搜索结果：Environmental pollution (Barking, Essex : 1987)

Ambient air pollution is a pervasive issue with wide-ranging effects on human health, ecosystem vitality, and economic structures. Utilizing data on ambient air pollution concentrations, researchers can perform comprehensive analyses to uncover the multifaceted impacts of air pollution across society. To this end, we introduce Environmental Insights, an open-source Python package designed to democratize access to air pollution concentration data. This tool enables users to easily retrieve historical air pollution data and employ a Machine Learning model for forecasting potential future conditions. Moreover, Environmental Insights includes a suite of tools aimed at facilitating the dissemination of analytical findings and enhancing user engagement through dynamic visualizations. This comprehensive approach ensures that the package caters to the diverse needs of individuals looking to explore and understand air pollution trends and their implications.

Air pollution regulation is central to urban public health governance, but estimating its effects is difficult because policies are implemented non-randomly and pollution trajectories are shaped by meteorology, socioeconomic change, temporal trends, and overlapping interventions. This study develops an uncertainty-aware Bayesian deep learning framework to estimate the aggregate effect of air pollution regulations on PM$_{2.5}$ concentrations in London from 2010 to 2020. The framework integrates daily PM$_{2.5}$ observations from Inner London monitoring stations, meteorological covariates, annual socioeconomic indicators, month-of-year and day-of-week indicators, and daily regulation status data for 32 policy measures. A Bayesian LSTM captures temporal dependencies in environmental and socioeconomic covariates, Bayesian embedding layers represent temporal and regulation status inputs, and a regulation status prediction branch supports propensity score-based adjustment for non-random policy implementation. Regulatory effects are estimated by comparing observed PM$_{2.5}$ concentrations with counterfactual predictions under a hypothetical no-regulation scenario, with uncertainty summa

Is environmental injustice driven by a global network of multinational companies? Is there a global environmental justice movement resisting extractivism? This study addresses these questions through a social network analysis of firms and Environmental Justice Organizations (EJOs) involved in socioenvironmental conflicts. Using Environmental Justice Atlas (EJAtlas) data, we examine 3,396 conflicts involving 6,244 companies and 11,231 EJOs. The findings reveal evident structural asymmetries. Corporations form cohesive, sector-specific global networks that support coordination across regions. Conversely, EJOs form a larger, decentralized, and self-organized network characterised by sparse localised interconnections that enable regional coordination across sectors. A limited number of international EJOs involved in many conflicts worldwide weaves a global environmental justice network to match the global spread of multinational corporations and supply chains. This research study shows that these contrasting network structures reflect the distinct purposes of their constituent actors: sectoral expansion for corporations and environmental and territorial defense for EJOs.

Oil spill incidents pose severe threats to marine ecosystems and coastal environments, necessitating rapid detection and monitoring capabilities to mitigate environmental damage. In this paper, we demonstrate how artificial intelligence, despite the inherent high computational and memory requirements, can be efficiently integrated into marine pollution monitoring systems. More precisely, we propose a drone-based smart monitoring system leveraging a compressed deep learning U-Net architecture for oil spill detection and thickness estimation. Compared to the standard U-Net architecture, the number of convolution blocks and channels per block are modified. The new model is then trained on synthetic radar data to accurately predict thick oil slick thickness up to 10 mm. Results show that our optimized Tiny U-Net achieves superior performance with an Intersection over Union (IoU) metric of approximately 79%, while simultaneously reducing the model size by a factor of $\sim$269x compared to the state-of-the-art. This significant model compression enables efficient edge computing deployment on field-programmable gate array (FPGA) hardware integrated directly into the drone platform. Hardw

In this paper investigations by the same authors on environmental issues concerning the control of the pollution produced by human activities have been extended to include costs related to environmental interventions. The proposed model consists of a spatially structured dynamic economic growth model which takes into account the level of pollution induced by production, a possible taxation based on the amount of produced pollution, and possible environmental interventions. It has been analyzed an optimal harvesting control problem with an objective function composed of four terms, namely the intertemporal utility of the decision maker, the space-time average of the level of pollution in the habitat, the disutility due to the imposition of taxation and the cost of environmental interventions. A specific novelty in the model proposed here is the localization of the possible interventions to a subregion of the whole habitat. Computational experiments have been carried out to exemplify the outcomes of the proposed model.

