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Simple Summary: Dairy farming is a significant source of pollutant gas emissions, contributing to climate change and air quality degradation. Understanding these emissions in milk production promotes productive efficiency and sustainable resource use. The aim of this paper is to provide a comprehensive analysis of ammonia and greenhouse gas emissions in dairy farming, utilizing bibliometric analysis methods. Articles featuring experimental data collection were selected, exclusively in English. The databases considered were Scopus and Web of Science. The analyses were conducted using Bibliometrix version 4.0.0 and VOSviewer version 1.6.20 tools to create knowledge maps visualizing information on research countries, institutions, author collaborations, and keyword networks. The search yielded 187 articles related to major pollutant gases such as methane, ammonia, carbon dioxide, nitrous oxide, and hydrogen sulfide. This study provided valuable insights into gas emission sources, air quality, environmental sustainability, and animal welfare, as well as identifying gaps and critical points in research on the topic. Ammonia and greenhouse gas emissions in dairy farming are crucial for climate change mitigation and sustainable agriculture. This research can contribute to academic interests while enhancing agricultural practices and informing environmental policies. The relevance of this research will persist with the growing demand for sustainable practices, as more researchers and organizations recognize the importance of gas emissions and mitigation research. A deeper understanding of gas emissions in milk production is crucial for promoting productive efficiency, sustainable resource use, and animal welfare. This paper aims to analyze ammonia and greenhouse gas emissions in dairy farming using bibliometric methods. A total of 187 English-language articles with experimental data from the Scopus and Web of Science databases (January 1987 to April 2024) were reviewed. Publications notably increased from 1997, with the highest number of papers published in 2022. Research mainly focuses on ammonia and methane emissions, including quantification, volatilization, and mitigation strategies. Other gases like carbon dioxide, nitrous oxide, and hydrogen sulfide were also studied. Key institutions include the University of California–Davis and Aarhus University. Bibliometric analysis revealed research evolution, identifying trends, gaps, and future research opportunities. This bibliometric analysis offers insights into emissions, air quality, sustainability, and animal welfare in dairy farming, highlighting areas for innovative mitigation strategies to enhance production sustainability. This research contributes to academia, enhancing agricultural practices, and informing environmental policies. It is possible to conclude that this research is a valuable tool for understanding the evolution of research on gas emissions in dairy cattle facilities, providing guidance for future studies and interventions to promote more sustainable production. [ABSTRACT FROM AUTHOR]

Various forms of pollution carry a substantial burden with respect to increasing the risk of causing and exacerbating noncommunicable diseases, especially cardiovascular disease. The first part of this 2-part series on pollution and cardiovascular disease provided an overview of the impact of global warming and air pollution. This second paper provides an overview of the impact of water, soil, noise, and light pollution on the cardiovascular system. This review discusses the biological mechanisms underlying these effects and potential environmental biometrics of exposure. What is clear from both these pollution papers is that significant efforts and redoubled urgency are needed to reduce the sources of pollution in our environment, to incorporate environmental risk factors into medical education, to provide resources for research, and, ultimately, to protect those who are particularly vulnerable and susceptible. [Display omitted] • Increasing evidence suggests that water, soil, noise, and light pollution increase cardiovascular morbidity and mortality. • The mechanisms linking these sources of pollution to cardiovascular disease are diverse, making mitigation efforts challenging, but evolving methods to track these sources of pollution may point to new strategies. • Collectively, these data underscore the urgent need to address pollution in all its forms at a global level. [ABSTRACT FROM AUTHOR]

This paper uses machine learning approaches to predict the association between traffic volume, air pollution, and meteorological conditions. A key focus is on the interaction between these factors. The paper does this using hourly traffic volume, N O x , P M 2.5 , and weather data for Oslo, Norway. I considered a total of six datasets of the 2019 whole-year data to verify the prediction accuracy of the models. I find that the autoregressive integrated moving average model with exogenous input variables, and the autoregressive moving average dynamic linear model outperform the machine learning models in predicting air pollution. At the same time, I also explored the effect of sampling weather subsets on prediction accuracy. Finally, my study makes optimal policy recommendations for reducing air pollution from traffic volume, after considering the interaction and lagged effects of meteorology, time variables, traffic, and air pollution. [ABSTRACT FROM AUTHOR]

Since 2013, Permitted Development Rights (PDR) in England have allowed commercial-to-residential conversions in locations once deemed suitable only for non-residential land-use. This deregulation of planning control has been justified as a way of encouraging more home-building in areas experiencing 'housing crisis', but its overall consequences remain unclear. This paper hence compiles quantitative evidence on a city-wide scale on the price, size, build and location of these conversions in London 2013–2021. It finds that homes produced through this route are generally smaller than the London average and are over-concentrated in neighbourhoods with fewer accessible green spaces and higher-than-average levels of air pollution. Here, larger conversion schemes (of more than 10 units) appear particularly problematic, potentially subjecting residents to forms of 'slow violence' that could have long-term consequences for their physical and mental health. The paper also finds that, on average, PDR conversions are marginally more affordable than other new developments in the capital, but are also more expensive per square metre, suggesting deregulation is allowing developers to 'extract' maximum value from these schemes rather than providing affordable homes per se. The implications of this are discussed in relation to the politics of housing in London and the wider forms of planning deregulation allowing developers to accrue increased profits from housing in an era of intense financialisation. [ABSTRACT FROM AUTHOR]

