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In recent years, several studies have reported the recycling of by-products generated by the paper industry and their application to the construction industry. A majority of the existing studies used waste paper sludge ash, and considerable energy is consumed in such incineration processes. This may further contribute to air pollution. In this study, we used waste newspaper (WNP), which underwent a simple crushing process without a separate high-temperature treatment process, and we integrated it in cement mortar. We prepared mortars containing 0%, 0.2%, 0.4%, 0.6%, 0.8%, and 1.0% ground WNP as a cement substitute. Subsequently, the fluidity, compressive strength, tensile strength, carbonation depth, drying shrinkage, and microstructure of the mortars were compared and analyzed. The 28-day compressive strength of the mortar samples with WNP was approximately 3.2–16.1% higher than that of the control sample. The 28-day accelerated carbonation depth of the samples with WNP was approximately 1.03–1.61 mm. Furthermore, their carbonation resistance was approximately 5.2–39.4% higher than that of the control sample. Compressive strength, tensile strength, and carbonation resistance were improved by appropriately using ground WNP as a cement substitute in cement mortar. In this study, the appropriate amount of WNP according to the mechanical properties of cement mortar was found to be 0.4–0.8%, and considering the durability characteristics, the value 0.6 was the most ideal. [ABSTRACT FROM AUTHOR]

Greenhouse gases from industrial activities have become a global problem. Emissions management is being developed to raise awareness of the importance of controlling pollution in general and atmospheric emissions in particular. In 2017, the deficit of the rights of issuance in the industrial sectors increased up to 8.3% (verified emissions in 2017 versus allocation in 2017). This trend will increase more at the end of Phase III due to a progressive reduction in allocation. Phase IV will be much more restrictive in allocating emission rights than Phase III. The extra cost of this deficit reinforces the need for industry in general to reduce CO2 and for the paper industry to reduce GHG emissions and generate credits. Old factories are typically identified as sources of pollution in addition to being inefficient compared to new factories. This article discusses the possibilities offered by maintenance actions, whose integration into a process can successfully reduce the environmental impact of industrial plants, particularly by reducing the CO2 equivalent emissions (CO2-eq units henceforth CO2) they produce. This case study analyzes the integration of maintenance rules that enable significant thermal energy savings and consequently CO2 emissions reduction associated with papermaking. Managing Key Performance Indicators (KPIs), such as the amount of cold water added to the boiler circuit and the conditions of the air blown into the dryer section hood, can be used as indicators of CO2 emissions generated. The control of the water and temperature reduces these emissions. A defined measure—in this case, t CO2/t Paper—indicates an achievement of a 21% reduction in emissions over the past 8 years. [ABSTRACT FROM AUTHOR]

As a key project of China's western development, the West-East Natural Gas Transmission Project (WENGT) has effectively alleviated China's uneven energy distribution and supply-demand issues. It has promoted the optimization of China's energy structure and sustainable development in the new era, helping to achieve the goal of "double carbon". Based on pollution data from Chinese industrial enterprises from 2000 to 2012, this paper analyzes the impact of the West-East Natural Gas Pipeline Project on the air pollution emissions of industrial enterprises using a difference-in-difference model and explores the moderating role of local government environmental regulations. The findings indicate that the project significantly reduced air pollutant emissions from industrial enterprises in cities along the route, with a more pronounced reduction effect in areas with higher environmental regulation intensity. Additionally, the emission reduction effect varied among different types of enterprises and regions, with private enterprises showing the smallest reduction impact and the eastern coastal region exhibiting the most significant effect. This paper delves into the relationship between sustainable cross-regional energy development and air pollution emissions from industrial enterprises, providing a theoretical basis for energy conservation and emission reduction policies in China. [ABSTRACT FROM AUTHOR]

Air pollution is a critical problem in densely populated urban areas, with traffic significantly contributing. To mitigate the adverse effects of air pollution on public health and the environment, there is a growing need for the real-time monitoring and detection of pollution spikes in transportation. This paper presents a novel approach to using Internet of Things (IoT) edge networks for the real-time detection of air pollution peaks in transportation, specifically designed for innovative city applications. The proposed system uses IoT sensors in buses, cabs, and private cars. These sensors are equipped with air quality monitoring capabilities, including the measurement of pollutants such as particulate matter (PM2.5 and PM10), nitrogen dioxide (NO2), ozone (O3), sulfur dioxide (SO2), and carbon dioxide (CO2). The sensors continuously collect air quality data and transmit them to edge devices within the transportation infrastructure. The data collected by these sensors are analyzed, and alerts are generated when pollution levels exceed predefined thresholds. By deploying this system within IoT edge networks, transportation authorities can promptly respond to pollution spikes, improving air quality, public health, and environmental sustainability. This paper details the sensor technology, data analysis methods, and the practical implementation of this innovative system, shedding light on its potential for addressing the pressing issue of transportation-related pollution. The proposed IoT edge network for real-time air pollution spike detection in transportation offers significant advantages, including low-latency data processing, scalability, and cost-effectiveness. By leveraging the power of edge computing and IoT technologies, smart cities can proactively monitor and manage air pollution, leading to healthier and more sustainable urban environments. [ABSTRACT FROM AUTHOR]

Imported air pollution has a significant impact on urban air quality. Relevant studies have shown that many urban air pollution events are not resourced by local emissions but are imported by air pollution from surrounding areas transported across regions. The prevention and control of air pollution is very necessary. However, the existing supervision of urban air quality mostly relies on ground monitoring stations, which are extremely limited in time and space, and cannot satisfy continuous time-space air pollution research. Therefore, aiming at the problem of urban air pollution control, this paper used MERRA-2 reanalysis data and ground monitoring data to establish a "Time-Longitude-Latitude" three-dimensional pollution curve, and then a genetic algorithm was used to optimize its fitting. This study finally reconstructed the imported air pollution transmission route. This paper takes an air pollution event that occurred in Xuzhou City, China, on 12 January 2020, as an example. Through the analysis of aerosol optical depth (AOD), particulate matter (PM), wind speed, and other factors, we found the source, transmission route, and impact time of this pollution. We have verified the correctness and accuracy of the reconstructed contamination transport paths. It is proved that the method is universal and it can quickly and accurately restore the air pollution transmission route and identify the urban imported air pollution transmission entrance. This method will also provide strong data support for the division of responsibilities of environmental protection departments in various regions for severe air pollution transmission events and provide effective governance ideas for the prevention and control of imported air pollution in recipient cities. [ABSTRACT FROM AUTHOR]