Examining the economic impact of noise pollution from a lens of household is a burgeoning field in the study of environmental sustainability. Economics studies cover the source, measure, consequence of noise pollution, as well as the econometric methods used to identify the causal impact of noise pollution on socioeconomic welfare. There are broadly four major noise origins along with the industrial growth and urban development, which are airport, railway, urban traffic, and neighborhood. Four general kinds of measures or data sources are used in economics studies to capture the noise variations, namely, proximity to noise origins, real-time noise monitor records, household surveys, and administrative records on noise complaints. The socioeconomic consequences of noise pollution span from physical or mental health to happiness, violence and suicide, housing market capitalization, and inequality. In economics studies, generally three types of econometric methods are used to identify causal impact of noise pollution on the household's welfare, which are instrumental variable estimation, difference-in-difference estimation, randomized and quasi-natural experiments. The causal impact o

We study the design of fair allocation rules for the abatement of riparian pollution. To do so, we consider the so-called river pollution claims model, recently introduced by Yang et al. (2025) to distribute a budget of emissions permits among agents (cities, provinces, or countries) located along a river. In such a model, each agent has a claim reflecting population, emission history, and business-as-usual emissions, and the issue is to allocate among them a budget that is lower (or equal) than the aggregate claim. For environmental reasons, the specific location along the river where pollutants are emitted is an important concern (the more upstream the location is the higher the damage of polluting the river). We characterize a class of geometric rules that adjust proportional allocations to compromise between fairness and environmental concerns. Our class is an alternative to the one proposed by Yang et al. (2025). We compare both alternatives through an axiomatic study, as well as an illustration for the case study of the Tuojiang Basin in China.

Air pollution is the origination of particulate matter, chemicals, or biological substances that brings pain to either humans or other living creatures or instigates discomfort to the natural habitat and the airspace. Hence, air pollution remains one of the paramount environmental issues as far as metropolitan cities are concerned. Several air pollution benchmarks are even said to have a negative influence on human health. Also, improper detection of air pollution benchmarks results in severe complications for humans and living creatures. To address this aspect, a novel technique called, Discretized Regression and Least Square Support Vector (DR-LSSV) based air pollution forecasting is proposed. The results indicate that the proposed DR-LSSV Technique can efficiently enhance air pollution forecasting performance and outperforms the conventional machine learning methods in terms of air pollution forecasting accuracy, air pollution forecasting time, and false positive rate.

Artificial intelligence (AI) is often presented as a key tool for addressing societal challenges, such as climate change. At the same time, AI's environmental footprint is expanding increasingly. This report describes the systemic environmental risks of artificial intelligence, in particular, moving beyond direct impacts such as energy and water usage. Systemic environmental risks of AI are emergent, cross-sector harms to climate, biodiversity, freshwater, and broader socioecological systems that arise primarily from AI's integration into social, economic, and physical infrastructures, rather than its direct resource use, and that propagate through feedbacks, yielding nonlinear, inequitable, and potentially irreversible impacts. While these risks are emergent and quantification is uncertain, this report aims to provide an overview of systemic environmental risks. Drawing on a narrative literature review, we propose a three-level framework that operationalizes systemic risk analysis. The framework identifies the structural conditions that shape AI development, the risk amplification mechanisms that propagate environmental harm, and the impacts that manifest as observable ecological