Due to concerns with air pollution and climate change, governments and transport operators around the world have engaged in transforming their fossil-fueled vehicles into electric vehicles (EVs). It is essential to build a model for the electrifying process to minimize the operation costs. This paper presents a systematic analytical approach for the electrification of a fire ambulance service station. This approach begins with the selection of suitable EVs to replace the current service vehicles. Subsequently, an in-depth analysis is conducted to determine the practical utilization of EVs at the station. The model proposes two charging strategies: immediate charging upon an EVs' return and smart charging. Based on the chosen EVs and charging strategies, a comprehensive assessment of the load profiles for the planned EV charging station is performed. In accordance with the load profiles, a mathematical model to minimize the infrastructure and operation costs of the charging station is proposed. Various pricing schemes are compared to identify the most efficient pricing scheme for the charging station, and economic analyses of the EVs and traditional ambulance vehicles are proposed in this paper. The test results indicate that the progressive pricing scheme is well suited for immediate charging strategies, whereas smart charging should opt for the time-of-use pricing scheme. Selecting the appropriate pricing scheme has the potential to significantly reduce electric energy costs. [ABSTRACT FROM AUTHOR]

Climatic and geographic factors characterizing local conditions, as well as the ecological state of the external environment, have a direct effect on human health, morbidity, and life expectancy. The paper considers the basic concepts of medical ecology, a pathogenetic role of environmental atmospheric pollution in the development of environmentally induced and environmentally dependent diseases associated with exposure to meteorological factors. In addition to the major hygienic risk factors affecting the public health (a level of air, water, and soil pollution), the levels of acoustic and electromagnetic background, the type of human nutrition, architectural and planning conditions of human life are of significant importance. Global climate warming causes numerous environmental changes, primarily an increase in surface air temperature, which in turn entails global changes in weather conditions that lead to various weather anomalies. Human health is directly dependent on environmental conditions and a degree of adaptation to them. The paper highlights the issues of diagnosing environmentally induced diseases and measures to counteract their spread. Based on the system diagnosis using physiological indicators of human health and the integration of the adaptation and nosological approaches, a hardware-software complex has been developed and introduced into medical practice. Corrective technologies of regenerative medicine that are used for ecologically dependent pathologies are described. Their application in the process of the complex sanatorium and resort treatment is most effective. [ABSTRACT FROM AUTHOR]

This study aimed to explore the published research on the relationship between climate change and skin cancer and the implications for prevention, management and further research. Scoping review. This scoping review following JBI methodology reviewed English articles identified in searches of MEDLINE, Embase, CINAHL, Web of Science and Scopus on 14 April 2023. The screening of articles was completed by two independent reviewers. Data were extracted by a single reviewer and checked by another. A causal pathway diagram was iteratively developed throughout the review and was used to categorise the findings. The search identified 1376 papers, of which 45 were included in the final review. Nine papers reported primary research, and 36 papers were reviews, perspectives, commentaries, editorials, or essays. The papers examined climate change influencing behaviours related to ultraviolet exposure (30 papers), ambient temperature (21 papers) and air pollution (five papers) as possible risk factors; occupational, rural, and contextual factors affecting skin cancer (11 papers); and prevention and access to health care in the context of climate change (seven papers). Most papers were published in journals in subject areas other than health. This review identified ultraviolet radiation, occupation, rising temperature, individual behaviour and air pollution as possible influences on skin cancer rates. Furthermore, it highlights the complexity and uncertainties in the relationship between climate change and skin cancer and the need for further research on this relationship, including primary epidemiological research and reviews that follow recognised review guidelines and include assessment of health services and social determinants in the causal pathways of this relationship. [ABSTRACT FROM AUTHOR]

Despite their key role in integrated waste management, direct (incineration) and indirect (gasification/pyrolysis) waste combustion processes are still opposed by some of the general public due to the past emission levels of air pollutants. In fact, although the release of air pollutants (especially dioxin) to the atmosphere from waste combustion processes has gradually decreased over the years, thanks to the introduction of stricter regulations and more advanced removal technologies, there is still an unsolved problem regarding the public acceptance of waste-to-energy facilities. The aim of this paper is to provide an overview of the state-of-the-art air pollution control (APC) technologies used in waste combustion facilities. Air pollution control technologies are designed to reduce or eliminate the emissions of harmful pollutants into the atmosphere. These technologies are important for safeguarding public health, protecting ecosystems, complying with regulations, and promoting a more sustainable and resilient future for both local and global communities. This paper will highlight the complexity behind emission control and the efforts made by this sector over the years. This paper will also propose suggested configurations based on the interactions/complementarity between different APC technologies and recent findings to improve their performance. [ABSTRACT FROM AUTHOR]

This paper investigates the influence of air pollution on irrational behaviors in stock trading through behavioral experiments in laboratory, simulating air pollution by burning straw and mosquito coils. The results of this study show that air pollution significantly improves disposition effect and repurchase effect in an asymmetric way, which are thought as irrational behaviors in stock investments, making subjects prefer selling winning stocks (part of disposition effect) and repurchasing stocks that have fallen in price since the sale (part of repurchase effect). Furthermore, regret, a negative emotion, is the psychological mechanism by which air pollution influences the irrational behaviors. [ABSTRACT FROM AUTHOR]

The concentration prediction of PM2.5 plays a vital role in controlling the air and improving the environment. This paper proposes a prediction model (namely EEMD-ALSTM) based on Ensemble Empirical Mode Decomposition (EEMD), Attention Mechanism and Long Short-Term Memory network (LSTM). Through the combination of decomposition and LSTM, attention mechanism is introduced to realize the prediction of PM2.5 concentration. The advantage of EEMD-ALSTM model is that it decomposes and combines the original data using the method of ensemble empirical mode decomposition, reduces the high nonlinearity of the original data, and Specially reintroduction the attention mechanism, which enhances the extraction and retention of data features by the model. Through experimental comparison, it was found that the EEMD-ALSTM model reduced its MAE and RMSE by about 15% while maintaining the same R2 correlation coefficient, and the stability of the model in the prediction process was also improved significantly. [ABSTRACT FROM AUTHOR]