This systematic review assessed to evaluate the potential correlation between oral health and air pollution. To the best of the authors' knowledge, this is the first systematic review endeavoring to compare air pollution and oral health. A systematic search was performed according to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-analyses) statement and employed the PICO(S) approach (Patient or Population, Intervention, Control or Comparison, Outcome, and Study types). The search was limited to English-language articles, and publications within a 15-year timeframe were included in the electronic search. A comprehensive search was conducted across PubMed, Scopus, Embase, and Web of Science databases, spanning the years 2008 to 2023, resulting in a total of 4983 scientific articles. A final selection of 11 scientific papers was made based on their study type and the specific air pollutants examined. The selected papers analyzed various air pollutants associated with health-related diseases, including Ozone, Nitrogen Dioxide, Nitrogen Monoxide, Carbon Monoxide, sulfur dioxide, and particulate matter. Three out of eleven of the reviewed studies assert a strong correlation between air pollutants and oral diseases, specifically periodontitis. However, the exact biological mechanisms underlying this correlation do not seem to be fully understood, indicating the need for further comprehensive investigation in this regard. Dentists can contribute to the collective effort by educating their patients about the oral health implications of air pollution, thereby supporting initiatives aimed at promoting environmental and health sustainability. [ABSTRACT FROM AUTHOR]

Air pollution is a growing concern due to severe threats to public health and the environment. The need for reliable air quality monitoring solutions has never been more critical. This research paper introduces an innovative approach to addressing this challenge by deploying a low-cost Internet of Things (IoT) air monitoring station and providing a blockchain technology solution to enhance environmental data transparency, reliability, and accessibility. Our paper adopts a concept of merging IoT and blockchain technologies and collecting some parameters that help to assess air quality by using three sensors, DHT11, MQ7, and MQ135, to collect temperature, humidity, carbon monoxide, and carbon dioxide parameters, respectively, to measure the gases and thus indicate the air quality within the surrounding area. Collecting and sharing these types of valuable data will be very important for various stakeholders, such as governmental bodies, researchers, and the public. This approach is consistent with the principles of sustainable development, facilitating informed decision-making and promoting eco-friendly policies. This research explores the technical architecture of the IoT air monitoring stations, offering a promising solution for addressing air pollution concerns while promoting sustainable development goals. The proposed system is a model for leveraging emerging technologies to advance environmental monitoring and create smarter, livable cities. This approach aligns with the principles of sustainable development and eco-friendly initiatives. This research offers a promising model for enhancing environmental monitoring efforts and advancing the creation of smarter, more sustainable urban environments. The proposed IoT, cloud platform and blockchain-based system not only addresses pressing air pollution challenges but also sets a benchmark for leveraging emerging technologies in environmental science. [ABSTRACT FROM AUTHOR]

Research on the quality of the air in rural areas is essential for determining base emissions of air pollutants, evaluating the effects of dust pollutants particular to rural areas, modeling the dispersion of pollutants, and developing appropriate pollution mitigation systems. The absence of a systematic review based on the assessment of air quality levels in agricultural settings based on integrated weather variables and air pollutants in the literature draws attention to the deficiencies and the necessity of further research in this area. Hence, our study aimed to develop an Arduino monitoring system with related sensors to acquire some air pollutants and weather parameters. Additionally, we proposed an innovative solution to compare air quality levels by suggesting a new criterion called an integrated indicator for air quality assessment (IAQA). It was created based on the weighted average method to combine the investigated air pollutants and weather parameters. This criterion was evaluated while conducting field measurements in 29 environmentally different agricultural regions located within the Kingdom of Saudi Arabia. To determine the integrated indicator, all the values of the variables were normalized between 0 and 1. The agricultural setting with the lowest integrated indicator was the best environmentally. The lowest and highest values of the integrated indicator ranged from 37.03% and 66.32%, respectively, with an arithmetic average of 48.24%. The developed criterion can change its value depending on the change in the weight value of the variables involved, and it is suitable for application to any other agricultural or non-agricultural setting to evaluate the pollution level in the air. Although similar research has been published, this paper presents novelty findings based on integrated values of air pollutants and weather variables for defining a new criterion called IAQA. Additionally, this paper presents original results for air pollutants and weather aspects in different agricultural settings. [ABSTRACT FROM AUTHOR]

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]

The Upper Hunter Valley is a major coal mining area in New South Wales (NSW), Australia. Due to the ongoing increase in mining activities, PM10 (air-borne particles with an aerodynamic diameter less than 10 micrometres) pollution has become a major air quality concern in local communities. The present study was initiated to quantitatively examine the spatial and temporal variability of PM10 pollution in the region. An earlier paper of this study identified two air quality subregions in the valley. This paper aims to provide a holistic summarisation of the relationships between elevated PM10 pollution in two subregions and the local- and synoptic-scale meteorological conditions for spring and summer, when PM10 pollution is relatively high. A catalogue of twelve synoptic types and a set of six local meteorological patterns were quantitatively derived and linked to each other using the self-organising map (SOM) technique. The complex meteorology–air pollution relationships were visualised and interpreted on the SOM planes for two representative locations. It was found that the influence of local meteorological patterns differed significantly for mean PM10 levels vs. the occurrence of elevated pollution events and between air quality subregions. In contrast, synoptic types showed generally similar relationships with mean vs. elevated PM10 pollution in the valley. Two local meteorological patterns, the hot–dry–northwesterly wind conditions and the hot–dry–calm conditions, were found to be the most PM10 pollution conducive in the valley when combined with a set of synoptic counterparts. These synoptic types are featured with the influence of an eastward migrating continental high-pressure system and westerly troughs, or a ridge extending northwest towards coastal northern NSW or southern Queensland from the Tasman Sea. The method and results can be used in air quality research for other locations of NSW, or similar regions elsewhere. [ABSTRACT FROM AUTHOR]

This research is an attempt to examine the recent status and development of scientific studies on the use of machine learning algorithms to model air pollution challenges. This study uses the Web of Science database as a primary search engine and covers over 900 highly peer-reviewed articles in the period 1990–2022. Papers published on these topics were evaluated using the VOSViewer and biblioshiny software to identify and visualize significant authors, key trends, nations, research publications, and journals working on these issues. The findings show that research grew exponentially after 2012. Based on the survey, "particulate matter" is the highly occurring keyword, followed by "prediction". Papers published by Chinese researchers have garnered the most citations (2421), followed by papers published in the United States of America (2256), and England (722). This study assists scholars, professionals, and global policymakers in understanding the current status of the research contribution on "air pollution and machine learning" as well as identifying the relevant areas for future research. [ABSTRACT FROM AUTHOR]