The space-borne gravitational wave detectors such as TianQin offers a new window to test General Relativity by observing the early inspiral phase of stellar-mass binary black holes. A key concern arises if these stellar-mass binary black holes reside in gaseous environments such as active galactic nucleus accretion disks, where environmental effects imprint detectable modulations on the gravitational waveform. Using Bayesian inference on simulated signals containing both environmental and dipole deviation, we have assessed the extent to which the presence of environmental effects affects the detectability of dipole radiation. Our results demonstrate that even in the presence of strong environmental coupling, the dipole parameter can be recovered with high precision, and the evidence for dipole radiation remains distinguishable. Crucially, we find that the existence of environmental effects does not fundamentally impede the identification of dipole radiation, provided both effects are simultaneously modelled in the inference process. This study establishes that future tests of modified gravity with space-borne observatories can remain robust even for sources in astrophysical environ

Artificial Intelligence (AI) is changing the world, but its impacts on the environment and human well-being remain uncertain. We conducted a systematic literature review of 1,291 studies selected from 6,655 records, identifying the main impacts of AI and how they are assessed. The evidence reveals an uneven landscape: 72% of environmental studies focus narrowly on energy use and CO2 emissions, while only 11% consider systemic effects. Well-being research is largely conceptual and overlooks subjective dimensions. Strikingly, 83% of environmental studies portray AI's impacts as positive, while well-being analyses show a near-even split overall (44% positive; 46% negative). However, this split masks differences across well-being dimensions. While the impacts of AI on income and health are expected to be positive, its impacts on inequality, social cohesion, and employment are expected to be negative. Based on our findings, we suggest several areas for future research. Environmental assessments should incorporate water, material, and biodiversity impacts, and apply a full life-cycle perspective, while well-being research should prioritise empirical analyses. Evaluating AI's overall impa

We analyze the spatial distributions of two groups of benthic foraminifera (Adelosina spp. + Quinqueloculina spp. and Elphidium spp.), along Sicilian coast, and their correlation with six different heavy metals, responsible for the pollution. Samples were collected inside the Gulf of Palermo, which has a high level of pollution due to heavy metals, and along the coast of Lampedusa island (Sicily Channel, Southern Mediterranean), which is characterized by unpolluted sea waters. Because of the environmental pollution we find: (i) an anticorrelated spatial behaviour between the two groups of benthic foraminifera analyzed; (ii) an anticorrelated (correlated) spatial behaviour between the first (second) group of benthic foraminifera with metal concentrations; (iii) an almost uncorrelated spatial behaviour between low concentrations of metals and the first group of foraminifera in clean sea water sites. We introduce a two-species model based on the generalized Lotka-Volterra equations in the presence of a multiplicative noise, which models the interaction between species and environmental pollution due to the presence in top-soft sediments of heavy metals. The interaction coefficients be

In this paper, we propose a Physics-Informed Neural Network framework for time-dependent simulations of pollution propagation originating from moving emission sources. We formulate a robust variational framework for the time-dependent advection-diffusion problem and establish the boundedness and inf-sup stability of the corresponding discrete weak formulation. Based on this mathematical foundation, we construct a robust loss function that is directly related to the true approximation error, defined as the difference between the neural network approximation and the (unknown) exact solution. Additionally, a collocation-based strategy is introduced to speed up neural network training. As a case study, we investigate pollution propagation caused by snowmobile traffic in Longyearbyen, Spitsbergen, supported by detailed in-field measurements collected using dedicated sensors. The proposed framework is applied to analyze the effects of thermal inversion on pollutant accumulation. Our results demonstrate that thermal inversion traps dense and humid air masses near the ground, significantly enhancing particulate matter (PM) concentration and worsening local air quality.

Real and effective regulation of contributions to greenhouse gas emissions and pollutants requires unbiased and truthful monitoring. Blockchain has emerged not only as an approach that provides verifiable economical interactions but also as a mechanism to keep the measurement, monitoring, incentivation of environmental conservationist practices and enforcement of policy. Here, we present a survey of areas in what blockchain has been considered as a response to concerns on keeping an accurate recording of environmental practices to monitor levels of pollution and management of environmental practices. We classify the applications of blockchain into different segments of concerns, such as greenhouse gas emissions, solid waste, water, plastics, food waste, and circular economy, and show the objectives for the addressed concerns. We also classify the different blockchains and the explored and designed properties as identified for the proposed solutions. At the end, we provide a discussion about the niches and challenges that remain for future research.