Non-exhaust road transport emissions in cities contribute to poor air quality and have an impact on human health. This paper presents a new study of particles emitted by tyre wear in real driving conditions and gives their emission factors. The most frequently emitted particles were collected in urban, suburban and road areas. They were identified and analysed physically and chemically. Their level of toxicity is well known. An overall analysis of the measured pollutants was carried out to assess their emission factors in real driving situations. The highest emitting pollutants, considered separately, were found to have high emission factors. The values obtained exceed the Euro standard for vehicles but are below those of vehicles not equipped with particle filters. Significant test analysis confirmed that the inertia of chemical pollutants is homogeneous. Emission factors have also been provided for PM10 and PM2.5. These results should contribute to the emergence of future regulations of non-exhaust emissions and should help to analyse the exposure-impact relationship for particles from tyre wear. [ABSTRACT FROM AUTHOR]

Air pollution data provides important information on air quality, which can be used to assess the impact of atmospheric pollution on human health, the environment, and the economy, as well as to develop corresponding policies and measures to reduce pollutant emissions and improve air quality. In this paper, we propose a novel approach to improve the resolution of meteorological data via masked image modeling (PMIM) to generate high-resolution air pollution data. In order to apply the image masking modeling to process air pollution data, we convert the data format and use radial basis function visualization to generate smooth distribution maps of air pollution data. To generate high-resolution air pollution data, we design several different masking strategies and use the masked image modeling to simulate the reconstruction process from low-resolution grid data to high-resolution grid data, obtaining the reconstructed high-resolution grid images. Finally, we use the mapping relationship between the pixel colors of the reconstructed images and the air pollution data to generate high-resolution air pollution concentration data. In order to verify the effectiveness of the proposed method, we conduct comparative experiments using different masking strategies and test air pollution data of different resolutions. The results show that our method has good applicability and effectiveness in different situations. [ABSTRACT FROM AUTHOR]

• Quantifying the impact of peer pressure on environmental improvements among local governments in China. • Investigating cities ' reaction to declining air quality rankings through regression discontinuity design. • Demonstrating the efficacy of political incentives in combating pollution and the catalytic effect of rankings on policy action. • Assessing the benefits resulting from air pollution reductions using an exposure response function. This paper estimates the impact of peer governmental pressure on air quality using a regression discontinuity design inherent in China's air quality ranking system. Because air quality rankings are important for political evaluations, local officials who witnessed a decline in air quality ranking are motivated to enforce more stringent environmental standards compared to their counterparts with increased rankings. By exploiting this regulatory discontinuity with monthly air quality ranking data from 2012 to 2020 of 169 cities, we find that cities experiencing a lower air quality ranking in the current month compared to the previous month exhibit a subsequent improvement in air quality during the following month, as evidenced by decreased pollutant concentrations. Furthermore, our analysis reveals that in cities where pollutant rankings decline more sharply, officials are less likely to encounter fewer promotion opportunities. Importantly, we observe that the discontinuity in individual pollutant concentrations is not solely influenced by their respective rankings but is also impacted by the overall A ir Q uality I ndex (AQI) ranking. Linking the reduction in air pollutants concentration with the economic benefits, a back-of-the-envelope calculation indicates the decrease in premature deaths due to cardiovascular and respiratory diseases amounts to approximately 200 thousand, resulting in an associated economic saving of more than 600 billion Chinese yuan. [ABSTRACT FROM AUTHOR]

Variable selection involves selecting truly important predictors from p-dimensional multivariate functional predictors in functional predictive models. In this paper, a variable selection method is designed for scalar-on-function predictions entangled with nonlinear joint associations among scalar response and multiple functional predictors. First, a nonparametric functional nonlinear conditional correlation coefficient, namely, the FunNCC coefficient, is proposed to measure complex dependencies, including the nonmonotonic marginal dependence, along with the conditional associations of redundancy, complement, and interaction. Then, a model-free feature ordering and selection method is designed, where the FunNCC is utilized to rank relevance, enabling the selection of a subset of predictors with the strongest joint dependence. Since this method allows for quantitatively evaluating the contributions of predictors in explaining responses, it achieves moderate model interpretability. Finally, extensive simulation studies and two real-data cases involving air pollution regression and hand gesture recognition are conducted to evaluate the finite sample performance of the proposed method, and the results show that the proposed FunNCC and variable selection methods outperform state-of-the-art baselines. [ABSTRACT FROM AUTHOR]

In a global society that is increasingly interrelated and focused on mobility, carbon monoxide emissions derived from internal combustion vehicles remain the most important factor that must be addressed to improve environmental quality. Certainly, air pollution generated by internal combustion engines threatens human health and the well-being of the planet. In this regard, this paper aims to address the urgent need to understand and face the CO emissions produced by internal combustion vehicles; therefore, this work proposes a mathematical model based on Auto-Regressive Exogenous that predicts the CO percentages produced by an internal combustion engine during its start-up. The main goal is to establish a strategy for diagnosing excessive CO emissions caused by changes in the engine temperature. The proposed CO emissions modeling is evaluated under a real dataset obtained from experiments, and the obtained results make the proposed method suitable for being implemented as a novel diagnosis tool in automotive maintenance programs. [ABSTRACT FROM AUTHOR]