Syndemics, the adverse interaction of two or more coterminous diseases or other negative health conditions, have probably existed since human settlement, plant and animal domestication, urbanization, and the growth of social inequality beginning about 10–12,000 years ago. These dramatic changes in human social evolution significantly increased opportunities for the spread of zoonotic infectious diseases in denser human communities with increased sanitation challenges. In light of a growing body of research that indicates that anthropogenic air pollution causes numerous threats to health and is taking a far greater toll on human life and wellbeing than had been reported, this paper proposes the possibility that air pollution is now the primary driver of infectious disease syndemics. In support of this assertion, this paper reviews the growth and health impacts of air pollution, the relationship of air pollution to the development and spread of infectious diseases, and reported cases of air pollution-driven infectious disease syndemics, and presents public health recommendations for leveraging the biosocial insight of syndemic theory in responding to infectious disease. [ABSTRACT FROM AUTHOR]

New digital interventions have shown potential in managing asthma and improving patients' quality of life compared with conventional interventions. Our objective was to conduct an exhaustive survey of the application of digital health technologies in evaluating, treating, and self-managing psychological and psychopathological elements linked to asthma. We analyzed a compendium of research papers pertaining to asthma, encompassing themes such as outdoor air pollution, early life wheezing disorders, atopic dermatitis, digital strategies for asthma self-management, psychiatric conditions and asthma, familial impacts on pediatric asthma, and the utilization of mobile health apps for managing asthma. We scrutinized six chosen studies to evaluate the capacity of digital health technologies to enhance the management and treatment outcomes of psychological factors related to asthma. The studies under review indicate that eHealth interventions, mixed reality instruments, mHealth technology-augmented nurse-led interventions, and smartphone apps incorporating Bluetooth-enabled sensors for asthma inhalers can markedly enhance self-management of symptoms, quality of life, and mental health outcomes, particularly in children and adolescents with asthma. Nonetheless, additional research is required to ascertain their efficacy and practicability across diverse populations and environments. Customized interventions that cater to the unique needs and preferences of asthma patients and associated psychological elements are vital to ensure the persistent and equitable adoption of these technologies. We underscore the significance of addressing psychological elements in the management and treatment of asthma and advocate for ongoing research and development in this domain. [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]

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]

Predicting particulate matter with a diameter of 10 μm (PM10) is crucial due to its impact on human health and the environment. Today, aerosol optical depth (AOD) offers high resolution and wide coverage, making it a viable way to estimate PM concentrations. Recent years have also witnessed in-creasing promise in refining air quality predictions via deep neural network (DNN) models, out-performing other techniques. However, learning the weights and biases of the DNN is a task classified as an NP-hard problem. Current approaches such as gradient-based methods exhibit significant limitations, such as the risk of becoming ensnared in local minimal within multi-objective loss functions, substantial computational requirements, and the requirement for continuous objective functions. To tackle these challenges, this paper introduces a novel approach that combines the binary gray wolf optimizer (BGWO) with DNN to improve the optimization of models for air pollution prediction. The BGWO algorithm, inspired by the behavior of gray wolves, is used to optimize both the weight and bias of the DNN. In the proposed BGWO, a novel sigmoid function is proposed as a transfer function to adjust the position of the wolves. This study gathers meteorological data, topographic information, PM10 pollution data, and satellite images. Data preparation includes tasks such as noise removal and handling missing data. The proposed approach is evaluated through cross-validation using metrics such as correlation rate, R square, root-mean-square error (RMSE), and accuracy. The effectiveness of the BGWO-DNN framework is compared to seven other machine learning (ML) models. The experimental evaluation of the BGWO-DNN method using air pollution data shows its superior performance compared with traditional ML techniques. The BGWO-DNN, CapSA-DNN, and BBO-DNN models achieved the lowest RMSE values of 16.28, 19.26, and 20.74, respectively. Conversely, the SVM-Linear and GBM algorithms displayed the highest levels of error, yielding RMSE values of 36.82 and 32.50, respectively. The BGWO-DNN algorithm secured the highest R2 (88.21%) and accuracy (93.17%) values, signifying its superior performance compared with other models. Additionally, the correlation between predicted and actual values shows that the proposed model surpasses the performance of other ML techniques. This paper also observes relatively stable pollution levels during spring and summer, contrasting with significant fluctuations during autumn and winter. [ABSTRACT FROM AUTHOR]

In China, Hebei Province and Guangdong Province have many pollution-intensive industries; yet Hebei suffers more serious atmospheric or environmental pollution than Guangdong. To explain the phenomenon, this paper chooses the statistical data of 286 prefecture-level cities in the Chinese mainland from 2005 to 2018 and empirically tests the spatial effect and threshold characteristics of atmospheric self-purification capacity on environmental pollution by using the spatial autoregression model (SAR), spatial Durbin model (SDM), and panel threshold model. As evinced, the local atmospheric self-purification capacity has a significant inhibitory effect on environmental pollution, and the absolute value of the direct effect coefficient reaches 1.337. Simultaneously, atmospheric self-purification capacity has a threshold effect on environmental pollution. The threshold value of environmental regulation as a threshold variable is 11.6349. This means that when the level of environmental regulation proves higher than 11.6349, it attenuates the inhibitory effect of the atmospheric self-purification capacity on environmental pollution. As the heterogeneity research unveils, atmospheric self-purification capacities in various regions form a significant correlation with environmental pollution. This paper suggests that local governments should strengthen environmental regulation and construct an inter-regional joint prevention and control system for atmospheric-pollution governance to enhance the atmospheric self-purification capacity. [ABSTRACT FROM AUTHOR]

Owing to a long-term, extensive development model and inadequate industrial development planning, cases of atmospheric environment pollution frequently occur in Hebei province. By using such approaches as the Spatial Gini Coefficient, the Herfindahl–Hirschman Index and location entropy, this paper analyzes the spatial-distribution characteristics of atmospheric pollution-intensive industries such as the thermal power industry in Hebei province. As shown, atmospheric pollution-intensive industries, such as the thermal power industry in Hebei province, excessively cluster. As industrial agglomeration continuously intensifies, the spatial imbalance becomes increasingly prominent. Taking the number of days with excellent air quality as a benchmark, this paper divides prefecture-level cities of Hebei province into four types of industrial management and designs targeted strategies for the optimization of atmospheric pollution-intensive industries, such as the thermal power industry. In terms of policies, Type I and Type II cities are advised to strengthen the transfer of atmospheric pollution-intensive industries such as the thermal power industry, and Type III and Type IV cities are advised to improve capacities in atmospheric self-purification and green-technology innovation in a bid to help government departments to scientifically manage atmospheric pollution-intensive industries such as the thermal power industry. [ABSTRACT FROM AUTHOR]

