potential to inform and guide the implementation of effective strategies to mitigate light pollution. Our code and data are publicly available at https://github.com/zhangyuuao/Light_Pollution_CAGAN.

Ihsane Gryech, Chaimae Assad, Mounir Ghogho et al.

According to the World Health Organization (WHO), air pollution kills seven million people every year. Outdoor air pollution is a major environmental health problem affecting low, middle, and high-income countries. In the past few years, the research community has explored IoT-enabled machine learning applications for outdoor air pollution prediction. The general objective of this paper is to systematically review applications of machine learning and Internet of Things (IoT) for outdoor air pollution prediction and the combination of monitoring sensors and input features used. Two research questions were formulated for this review. 1086 publications were collected in the initial PRISMA stage. After the screening and eligibility phases, 37 papers were selected for inclusion. A cost-based analysis was conducted on the findings to highlight high-cost monitoring, low-cost IoT and hybrid enabled prediction. Three methods of prediction were identified: time series, feature-based and spatio-temporal. This review's findings identify major limitations in applications found in the literature, namely lack of coverage, lack of diversity of data and lack of inclusion of context-specific features. This review proposes directions for future research and underlines practical implications in healthcare, urban planning, global synergy and smart cities.

Timo Häckel, Luca von Roenn, Nemo Juchmann et al.

The trend for Urban Air Mobility (UAM) is growing with prospective air taxis, parcel deliverers, and medical and industrial services. Safe and efficient UAM operation relies on timely communication and reliable data exchange. In this paper, we explore Cooperative Perception (CP) for Unmanned Aircraft Systems (UAS), considering the unique communication needs involving high dynamics and a large number of UAS. We propose a hybrid approach combining local broadcast with a central CP service, inspired by centrally managed U-space and broadcast mechanisms from automotive and aviation domains. In a simulation study, we show that our approach significantly enhances the environmental awareness for UAS compared to fully distributed approaches, with an increased communication channel load, which we also evaluate. These findings prompt a discussion on communication strategies for CP in UAM and the potential of a centralized CP service in future research.

Alex T. Ford, Amruthavarshini Shankar, Sarah Reynolds et al.

Abstract The planet faces a triple crisis from climate change, biodiversity loss, and pollution. Like any country, the UK needs to attract the best available talent to become thought leaders to overcome these global challenges. Several Science, Technology, Engineering and Maths subjects in the UK face challenges with attracting ethnic minority students. As part of a wider project on diversity in UK environmental/marine science, we analysed university applications amongst 180 environmental and 88 marine science degrees in the UK between 2019–2021. We found them to be the least diverse degree subjects for ethnic minority students and Asian students were less likely to be accepted than white students on environmental science degrees. A survey of UK marine science course leaders highlights the belief that these issues impact the pipeline of diversity from higher education institutions to marine/environmental science careers. In this paper, we discuss the impacts of these findings and develop a roadmap to change.

Yuan Ma, Bernhard Brümmer, Xiaohua Yu

This study is aimed at assessing agricultural and environmental performance and analyzing whether observable productivity changes stem from technologically induced or environmentally induced components. Based on individual farm household data from Hubei Province covering the period 2004 to 2010, we decompose total factor productivity (TFP) into technical efficiency change (TEC), technical change (TC), scale effect (SE), and the environmentally related allocative effect (AE) as a means of evaluating environmental performance. The empirical results indicate that the average TFP decrease rate is 2.8%, which reflects the comprehensive outcome of all relevant components. Regarding direct pollution-related inputs (fertilizer and land), improving nitrogen (N) fertilizer application efficiency and land use efficiency can contribute not only to less cropland expansion and greater productivity growth but also to N loss reduction and N pollution abatement in the short and long term. Concerning indirect pollution-related inputs (labor, intermediate input, etc.), although increases in quasi-fixed inputs (labor and intermediate input) can lead to both N and productivity growth, the magnitude of the positive effects of quasi-fixed inputs on productivity cannot offset the negative effect of fertilizer on productivity; thus, more scientific and economical fertilizer application is the key to improving agricultural productivity and benefiting the environment and the ecosystem.