To explore the relationship between air pollution and total factor productivity and new pathways, This paper examines the impact of air pollution on total factor productivity of A-share listed companies in Shanghai and Shenzhen between 2015 and 2019. It investigates this relationship by considering two pathways: investor sentiment and government attention. The findings indicate that air pollution suppresses total factor productivity of firms. However, air pollution stimulates investor sentiment, which in turn increases R&D investment and total factor productivity, reducing to some extent the dampening effect of air pollution on total factor productivity. There exists a notable positive correlation between air quality and government attention, acting as a mediating variable. This implies that air pollution has the potential to capture the attention of governmental entities, leading to the implementation of appropriate measures aimed at managing and mitigating the occurrence of air pollution caused by industrial enterprises.And the relevant governments should formulate a series of policies to meet the different needs of different enterprises. These two approaches have varying impacts depending on the type of enterprises, thus governments should develop laws to cater to the various demands of different types of enterprises. [ABSTRACT FROM AUTHOR]

The Multi-Compartment Hg (mercury) Modeling and Analysis Project (MCHgMAP) is an international multi-model research initiative intended to simulate and analyze the geospatial distributions and temporal trends of environmental Hg to inform the effectiveness evaluations of two multilateral environmental agreements (MEAs): the Minamata Convention on Mercury (MC) and Convention on Long-Range Transboundary Air Pollution (LRTAP). This MCHgMAP overview paper presents its science objectives, background and rationale, experimental design (multi-model ensemble (MME) architecture, inputs and evaluation data, simulations and reporting framework), and methodologies for the evaluation and analysis of simulated environmental Hg levels. The primary goals of the project are to facilitate detection and attribution of recent (observed) and future (projected) spatial patterns and temporal trends of global environmental Hg levels, and identification of key knowledge gaps in Hg science and modeling to improve future effectiveness evaluation cycles of the MEAs. The current advances and challenges of Hg models, emission inventories, and observational data are examined, and an optimized multi-model experimental design is introduced for addressing the key policy questions of the MEAs. A common set of emissions, environmental conditions, and observation datasets are proposed (where possible) to enhance the MME comparability. A novel harmonized simulation approach between atmospheric, land, oceanic and multi-media models is developed to account for the short- and long-term changes in secondary Hg exchanges and to achieve mechanistic consistency of Hg levels across environmental matrices. A comprehensive set of model experiments is developed and prioritized to ensure a systematic analysis and participation of a variety of models from the scientific community. [ABSTRACT FROM AUTHOR]

The American Iron and Steel Institute (AISI) gathered data between 1989 and 1997 to build an "objective database" to further understand the occupational exposures generated by the few asbestos-containing materials remaining at various steelmaking companies at this time. This paper analyzed the 520 samples from this campaign which occurred at five different steel manufacturers: Georgetown Steel Company, Inland Steel Company, Ling-Temco-Vought (LTV) Corporation, United States Steel Corporation, and Weirton Steel Corporation. This database is believed to have never previously been systematically organized. Samples were grouped based on sampling times to determine whether they should most appropriately be compared to the OSHA short-term excursion limit (EL) or the 8-hr time-weighted average (TWA) permissible exposure limit (PEL). Sampling times of 30 min or less were considered short-term samples, and samples of 180 min or greater were considered representative workday samples. Samples that did not fit into either category, with sampling times between 31 and 179 min, were considered task samples. Overall, the data indicated that the airborne concentrations were quite low in 1989 and they continued to be low through the study period which ended in 1997. Only seven out of 286 (approximately 2.5%) short-term or representative workday samples were in exceedance of the current OSHA OELs that were implemented in 1994 (short-term samples being compared to the 1 f/cc EL and representative workday samples being compared to the 0.1 f/cc 8-hr TWA PEL). Consistent with prior data, analysis of this dataset supports the view that materials containing asbestos were not used in many applications in the steel industry, and measured airborne concentrations of asbestos were almost always below the occupational exposure limits (OELs) in the post-OSHA era (1972–2000). [ABSTRACT FROM AUTHOR]

Several coal-fired power plants (CFPPs) were built in South Africa, mainly in the central Mpumalanga Province, due to an increase in the demand for Eskom, the national power utility, to keep up with socio-economic growth. The CFPPs, of which 90% are owned by Eskom, generate a significant share of the country’s electricity but contribute to the air pollution experienced in the country. The paper discusses sulphur dioxide (SO2), nitrogen dioxide (NO2) and particulate matter of size less than 10 micrometre (μm) in diameter (PM10), using data from 2014 to 2018. The statistics revealed higher PM10 concentrations during winter than in summer and spring at the Kriel and Komati sites; associated with the higher contribution of domestic burning. The study’s results could influence legislation and policies and help to understand the source of poor ambient air quality by assessing the three pollutants within the area of the selected power plants. [ABSTRACT FROM AUTHOR]

Bioaerosols are a subset of important airborne particulates that present a substantial human health hazard due to their allergenicity and infectivity. Chemical reactions in atmospheric processes can significantly influence the health hazard presented by bioaerosols; however, few studies have summarized such alterations to bioaerosols and the mechanisms involved. In this paper, we systematically review the chemical modifications of bioaerosols and the impact on their health effects, mainly focusing on the exacerbation of allergic diseases such as asthma, rhinitis, and bronchitis. Oxidation, nitration, and oligomerization induced by hydroxyl radicals, ozone, and nitrogen dioxide are the major chemical modifications affecting bioaerosols, all of which can aggravate allergenicity mainly through immunoglobulin E pathways. Such processes can even interact with climate change including the greenhouse effect, suggesting the importance of bioaerosols in the future implementation of carbon neutralization strategies. In summary, the chemical modification of bioaerosols and the subsequent impact on health hazards indicate that the combined management of both chemical and biological components is required to mitigate the health hazards of particulate air pollution. [ABSTRACT FROM AUTHOR]