As the world grapples with the pressing challenge of environmental sustainability, the need for innovative solutions to combat air pollution has become paramount. Air pollution is a complex issue that necessitates real-time monitoring of pollution sources for effective mitigation. This paper explores the potential of swarm algorithms applied as a novel and efficient approach to address this critical environmental concern. Swarm algorithms offer a promising framework for coordinating fleets of drones to collaboratively monitor and analyze air pollution sources. The unique capabilities of drones, including their agility, accessibility, and versatility, make them ideal candidates for aerial data collection. When harnessed in a swarm, these drones can create a dynamic and adaptable network that provides a more comprehensive and fine-grained understanding of air pollution dynamics. This paper delves into the conceptual foundations of using swarm algorithms in drone-based air pollution monitoring. [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]

A growing body of literature has attempted to characterize how traffic-related air pollution (TRAP) affects molecular and subclinical biological processes in ways that could lead to cardiorespiratory disease. To provide a streamlined synthesis of what is known about the multiple mechanisms through which TRAP could lead to cardiorespiratory pathology, we conducted a systematic review of the epidemiological literature relating TRAP exposure to methylomic, proteomic, and metabolomic biomarkers in adult populations. Using the 139 papers that met our inclusion criteria, we identified the omic biomarkers significantly associated with short- or long-term TRAP and used these biomarkers to conduct pathway and network analyses. We considered the evidence for TRAP-related associations with biological pathways involving lipid metabolism, cellular energy production, amino acid metabolism, inflammation and immunity, coagulation, endothelial function, and oxidative stress. Our analysis suggests that an integrated multi-omics approach may provide critical new insights into the ways TRAP could lead to adverse clinical outcomes. We advocate for efforts to build a more unified approach for characterizing the dynamic and complex biological processes linking TRAP exposure and subclinical and clinical disease and highlight contemporary challenges and opportunities associated with such efforts. [ABSTRACT FROM AUTHOR]

Studies on air quality in rural environments are fundamental to obtain first-hand data for the determination of base emissions of air pollutants, to assess the impact of rural-specific airborne pollutants, to model pollutant dispersion, and to develop proper pollution mitigation technologies. The literature lacks a systematic review based on the evaluation of the techniques and methods used for the sampling/monitoring (S/M) of atmospheric pollutants in rural and agricultural settings, which highlights the shortcomings in this field and the need for future studies. This work aims to review the study design applied for on-field monitoring campaigns of airborne pollutants in rural environments and discuss the possible needs and future developments in this field. The results of this literature review, based on the revision of 23 scientific papers, allowed us to determine (i) the basic characteristics related to the study design that should always be reported; (ii) the main techniques and analyses used in exposure assessment studies conducted in this type of setting; and (iii) contextual parameters and descriptors of the S/M site that should be considered to best support the results obtained from the different studies. Future studies carried out to monitor the airborne pollution in rural/agriculture areas should (i) include the use of multiparametric monitors for the contextual measurement of different atmospheric pollutants (as well as meteorological parameters) and (ii) consider the most important boundary information, to better characterize the S/M site. [ABSTRACT FROM AUTHOR]

Air pollution is recognized as a critical global health risk, yet there has been no comprehensive assessment of its impact on public health in Libya until now. This study evaluates the burden of disease associated with ambient particulate matter (PM 2.5 ) in Libya, drawing on data from the Global Burden of Disease Study 2019. By integrating satellite-based estimates, chemical transport models, and ground-level measurements, PM 2.5 exposure and its effects on mortality and disability-adjusted life years (DALYs) across the different sexes and all age groups from 1990 to 2019 are estimated. Our findings reveal that the annual population-weighted mean PM 2.5 concentration in Libya was 38.6 μg/m 3 in 2019, marking a 3% increase since 1990. In the same year, PM 2.5 was responsible for approximately 3368 deaths, accounting for 11% of all annual deaths in the country. Moreover, a total of 107,207 DALYs were attributable to PM 2.5 , with ischemic heart disease being the leading cause, representing 46% of these DALYs. The analysis also highlights a significant burden of years of life lost (YLLs) at 89,113 and years lived with disability (YLDs) at 18,094, due to PM 2.5 . Given the substantial health risks associated with air pollution, particularly from ambient particulate matter, Libyan authorities must implement effective policies aimed at reducing air pollution to enhance healthcare outcomes and preventive services., Competing Interests: Author Mohini Kak was employed by the World Bank. Author Sameh El Saharty was employed by the World Bank. Author Christopher H. Herbst was employed by the World Bank. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Air pollution poses a significant health hazard in urban areas across the globe, with India being one of the most affected countries. This paper presents environmental monitoring study conducted in Jodhpur, Rajasthan, India, to assess air quality in diverse urban environments. The study involved continuous indoor and outdoor air quality monitoring, focusing on particulate matter (PM 2.5 ) levels, bioaerosols, and associated meteorological parameters. Laser sensor-based low-cost air quality monitors were utilized to monitor air quality and Anderson 6-stage Cascade Impactor & Petri Dish methods for bioaerosol monitoring. The study revealed that PM 2.5 levels were consistently high throughout the year, highlighting the severity of air pollution in the region. Notably, indoor PM 2.5 levels were often higher than outdoor levels, challenging the common notion of staying indoors during peak pollution. The study explored the spatial and temporal diversity of air pollution across various land-use patterns within the city, emphasizing the need for tailored interventions in different urban areas. Additionally, bioaerosol assessments unveiled the presence of pathogenic organisms in indoor and outdoor environments, posing health risks to residents. These findings underscore the importance of addressing particulate matter and bioaerosols in air quality management strategies. Despite the study's valuable insights, limitations, such as using low-cost air quality sensors and the need for long-term data collection, are acknowledged. Nevertheless, this research contributes to a better understanding of urban air quality dynamics and the importance of public awareness in mitigating the adverse effects of air pollution. In conclusion, this study underscores the urgent need for effective air quality management strategies in urban areas. The findings provide valuable insights for policymakers and researchers striving to address air pollution in rapidly urbanizing regions.