Machine learning inference occurs at a massive scale, yet its environmental impact remains poorly quantified, especially on low-resource hardware. We present ML-EcoLyzer, a cross-framework tool for measuring the carbon, energy, thermal, and water costs of inference across CPUs, consumer GPUs, and datacenter accelerators. The tool supports both classical and modern models, applying adaptive monitoring and hardware-aware evaluation. We introduce the Environmental Sustainability Score (ESS), which quantifies the number of effective parameters served per gram of CO$_2$ emitted. Our evaluation covers over 1,900 inference configurations, spanning diverse model architectures, task modalities (text, vision, audio, tabular), hardware types, and precision levels. These rigorous and reliable measurements demonstrate that quantization enhances ESS, huge accelerators can be inefficient for lightweight applications, and even small models may incur significant costs when implemented suboptimally. ML-EcoLyzer sets a standard for sustainability-conscious model selection and offers an extensive empirical evaluation of environmental costs during inference.

This paper introduces AIJIM, the Artificial Intelligence Journalism Integration Model -- a novel framework for integrating real-time AI into environmental journalism. AIJIM combines Vision Transformer-based hazard detection, crowdsourced validation with 252 validators, and automated reporting within a scalable, modular architecture. A dual-layer explainability approach ensures ethical transparency through fast CAM-based visual overlays and optional LIME-based box-level interpretations. Validated in a 2024 pilot on the island of Mallorca using the NamicGreen platform, AIJIM achieved 85.4\% detection accuracy and 89.7\% agreement with expert annotations, while reducing reporting latency by 40\%. Unlike conventional approaches such as Data-Driven Journalism or AI Fact-Checking, AIJIM provides a transferable model for participatory, community-driven environmental reporting, advancing journalism, artificial intelligence, and sustainability in alignment with the UN Sustainable Development Goals and the EU AI Act.

Unscrupulous outdoor lighting produces a number of effects that are currently included under the term light pollution. Its consequences (e.g. loss of resources by energy waste), are being recognized for some time, as well as the possibility to mitigate this pollution. In the present work, we present some lines of action developed at the Facultad Regional San Nicolás of National Technological University (UTN) of Argentina to include the CL as a regular topic of study in the problems of air pollution.

Air pollution is associated with human diseases and has been found to be related to premature mortality. In response, environmental policies have been adopted in many countries, to decrease anthropogenic air pollution for the improvement of long-term air quality, since most air pollutant sources are anthropogenic. However, air pollution fluctuations have been found to strongly depend on the weather dynamics. This raises a fundamental question: What are the significant atmospheric processes that affect the local daily variability of air pollution? For this purpose, we develop here a multi-layered network analysis to detect the interlinks between the geopotential height of the upper air (~5 km) and surface air pollution in both China and the USA. We find that Rossby waves significantly affect air pollution fluctuations through the development of cyclone and anticyclone systems, and further affect the local stability of the air and the winds. The significant impacts of Rossby waves on air pollution are found to underlie most of the daily fluctuations in air pollution. Thus, the impact of Rossby waves on human life is greater than previously assumed. The rapid warming of the Arctic cou

Air pollution poses a serious threat to sustainable environmental conditions in the 21st century. Its importance in determining the health and living standards in urban settings is only expected to increase with time. Various factors ranging from artificial emissions to natural phenomena are known to be primary causal agents or influencers behind rising air pollution levels. However, the lack of large scale data involving the major artificial and natural factors has hindered the research on the causes and relations governing the variability of the different air pollutants. Through this work, we introduce a large scale city-wise dataset for exploring the relationships among these agents over a long period of time. We also introduce a transformer based model - cosSquareFormer, for the problem of pollutant level estimation and forecasting. Our model outperforms most of the benchmark models for this task. We also analyze and explore the dataset through our model and other methodologies to bring out important inferences which enable us to understand the dynamics of the causal agents at a deeper level. Through our paper, we seek to provide a great set of foundations for further research