Governments have implemented many regulations to solve traffic congestion and pollutant emissions. Traffic restriction (TR) policy is the most common and widely used traffic regulation. However, it is difficult to alleviate traffic pollution through TR policies. Only a few studies focus on TR policies in multiple cities. In this paper, we classify traffic restrictions by geographical factors and evaluate the impact of different policies based on PSM-DID first. Then, we classify the mixed restrictions and test their effects on air pollution. The results show that TR policy improves air quality regardless of the season. Moreover, the effect of TR policy is not affected by the superposition of the type of traffic restriction. For cities implementing the same TR policy, geographic factors can lead to different outcomes. This finding provides a reference for governments in reducing air pollution. • Traffic restrictions significantly affected air quality in different regions of China. • Cities implemented traffic restrictions have improved air quality in adjacent cities. • Effect of traffic restrictions does not depend on the number of mixed restrictions. [ABSTRACT FROM AUTHOR]

In this paper, the performance of a unidimensional continuous variable quantum key distribution protocol is analyzed using Gaussian modulated coherent state under fast fading channel. In the fast fading channel, both parties are connected in free space, resulting in a harsh propagation environment and pollution caused by atmospheric turbulence. This pollution makes it difficult for the communicators to determine the channel transmittance, which can only be estimated based on a probability distribution. To address this, only one modulator is used to code, which demonstrates performance equivalent to that of the symmetric modulated Gaussian coherent state protocol. The security of the protocol in the face of collective attacks is analyzed and it is proved that it can accept a certain amount of excessive channel noise while simplifying operations. Simultaneously, the impact of finite‐key length effects on the protocol is analyzed. It is also demonstrated that this protocol can reduce costs within an acceptable range of performance losses, making it more practical. [ABSTRACT FROM AUTHOR]

Recently, interest in wireless sensor networks (WSNs) and applications has grown significantly. The path loss caused by buildings and tall trees is a major obstacle in the distribution of sensor nodes. Because of absorption, dispersion, and attenuation by the structures or trees, a degradation occurs in the reliability of the communication link. In this paper, four path-loss models (PLMs) were developed for a LoRa (long-range) wireless sensor node mounted on the drone as a part of a WSN for monitoring air pollution over a distance of approximately 1,000 m utilising the received signal strength indicator (RSSI). Four novel PLMs were created using MATLAB's curve-fitting tools, employing polynomial (POLY), exponential (EXP), Gaussian, and power equations. To improve the accuracy of these equations, their coefficients were fine-tuned using the particle swarm optimisation (PSO) algorithm, resulting in the development of hybrid algorithms—EXP-PSO, POLY-PSO, Gaussian-PSO, and power-PSO. The results demonstrated that PSO-enhanced PLMs achieved an outstanding correlation coefficient (R2) value of 1, indicating their exceptional ability to represent PLMs accurately in the tested air population environment utilising WSNs. Notably, the POLY-PSO algorithm outperformed other algorithms, achieving the lowest root-mean-square error (RMSE) of 1.945 × 10−4 and sum of squares error (SSE) of 1.853 × 10−26. The PLMs based on hybridisation algorithms outperformed existing PLMs regarding the correlation coefficient. These findings hold significant implications for designing and optimising WSNs in air pollution applications, offering valuable insights into radio path-loss modelling in this area. [ABSTRACT FROM AUTHOR]

Air pollution poses a grave threat to human health and everyday life. Accurate air-quality prediction plays a crucial role in effectively preventing and controlling air pollution. A multi-graph spatial-temporal attention network is proposed to predict the air quality in a given area by analyzing the interconnections between stations and the individual characteristics of each station. Firstly, this paper constructs multi-scale spatial-temporal graphs from spatial and temporal perspectives to effectively capture the interconnections between stations, thereby comprehensively understanding the spatial-temporal relationships among them. Secondly, incorporating the nonlinear temporal correlation of station data, this paper proposes a temporal multi-graph attention-fusion module to integrate information from both neighboring stations and the station itself. Finally, predictions are made using a spatial-temporal graph network. The experiments in this paper were based on air-quality data from Beijing and Tianjin, and the following experimental results were obtained. (1) The proposed model outperforms the baselines significantly in both single-step and multi-step prediction tasks. (2) The ablation study confirms that the graph constructed in this paper contributes to improving the performance of the model. (3) Comparing the performance of attention components, the proposed attention mechanism exhibits better performance than other attention mechanisms. [ABSTRACT FROM AUTHOR]

Air pollution has caused many risks to people, but little research has been done on the effect of residents' air pollution risk perception on their subjective well-being. In order to reveal their relationship, this paper divides air pollution risk perception into four dimensions: risk controllability, risk trust, risk acceptability and risk effect, and explores the effects of different dimensions of air pollution risk perception on subjective well-being based on the structural equation model. The results show that risk controllability, risk trust and risk acceptability can improve well-being, while risk effect can reduce well-being, and air quality satisfaction plays a mediating role. There are also internal influencing mechanisms among different air pollution risk perceptions. Particularly, in areas with poor air quality, risk controllability had a negative association with air quality satisfaction, and higher risk effect corresponded to a higher degree of risk controllability. This paper provides some suggestions for environmental management from the perspective of risk perception. [ABSTRACT FROM AUTHOR]

This paper studies the effect of air pollution on voting outcomes. We use data from 60 federal and state elections in Germany from 2000 to 2018 and exploit plausibly exogenous fluctuations in ambient air pollution within counties across election dates. Higher air pollution on election day shifts votes away from incumbent parties and toward opposition parties. An increase in the concentration of particulate matter (PM10) by 10 g/m3--around two within-county SDs--reduces the vote share of incumbent parties by two percentage points, which is equivalent to 4% of the mean vote share. We generalize these findings by documenting similar effects with data from a weekly opinion poll and a large-scale panel survey. We provide further evidence that poor air quality leads to more negative emotions such as anger, worry, and unhappiness, which, in turn, may reduce the support for the political status quo. Overall, these results suggest that poor air quality affects decision-making in the population at large, including consequential political decisions. [ABSTRACT FROM AUTHOR]