Air pollution has been associated with several health problems. Detecting and measuring the concentration of harmful pollutants present in complex air mixtures has been a long-standing challenge, due to the intrinsic difficulty of distinguishing among these substances from interferent species and environmental conditions, both indoor and outdoor. Despite all efforts devoted by the scientific and industrial communities to tackling this challenge, the availability of suitable device technologies able to selectively discriminate these pollutants present in the air at minute, yet dangerous, concentrations and provide a quantitative measure of their concentrations is still an unmet need. Thermal conductivity detectors (TCDs) show promising characteristics that make them ideal gas sensing tools capable of recognising different gas analytes based on their physical fingerprint characteristics at the molecular level, such as their density, thermal conductivity, dynamic viscosity, and others. In this paper, the operation of TCD gas sensors is presented and explored using a finite element simulation of Joule heating in a sensing electrode placed in a gas volume. The results obtained show that the temperature, and hence, the resistance of the individual suspended microbridge sensor device, depends on the surrounding gas and its thermal conductivity, while the sensitivity and power consumption depend on the properties of the constitutive metal. Moreover, the electrode resistance is proven to be linearly dependent on the applied voltage. [ABSTRACT FROM AUTHOR]

Ozone at ground level (O3) is an air pollutant that is formed from primary precursor gases like nitrogen oxides (NOx) and volatile organic compounds (VOCs). It plays a significant role as a precursor to highly reactive hydroxyl (OH) radicals, which ultimately influence the lifespan of various gases in the atmosphere. The elevated surface O3 levels resulting from anthropogenic activities have detrimental effects on both human health and agricultural productivity. This paper provides a comprehensive analysis of the variations in surface O3 levels across various regions in the Indian subcontinent, focusing on both spatial and temporal changes. The study is based on an in-depth review of literature spanning the last thirty years in India. Based on the findings of the latest study, the spatial distribution of surface O3 indicates a rise of approximately 50–70 ppbv during the summer and pre-monsoon periods in the northern region and Indo-Gangetic Plain. Moreover, elevated levels of surface O3 (40–70 ppbv) are observed during the pre-monsoon/summer season in the western, southern, and peninsular Indian regions. The investigation also underscores the ground-based observations of diurnal and seasonal alterations in surface O3 levels at two separate sites (rural and urban) in Kannur district, located in southern India, over a duration of nine years starting from January 2016. The O3 concentration exhibits an increasing trend of 7.91% (rural site) and 5.41% (urban site), ascribed to the rise in vehicular and industrial operations. This review also presents a succinct summary of O3 fluctuations during solar eclipses and nocturnal firework displays in the subcontinent. [ABSTRACT FROM AUTHOR]

Fine particles (PM2.5) have generally been reported as the major contributor to the adverse health effects of air pollution. Lebanon is characterized by a high density of transport, the production of electricity by generators, and a problem of uncontrolled incineration of household waste. For the purpose of this paper, the physico-chemical properties of fine (PM2.5-0.3) and quasi-ultrafine (PM0.3) particulate matter sampled in Southern Lebanon, were studied. Then, an evaluation and comparison of the toxicity of the different extracted fractions from PM (i.e., native PM2.5-0.3 vs. organic extractable matter fraction (OEM2.5-0.3), and non-extractable matter fraction (NEM2.5-0.3)) was performed. Also, an examination of the toxicity of PM0.3 was conducted indirectly through the evaluation of the OEM0.3 harmfulness. The physico-chemical analysis showed that PM0.3 was much more concentrated than PM2.5-0.3 in organic compounds such as polycyclic aromatic hydrocarbons (PAHs) (28-fold) and their nitrated (N-PAHs, 14-fold) and oxygenated (O-PAHs, 10-fold) derivatives. Normal human bronchial epithelial cells (BEAS-2B) were exposed to PM2.5-0.3, its derived fractions (i.e., OEM2.5-0.3 and NEM2.5-0.3), and OEM0.3 before evaluating the global cytotoxicity, metabolic activation of organic compounds, genotoxicity, and inflammatory response. Different responses were observed depending on the considered fraction of particles. The global cytotoxicity showed a pronounced response related to ATP and LDH activities after exposure to the quasi-ultrafine organic extractable matter fraction (OEM0.3). There was no significant induction of the AhR cell-signaling pathway by NEM2.5-0.3. Despite the apparent difference in the kinetics of induction of the toxicological endpoints under study, OEM0.3 provoked a higher overall cytotoxicity and genotoxicity than OEM2.5-0.3 and total PM2.5-0.3. Taken together, these results clearly showed that the finest particles are more damaging to BEAS-2B cells than PM2.5-0.3 because they are richer in organic compounds, thereby inducing more remarkable toxic effects. [ABSTRACT FROM AUTHOR]

In this paper, we present a city-scale (possibly global) air pollution network made of low-cost sensors for particulate matter concentration in the air. The components of the presented system are based on our research and experience from previous studies focused on air quality instruments and sensors for addressing urban air pollution. Sensor nodes are produced locally and distributed over Sarajevo, Bosnia and Herzegovina. The system collects, calibrates, and displays particulate matter concentrations in real-time. In comparison to the available measurements from governmental institutions, our system demonstrated good agreement of measured parameters, with several advantages including, higher resolution in space and time, lower costs, both horizontal and vertical measurements. One of the capabilities of the system is the real-time air pollution city map with animations. By installing multiple sensor nodes over a slope, we receive vertical measurements of temperature, humidity, and particulate matter concentration in real time, which gives a valuable insight into the dynamics of temperature inversion episodes and air pollution below the inversion layer. [ABSTRACT FROM AUTHOR]

The port of Cartagena, south-east Spain, is noted for its intense activity. This paper presents the influence of the COVID-19 pandemic on the recorded levels of six pollutants. Fifteen years of measurements were used, with two objectives. The first was to investigate how these pollutants evolved, together with their usual cycles. The second objective was to ascertain whether the COVID-19 pandemic had an impact on the concentrations recorded. The results showed that nitrogen oxide concentrations remained steady in the first half of the measurement period and decreased in the second half. SO2 concentrations decreased irregularly, whereas O3 and PM10 presented steady concentrations. The annual cycle was observed for nitrogen oxides and O3. Only SO2 evidenced no weekly cycle. Finally, the analysis of pre- and post-pandemic concentrations revealed a sharp decrease in nitrogen oxides, whereas the pandemic was not seen to have had any impact on the other pollutants. In addition, NO2 emerged as the best indicator of human activity—represented by car and maritime traffic—due to its response to the relaxation measures. Finally, Lamb weather types were calculated. The unclassified type was the most frequent. However, the greatest concentration changes were observed for anticyclonic and eastern flow types. [ABSTRACT FROM AUTHOR]

Mobile monitoring is used as an additional tool to collect air quality data at a high spatial resolution and to complement data from fixed air quality stations. Citizens are interested in contributing to air quality monitoring, and while the availability of low-cost air quality sensors can create opportunities to measure the air quality at a high spatial resolution, the data are often of lower quality, and sensors that measure combustion-related aerosols (like black carbon) are not commonly available. Mobile monitoring using a mid-range instrument can fill this gap. We present the results of a mobile BC (black carbon) monitoring campaign performed by citizens in Mechelen as part of a local citizen observatory (CO), Meet Mee Mechelen, initiated as part of the European H2020 project, Ground Truth 2.0. The goal of the study was two-fold: (1) to propose and evaluate a mobile monitoring method (data collection and data processing) to construct pollution maps of BC concentrations and (2) to demonstrate how to organize community-based air quality monitoring to measure both the spatial and temporal variations in air pollution levels. Measurements were taken during peak hours in four campaigns characterized by different meteorological conditions: October–November 2017, February–March 2018, June–July 2018 and September 2018. The results show large spatial and temporal variabilities. Spatial variability is influenced by traffic volume, stop-and-go traffic and also the building environment and the distance of biking paths from road traffic. The four different campaigns show similar spatial patterns, but due to background and meteorological influences, the absolute concentrations differ between seasons. A rescaling method using data from fixed stations in the air quality monitoring network (AQMN) was presented to construct maps representative of longer periods. This paper shows that mobile measurements can be used by CO to assess the spatial variability of air quality in a city. The data can be used to evaluate mobility plans, carry out hot spot detection, evaluate the exposure of cyclists as a function of cycling infrastructure and perform model validation. However, it is important to use high-quality instruments and apply the correct measurement methodology (number of repetitions, season) to obtain meaningful data. [ABSTRACT FROM AUTHOR]

Current evidence suggests that airborne pollutants have a detrimental effect on fetal growth through the emergence of small for gestational age (SGA) or term low birth weight (TLBW). The study's objective was to critically evaluate the available literature on the association between environmental pollution and the incidence of SGA or TLBW occurrence. A comprehensive literature search was conducted across Pubmed/MEDLINE, Web of Science, Cochrane Library, EMBASE, and Google Scholar using predefined inclusion and exclusion criteria. The methodology adhered to the PRISMA guidelines. The systematic review protocol was registered in PROSPERO with ID number: CRD42022329624. As a result, 69 selected papers described the influence of environmental pollutants on SGA and TLBW occurrence with an Odds Ratios (ORs) of 1.138 for particulate matter ≤ 10 μm (PM10), 1.338 for particulate matter ≤ 2.5 μm (PM2.5), 1.173 for ozone (O3), 1.287 for sulfur dioxide (SO2), and 1.226 for carbon monoxide (CO). All eight studies analyzed validated that exposure to volatile organic compounds (VOCs) is a risk factor for SGA or TLBW. Pregnant women in the high-risk group of SGA occurrence, i.e., those living in urban areas or close to sources of pollution, are at an increased risk of complications. Understanding the exact exposure time of pregnant women could help improve prenatal care and timely intervention for fetuses with SGA. Nevertheless, the pervasive air pollution underscored in our findings suggests a pressing need for adaptive measures in everyday life to mitigate worldwide environmental pollution. [ABSTRACT FROM AUTHOR]

Reducing carbon emissions while controlling air pollution is a dual challenge for China. However, few studies have analyzed whether there is a synergy between the two. In view of this, this paper takes the urban agglomeration in Central Henan as an example, uses multi-source remote sensing and panel data from 2000 to 2022 and analyzes the spatiotemporal evolution patterns and synergistic benefits of air pollution and carbon emissions based on the spatial distribution direction analysis model, coupling coordination degree model and multi-scale geographic weighting model. The results indicate the following: (1) Carbon emissions show a growing trend, but the difference in the carbon emissions of different cities is relatively large, showing the characteristics of "one center and two zones" in space. Air pollution shows a trend of first increasing and then decreasing. (2) The synergistic benefits have been continuously enhanced, and the overall unbalanced state has gradually become coordinated. There is no obvious aggregation feature. (3) The impact of socioeconomic factors on the synergistic benefit is obviously stronger than that of natural ecological factors, among which the total energy consumption, population density and industrial structure are the leading factors of the synergistic benefit of carbon emissions and air pollution. This study offers valuable insights for green development, high-quality growth and collaborative environmental governance within the Central Henan urban agglomeration. [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]

With the continuous acceleration of urbanization, air pollution has become an increasingly serious threat to public health. Strengthening the detection and control of pollutants has become a focal point in current society. In light of the increasing amount of literature in the field of air pollution control with every passing year, numerous reviews have been compiled; however, only a limited number employ bibliometric methods to comprehensively review and summarize research trends in this field. Herein, this study utilizes two bibliometric analysis tools, namely, CiteSpace (6.1.R6) and VOSviewer (1.6.20), to conduct a visual and comprehensive analysis of air pollution literature spanning 2000 to 2023. By doing so, it establishes a knowledge framework for research on air pollution control. Simultaneously, collaborative network analysis, reference co-citation network analysis, keyword co-occurrence network analysis, and keyword prominence are employed to undertake an exhaustive and profound visual examination within this domain. Results indicate that, over time, the number of relevant papers has exponentially increased, while interdisciplinary cooperation trends have gradually formed. Additionally, this study describes key areas of current research, including air pollution control residue treatment, regional joint air pollution control, and air pollution control mechanism analysis. Finally, challenges faced by researchers in this field and their different perspectives are discussed. To better integrate research findings on air pollution control, we explore the correlations among data and systematically present their developmental trends. This confirms the interdisciplinary nature of air pollution control research, in the hope of its guiding air pollution control in the future. [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]

The PM2.5 index is an important basis for measuring the degree of air pollution. The accurate prediction of PM2.5 concentration has an important guiding role in air pollution prevention and control. The Pearson Correlation Coefficient (PCC) is a common index used to mine the correlation between meteorological factors and other air pollutants. However, this index cannot be used to mine non-linear correlations, nor can it quantitatively analyze the weight of each related attribute. In order to accurately explore the correlation between meteorological factors and other air pollutants and to achieve an accurate prediction of PM2.5 concentration, this paper proposes a short- and long-time memory (LSTM) network prediction model based on Copula entropy (CE) and the adaptive genetic algorithm (AGA). By calculating CE, the correlation between multiple meteorological factors and various atmospheric pollutants and PM2.5 was analyzed. The correlation of influencing factors was sorted according to the size of the correlation coefficients. The contribution rate of meteorological factors and atmospheric pollutants to PM2.5 concentration was determined, used as the weight of each influencing factor and predicted as the input data of the prediction model. In this paper, a long- and short-term memory network (LSTM) suitable for time series data was selected as the prediction model, while the selection of model parameters was taken into account, and the relevant parameters were sought by an adaptive genetic algorithm (AGA). The air pollutant data and meteorological data of Beijing from 1 January 2016 to 31 December 2016 were selected, and MAE and RMSE were used as evaluation indexes. By comparing the experimental results of the CE-AGA-LSTM with those of other eight prediction models (LR, SVM, RF, ARMA, ST-LSTM, LSTM, CE-LSTM and CE-RNN), we found that among the models, the CE-AGA-LSTM model provided the lowest MAE and RMSE values, i.e., 14.5 and 21.88, respectively. At the same time, the loss rate and accuracy of the CE-AGA-LSTM model were evaluated, and the experimental results verified the validity of the model. [ABSTRACT FROM AUTHOR]