Introduction: Curtis Bay (CB) is an environmental justice (EJ) community in South Baltimore. With a high concentration of industrial polluters and compounding non-chemical stressors, CB has experienced socioeconomic, quality of life, and health burdens for over 100 years. Today, these polluters include the open-air CSX Coal Terminal, waste-to-energy incinerators, and heavy diesel traffic through residential areas. The Community of Curtis Bay Association, Free Your Voice, and South Baltimore Community Land Trust are local organizations enacting a vision for equitable, healthy, and community-led development without industrial encroachment. In response to community-identified EJ concerns and an explosion at the CSX Coal Terminal, CB community groups partnered with academic researchers to develop a community-driven hyperlocal air monitoring and capacity building approach. This paper describes this approach to characterizing hyperlocal air quality in CB, building bridges between community residents and regulatory agencies, and nurturing a cohesive and effective community-academic partnership toward EJ. Methods: Using hyperlocal air monitoring, we are collecting real-time air pollution (particulate matter, black carbon, and ground-level gas species) and meteorological data from 15 low-cost sensors in residential and industrial areas of CB. We also use trail cameras to record activities at the CSX Coal Terminal. We merge air pollution and industrial activity data to evaluate the following: overall air quality in CB, multi-air pollutant profiles of elevated events, spatiotemporal changes in air quality in the community, patterns of industrial activity, and potential correlations between air quality and observed industrial activity. Members of our partnership also lead a high school course educating students about the history and ongoing efforts of the EJ movement in their community. Students in this course learn how to employ qualitative and quantitative data collection and analysis methods to bring scientific support to community EJ concerns. Results and Discussion: Our hyperlocal air monitoring network and community-academic partnership are continuing to evolve and have already demonstrated the ability to respond to community-identified EJ issues with real-time data while developing future EJ leaders. Our reflections can assist other community and academic groups in developing strong and fruitful partnerships to address similar EJ issues. [ABSTRACT FROM AUTHOR]

In recent years, atmospheric composite pollution has become increasingly serious, and accelerating VOC governance has become the focus of and difficulty in atmospheric governance. Volatile organic compounds (VOCs) come from a wide range of sources, and industrial sources are the main source of anthropogenic VOC emissions in China. Chemicals, oil refining, and solvents are three typical VOC-emitting industries, and their control is an important part of accelerating and optimizing VOC governance. It can be controlled by source control, process control, and pipe end treatment; among these, pipe end treatment is divided into recovery technology and destruction technology. In pipeline end treatment, with the key materials of adsorption, absorption, catalytic oxidation, and catalytic combustion, the research and development of adsorbents, absorbents and catalysts has become a hot spot in recent years. This paper summarizes the chemical materials used in typical VOC pipe end-treatment technology (including adsorbents used in the adsorption method, absorbents used in the adsorption method, and catalysts used in the catalytic oxidation and catalytic combustion methods), summarizes the advantages and disadvantages of various materials and their application range, and looks forward to the direction of future developments. [ABSTRACT FROM AUTHOR]

This paper presents the results of a study of ground-level ozone concentration variability in Crimea at the background environmental monitoring station (BEMS) of the Karadag State Nature Reserve for 2012–2021 with a more detailed analysis of the last 6 years from 2016 to 2021. A significantly high level of air pollution by ground-level ozone in the observation area was revealed, despite the absence of significant sources of anthropogenic pollution in the vicinity of the station. The relationship between the ground-level ozone concentration and meteorological parameters has been studied, and characteristic wind directions leading to increased levels of ground-level ozone pollution have been established. Intra-annual variations are analyzed, and factors causing a local summer minimum of ground-level ozone concentration in individual years are identified. Using the NOAA HYSPLIT model and ERA5 reanalysis meteorological fields, a spatial analysis of the atmospheric circulation pattern in the region has been carried out. The recurrence of episodes in which the permissible 8-h average ozone concentration level of 100 μg/m3, as recommended by World Health Organization (WHO), was exceeded has been assessed, and the possible causes of these episodes are identified. Mechanisms of long-range transport and their contribution to the ozone regime in the station area have been established. Annual trends in ground-level ozone concentration between 2012 and 2021 are assessed as statistically insignificant. [ABSTRACT FROM AUTHOR]

Atmospheric air pollution with fine particles PM2.5 and PM10 is one of the main environmental problems of the city. This paper presents the results of the study of quantitative and structural components of solid particles’ emissions in the city of Almaty, released during the operation of cars: by an engine with exhaust gases, by tire abrasion, by braking devices (linings) and road pavement wear. РM solid particles in the amount of 543.2 tons are released annually into the atmospheric air of Almaty by motor transport. The bulk PM10 is released totally from the tires, brakes and road – 65.4 %, from the engine – 34.6 %. On diesel buses and trucks, the bulk РM particles’ emissions are released from the exhaust gas engines. Recommendations are given to reduce PM particles emitted by motor transport. [ABSTRACT FROM AUTHOR]

Disruption of the airway epithelium triggers a defensive immune response that begins with the production and release of alarmin cytokines. These epithelial-derived alarmin cytokines, including thymic stromal lymphopoietin (TSLP), are produced in response to aeroallergens, viruses, and toxic inhalants. An alarmin response disproportionate to the inhaled trigger can exacerbate airway diseases such as asthma. Allergens inhaled into previously sensitized airways are known to drive a T2 inflammatory response through the polarization of T cells by dendritic cells mediated by TSLP. Harmful compounds found within air pollution, microbes, and viruses are also triggers causing airway epithelial cell release of TSLP in asthmatic airways. The release of TSLP leads to the development of inflammation which, when unchecked, can result in asthma exacerbations. Genetic and inheritable factors can contribute to the variable expression of TSLP and the risk and severity of asthma. This paper will review the various triggers and consequences of TSLP release in asthmatic airways. [ABSTRACT FROM AUTHOR]