With the rapid development of urbanization, ambient air pollution is becoming increasingly serious. Out of many pollutants, fine particulate matter (PM2.5) is the pollutant that affects the urban atmospheric environment to the greatest extent. Fine particulate matter (PM2.5) concentration prediction is of great significance to human health and environmental protection. This paper proposes a CNN-SSA-DBiLSTM-attention deep learning fusion model. This paper took the meteorological observation data and pollutant data from eight stations in Bijie from 1 January 2015 to 31 December 2022 as the sample data for training and testing. For the obtained data, the missing values and the data obtained from the correlation analysis performed were first processed. Secondly, a convolutional neural network (CNN) was used for the feature selection. DBILSTM was then used to establish a network model for the relationship between the input and actual output sequences, and an attention mechanism was added to enhance the impact of the relevant information. The number of units in the DBILSTM and the epoch of the whole network were optimized using the sparrow search algorithm (SSA), and the predicted value was the output after optimization. This paper predicts the concentration of PM2.5 in different time spans and seasons, and makes a comparison with the CNN-DBILSTM, BILSTM, and LSTM models. The results show that the CNN-SSA-DBiLSTM-attention model had the best prediction effect, and its accuracy improved with the increasing prediction time span. The coefficient of determination (R2) is stable at about 0.95. The results revealed that the proposed CNN-SSA-DBiLSTM-attention ensemble framework is a reliable and accurate method, and verifies the research results of this paper in regard to the prediction of PM2.5 concentration. This research has important implications for human health and environmental protection. The proposed method could inspire researchers to develop even more effective methods for atmospheric environment pollution modeling. [ABSTRACT FROM AUTHOR]

Accurate estimations and assessments of vehicle emissions can support decision-making processes. Current emission estimation tools involve several calculation methods that provide estimates of the exhaust components that result from driving on urban arterial roads. This is an important consideration, as the emissions generated have a direct impact on the health of pedestrians near the roads. In recent years, there has been an increase in the use of emission models, especially in combination with traffic simulator models. This is because it is very difficult to obtain an actual measurement of road emissions for all vehicles travelling along the analysed road section. This paper concerns a review of selected traffic simulations and the estimation of exhaust gas components models. The models presented have been aggregated into a group with respect to their scale of accuracy as micro, meso, and macro. This paper also presents an overview of selected works that combine both traffic and emission models. The presented literature review also emphasises the proper calibration process of simulation models as the most important factor in obtaining accurate estimates. This work also contains information and recommendations on modelling that may be helpful in selecting appropriate emission estimation tools to support decision-making processes for, e.g., road managers. [ABSTRACT FROM AUTHOR]

The continuous degradation of cultural heritage artifacts (due to different factors, including the rising air pollution, climate change or excessive biological activity, among others) requires the continuous development of protection strategies, technologies and materials. In this regard, polyelectrolytes have offered effective ways to fight against degradation but also to conserve the cultural heritage objects. In this review, we highlight the key developments in the creation and use of polyelectrolytes for the preservation, consolidation and cleaning of the cultural heritage artifacts (with particular focus on stone, metal and artifacts of organic nature, such as paper, leather, wood or textile). The state of the art in this area is presented, as well as future development perspectives. [ABSTRACT FROM AUTHOR]

In recent years, air pollution in Xinjiang, the core region of the Silk Road Economic Belt, has become increasingly severe, posing a more significant threat to human health. This paper selects the tropospheric ozone, nitrogen dioxide, and formaldehyde datasets under OMI remote-sensing monitoring and the PM2.5 dataset in China High Air Pollutants (CHAP) for 2018–2021. The spatial and temporal distribution of multi-pollutants, the spatial autocorrelation of Moran's I index pollutants, and the correlation between pollutants in the warm period were studied in southern Xinjiang. Meanwhile, the geographical and temporal weighted regression (GTWR) model was used for influencing factor analysis, and the BenMap-CE model was used for health benefit analysis. The results showed that the spatial distribution of ozone concentration values in southern Xinjiang shows a decreasing distribution pattern from the east–central region to the western and southern regions. The spatial distribution of formaldehyde concentration values is opposite to that of ozone. There is a clear high-value area in the ozone concentration value in April–September. The NO2 column concentration values were in the range of 0.55~1.09 × 1015 molec/cm2 in most parts of southern Xinjiang. The area of high concentration values is located in the northeast of the study area; PM2.5 concentration values are higher in the middle area of southern Xinjiang. The spatial autocorrelation characteristics showed that the spatial aggregation of O3 and NO2 displayed a slow increasing trend year by year. The spatial aggregation of HCHO and PM2.5 fluctuated slightly in four years. The overall trend of HCHO is slowly decreasing, while PM2.5 is fluctuating and increasing. In the GTWR model analysis, overall, the atmospheric pressure has a strong influence on all pollutants. The effect of NO2 on O3 was higher than that of HCHO among the four pollutants. The correlation between O3 and PM2.5 was as high as −0.7872. The BenMap-CE health benefits assessment concluded that the number of premature deaths caused by ozone pollution was much higher than that of premature deaths caused by PM2.5. The highest number of premature deaths for both pollutants occurred in Kashgar. [ABSTRACT FROM AUTHOR]

Air pollution is a critical environmental concern that poses significant health risks and affects multiple aspects of human life. ML algorithms provide promising results for air pollution prediction. In the existing scientific literature, Long Short-Term Memory (LSTM) predictive models, as well as their combination with other statistical and machine learning approaches, have been utilized for air pollution prediction. However, these combined algorithms may not always provide suitable results due to the stochastic nature of the factors that influence air pollution, improper hyperparameter configurations, or inadequate datasets and data characterized by great variability and extreme dispersion. The focus of this paper is applying and comparing the performance of Support Vector Machine and hybrid LSTM regression models for air pollution prediction. To identify optimal hyperparameters for the LSTM model, a hybridization with the Genetic Algorithm is proposed. To mitigate the risk of overfitting, the bagging technique is employed on the best LSTM model. The proposed predicitive model aims to determine the Common Air Quality Index level for the next hour in Niksic, Montenegro. With the hybridization of the LSTM algorithm and by applying the bagging technique, our approach aims to significantly enhance the accuracy and reliability of hourly air pollution prediction. The major contribution of this paper is in the application of advanced machine learning analysis and the combination of the LSTM, Genetic Algorithm, and bagging techniques, which have not been previously employed in the analysis of air pollution in Montenegro. The proposed model will be made available to interested management structures, local governments, national entities, or other relevant institutions, empowering them to make effective pollution level predictions and take appropriate measures. [ABSTRACT FROM AUTHOR]