Parkinson's disease is the world's fastest growing brain disorder, and exposure to environmental toxicants is the principal reason. In this paper, we consider alternative, but unsatisfactory, explanations for its rise, including improved diagnostic skills, aging populations, and genetic causes. We then detail three environmental toxicants that are likely among the main causes of Parkinson's disease— certain pesticides, the solvent trichloroethylene, and air pollution. All three environmental toxicants are ubiquitous, many affect mitochondrial functioning, and all can access humans via various routes, including inhalation and ingestion. We reach the hopeful conclusion that most of Parkinson's disease is thus preventable and that we can help to create a world where Parkinson's disease is increasingly rare. [ABSTRACT FROM AUTHOR]

The external insulation strength of the power equipment sleeve in the substation and converter station will be reduced with the increase in altitude and atmosphere pollution. The weakness of external insulation seriously threatens the safe operation of electrical equipment. At present, there are few studies and conclusions that focus on the flashover characteristics of power equipment bushing in high-altitude polluted areas. Therefore, this paper takes the 220 kV voltage level bushing as the research object and studies the Alternating Current (AC) pollution flashover characteristics and lightning and switching impulse flashover characteristics at low pressure. The insulation configuration at high-altitude polluted areas is analyzed. Finally, the insulation configuration scheme, which is suitable for high-altitude polluted areas, is proposed. The results show that the AC pollution flashover voltages of the bushing decrease with the decrease in the atmospheric pressure and the increase in the salt density as a power exponent function. The impulse flashover voltages also decrease with the decrease in the atmospheric pressure as a power exponent function. The atmospheric pressure/pollution impact characteristic index is related to the type of voltage, the value of the atmospheric pressure, and the value of the salt density. The test bushing does not meet the insulation configuration requirements in some high-altitude polluted areas. Through the analysis and calculation of the test results, the bushing insulation configuration scheme in high-altitude polluted areas is proposed. The research results can provide a reference for the external insulation design of bushings in substations and converter stations in high-altitude polluted areas. [ABSTRACT FROM AUTHOR]

Atmospheric pollution significantly impacts the regional economy and human health, and its prediction has been increasingly emphasized. The performance of traditional prediction methods is limited due to the lack of historical data support in new atmospheric monitoring sites. Therefore, this paper proposes a two-stage attention mechanism model based on transfer learning (TL-AdaBiGRU). First, the first stage of the model utilizes a temporal distribution characterization algorithm to segment the air pollutant sequences into periods. It introduces a temporal attention mechanism to assign self-learning weights to the period segments in order to filter out essential period features. Then, in the second stage of the model, a multi-head external attention mechanism is introduced to mine the network's hidden layer key features. Finally, the adequate knowledge learned by the model at the source domain site is migrated to the new site to improve the prediction capability of the new site. The results show that (1) the model is modeled from the data distribution perspective, and the critical information within the sequence of periodic segments is mined in depth. (2) The model employs a unique two-stage attention mechanism to capture complex nonlinear relationships in air pollutant data. (3) Compared with the existing models, the mean absolute error (MAE), root mean square error (RMSE), and mean absolute percentage error (MAPE) of the model decreased by 14%, 13%, and 4%, respectively, and the prediction accuracy was greatly improved. [ABSTRACT FROM AUTHOR]

The significance of air quality monitoring for analyzing impact on public health is growing worldwide. A crucial part of smart city development includes deployment of suitable air pollution sensors at critical locations. Note that there are various air quality measurement instruments, ranging from expensive reference stations that provide accurate data to low-cost sensors that provide less accurate air quality measurements. In this research, we use a combination of sensors and monitors, which we call hybrid instruments, and focus on optimal placement of such instruments across a region. The objective of the problem is to maximize a satisfaction function that quantifies the weighted closeness of different regions to the places where such hybrid instruments are placed (here weights for different regions are quantified in terms of the relative population density and relative PM 2.5 concentration). Note that there can be several constraints such as those on budget, the minimum number of reference stations to be placed, or the set of important regions where at least one sensor should be placed. We develop two algorithms to solve this problem. The first one is a genetic algorithm that is a metaheuristic and that works on the principles of evolution. The second one is a greedy algorithm that selects the locally best choice in each iteration. We test these algorithms on different regions from India with varying sizes and other characteristics such as population distribution, PM 2.5 emissions, or available budget. The insights obtained from this paper can be used to quantitatively place reference stations and sensors in large cities rather than using ad hoc procedures or rules of thumb. [ABSTRACT FROM AUTHOR]

In the electrostatic precipitation of microparticles, it is common to evaluate the voltages between the corona onset voltage and the breakdown point. However, the charging of particles in the nanoscale size range mainly occurs by diffusion, suggesting the possibility of its occurrence, even at low values. This paper investigates the implications of a wide range of electric fields applied in the collection of nanoparticles (6.15–241.4 nm). Two electrostatic precipitators (P.I. and P.II.), both with plates 10 cm high and 30 cm long, but with different plate spacings (4 and 6.5 cm), were tested using three air velocities (1.9, 2.9, and 3.9 cm/s) and 36 electric fields (0.0 − 5.0 kV/cm). Under the highest electric fields, both precipitators showed efficiencies above 99.9%. Furthermore, nanoparticles were collected before the corona onset voltage, with efficiencies of around 28% (P.I.) and 35% (P.II.). Even though no current was detected by the high voltage source, charges on the particles were measured in electric fields lower than 3.0 kV/cm. The higher the applied voltage, the higher the residual charge of the particles at the precipitator outlet. It is expected that the findings of this work should contribute to the technological development of electrostatic processes for air treatment. [ABSTRACT FROM AUTHOR]

The spatially misaligned data problem occurs when data at different spatial scales need to be combined. Bayesian hierarchical models such as melding and downscaler approaches are suitable to solve this problem but their application is limited when MCMC is used for inference due to the high computational cost. The use of INLA and SPDE represents an alternative to MCMC for Bayesian inference that enables faster inference for latent Gaussian models. In this paper, we describe how INLA and SPDE can be adapted to fit Bayesian melding and downscaler models to combine spatially misaligned data. We assess the performance of the models using simulated and real data in a range of scenarios and sampling strategies and compare the suitability of the models in each situation. We also show how to obtain fine particulate matter (PM 2.5 ) predictions in the UK at a continuous surface and at policy-relevant spatial scales by combining spatially misaligned monitoring station data, satellite-derived indicators, road information, and environmental factors. [ABSTRACT FROM AUTHOR]

Air pollution is an important cause of adverse health effects, even in the Nordic countries, which have relatively good air quality. Modelling-based air quality assessment of the health impacts relies on reliable model estimates of ambient air pollution concentrations, which furthermore rely on good-quality spatially resolved emission data. While quantitative emission estimates are the cornerstone of good emission data, description of the spatial distribution of the emissions is especially important for local air quality modelling at high resolution. In this paper we present a new air pollution emission inventory for the Nordic countries with high-resolution spatial allocation (1 km × 1 km) covering the years 1990, 1995, 2000, 2005, 2010, 2012, and 2014. The inventory is available at 10.5281/zenodo.10571094 (Paunu et al., 2023). To study the impact of applying national data and methods to the spatial distribution of the emissions, we compared road transport and machinery and off-road sectors to CAMS-REGv4.2, which used a consistent spatial distribution method throughout Europe for each sector. Road transport is a sector with well-established proxies for spatial distribution, while for the machinery and off-road sector, the choice of proxies is not as straightforward as it includes a variety of different type of vehicles and machines operating in various environments. We found that CAMS-REGv4.2 was able to produce similar spatial patterns to our Nordic inventory for the selected sectors. However, the resolution of our Nordic inventory allows for more detailed impact assessment than CAMS-REGv4.2, which had a resolution of 0.1° × 0.05° (longitude–latitude, roughly 5.5 km × 3.5–6.5 km in the Nordic countries). The EMEP/EEA Guidebook chapter on spatial mapping of emissions has recommendations for the sectoral proxies. Based on our analysis we argue that the guidebook should have separate recommendations for proxies for several sub-categories of the machinery and off-road sectors, instead of including them within broader sectors. We suggest that land use data are the best starting point for proxies for many of the subsectors, and they can be combined with other suitable data to enhance the spatial distribution. For road transport, measured traffic flow data should be utilized where possible, to support modelled data in the proxies. [ABSTRACT FROM AUTHOR]

Micro- and nanoplastics (MNPs) are increasingly found in all realms of the world, including water and soil. Now, there is growing concern over this type of pollution in the air. Many well-established techniques exist for detecting particulate matter (PM) in the air. They are low-cost and handheld, and some of them even allow direct detection from the air. While various MNP detection methods have been demonstrated, airborne MNP detection methods depend on expensive laboratory instruments. This review paper examines the current analytic methods used to identify PM and MNP and discusses their similarities and differences. PM can be detected directly from the air primarily via light scattering, while direct air detection of MNP has not been demonstrated. Sampling steps allow both PM and MNP to be detected from the air. Since PM detection does not require the type identification of materials, mass detection has been utilized, including gravimetric and microbalance methods. Simple optical detection based on absorbance or reflectance and electrical current measurements have also been used for PM detection. However, MNP detection does require type identification, including plastic vs non-plastic or the type of plastic, requiring more sophisticated methods, including spectroscopic and thermal analyses. Microscopic identification has been utilized for both PM and MNP detection since it can identify the size, morphology, autofluorescence, and spectroscopic properties. Machine learning algorithms can also analyze the microscopic images and spectra to identify the type of PM and MNP. While microscopic identification previously required a bulky benchtop microscope, a portable or even handheld microscope has become available, allowing it to detect MNPs in a portable, low-cost manner. [ABSTRACT FROM AUTHOR]

In order to survive, mankind needs air. The quality of life depends on the purity of the air we breathe in. Hazardous pollutants are stirred up in our environment by various activities everyday. In a developing country like India, which has huge population, protecting public health is important. In general, air quality is measured using the air quality index, which records the pollutants level in the air. These recordings are to be mentioned across various places, to know about the air quality level. The advancement of the recent artificial intelligence can be replaced by the human efforts and to automate the system in flowless manner. Hence, an effort has been taken in this paper, by proposing a framework of predicting the air quality seasonally using regularized combinational LSTM (REG-CLSTM). For an efficient air quality level prediction with an improved error rate, time, and accuracy, the study has implemented Reg-CLSTM with a large amount of real-time dataset. To improvise the comprehensiveness and the potentiality of the proposed model, the significant feature is extracted by the rough set-wrapper method. The significant challenge is to provide a seasonal limit range for each considered pollutant in place of a generalized range partition. Aiming at this problem, the proposed model is able to identify the highest concerning pollutants occurrence in individual seasons. Through this study, pyramid learning-based hybridized deep learning framework is developed which can play a crucial role in warning the policy maker to reduce the activities that instigate air pollution. [ABSTRACT FROM AUTHOR]