Elevated levels of fine particulate matter (PM2.5) in the atmosphere present substantial risks to human health and welfare. The accurate assessment of PM2.5 concentrations plays a pivotal role in facilitating prompt responses by pertinent regulatory bodies to mitigate air pollution. Additionally, it furnishes indispensable information for epidemiological studies concentrating on PM2.5 exposure. In recent years, predictive models based on deep learning (DL) have offered promise in improving the accuracy and efficiency of air quality forecasts when compared to other approaches. Long short-term memory (LSTM) networks have proven to be effective in time series forecasting tasks, including air pollution prediction. However, optimizing LSTM models for enhanced accuracy and efficiency remains an ongoing research area. In this paper, we propose a novel approach that integrates the novel binary chimp optimization algorithm (BChOA) with LSTM networks to optimize air pollution prediction models. The proposed BChOA, inspired by the social behavior of chimpanzees, provides a powerful optimization technique to fine-tune the LSTM architecture and optimize its parameters. The evaluation of the results is performed using cross-validation methods such as the coefficient of determination ( R 2 ), accuracy, the root mean square error (RMSE), and receiver operating characteristic (ROC) curve. Additionally, the performance of the BChOA-LSTM model is compared against eight DL architectures. Experimental evaluations using real-world air pollution data demonstrate the superior performance of the proposed BChOA-based LSTM model compared to traditional LSTM models and other optimization algorithms. The BChOA-LSTM model achieved the highest accuracy of 96.41% on the validation datasets, making it the most successful approach. The results show that the BChOA-LSTM architecture performs better than the other architectures in terms of the   R 2 convergence curve, RMSE, and accuracy. [ABSTRACT FROM AUTHOR]

In today's urban environments, accurately measuring and forecasting air pollution is crucial for combating the effects of pollution. Machine learning (ML) is now a go-to method for making detailed predictions about air pollution levels in cities. In this study, we dive into how air pollution in urban settings is measured and predicted. Using the PRISMA methodology, we chose relevant studies from well-known databases such as PubMed, Springer, IEEE, MDPI, and Elsevier. We then looked closely at these papers to see how they use ML algorithms, models, and statistical approaches to measure and predict common urban air pollutants. After a detailed review, we narrowed our selection to 30 papers that fit our research goals best. We share our findings through a thorough comparison of these papers, shedding light on the most frequently predicted air pollutants, the ML models chosen for these predictions, and which ones work best for determining city air quality. We also take a look at Skopje, North Macedonia's capital, as an example of a city still working on its air pollution measuring and prediction systems. In conclusion, there are solid methods out there for air pollution measurement and prediction. Technological hurdles are no longer a major obstacle, meaning decision-makers have ready-to-use solutions to help tackle the issue of air pollution. [ABSTRACT FROM AUTHOR]

Simple Summary: Human exposure to pollutants in indoor environments is a relevant health concern. Long-term monitoring data in indoor environments are largely missing due to a lack of adequate measuring devices. Biomonitoring (the use of living organisms to identify/assess potential hazardous exposure to chemicals and their effects) can provide useful information on indoor air quality and effects. Given their ability to intercept pollutants from the atmosphere, lichens and mosses are commonly used as outdoor biomonitors of atmospheric pollution by potentially toxic elements. Their application in indoor environment is recent but represents a promising output of the scientific research. In this review, indoor biomonitoring studies carried out using mosses and lichens have been compared, and critical issues and open matters have been underlined, as well as future perspectives related to their fruitful application in indoor environments. This review highlights the peculiarities of each study and the need for the development of shared harmonised protocols. Biomonitoring in indoor environments is a recent application, and so far, indoor air quality (IAQ) has been investigated only in a few cases using photosynthesising biomonitors. On the whole, 22 studies have been selected and reviewed, being specifically focused on the assessment of IAQ using biomonitors, such as lichens (9 papers), mosses (10), or their combination (3). In general, indoor samples face an altered light regime, ventilation, and a reduced hydration, which should be taken into consideration during the design and implementation of indoor monitoring. This review highlights critical issues (and some solutions) related to sample devitalisation (moss), hydration during exposure, preparation of the exposure device (mostly lichen and moss bags), duration of the exposure, post-exposure treatments, assessment of the vitality of the samples, as well as data elaboration and interpretation. This review evidences the feasibility and usefulness of lichen/moss monitoring in indoor environments and the need to develop standardised protocols. [ABSTRACT FROM AUTHOR]

With economic development, air pollution is becoming increasingly serious, which affects the sustainable development of the global economy. In order to explore policy measures to curb air pollution, this paper selects data from 30 regions in China and explores their impact studies on air pollution from the perspectives of environmental regulation and foreign direct investment using a systematic GMM model. Then, the threshold effect model is selected to verify their nonlinear relationship. The conclusions are as follows: (1) Environmental regulation does not pass the significance test. There is no significant "U-shaped" or "inverted U-shaped" relationship between environmental regulation and air pollution. In the eastern region, there is an inverted U-shaped relationship, which is consistent with the environmental Kuznets curve (EKC) hypothesis, and the effect in the central and western regions is higher than in the eastern region of China. (2) The entry of foreign direct investment (FDI) aggravates the environmental pollution problem in China, and the overall status quo is consistent with the pollution haven hypothesis, which shows an inverted U-shaped curve between FDI and air pollution. The eastern region does not have any U-shaped relationships. The other two regions show an inverted U-shaped curve between foreign direct investment and air pollution; these two regions are still in the left half of the curve, and the increase in foreign capital will lead to aggravation of the pollution status quo. (3) The industrial structure will aggravate air pollution in the whole country and the central and western regions; the eastern region shows a suppression effect, but this is not significant. Urbanization exacerbates air pollution in the central and western regions but has an insignificant effect in the eastern region. The level of economic development increases air pollution in all regions. The expansion of the population size brings a large amount of production and living pollution, which aggravates environmental pollution. The research in this paper can provide theoretical references for regional policies to control air pollution. [ABSTRACT FROM AUTHOR]

Carbonaceous aerosol is the most significant contributor to the particulate matter in the atmosphere. It is composed of a complex mixture of compounds containing carbon atoms and is usually classified into two main fractions: black carbon (BC) and organic carbon (OC). BC is essentially a primary pollutant emitted in particulate form, and its chemical stability excludes chemical transformations during its lifetime in the atmosphere. Therefore, it should be considered a tracer for the long-range transport of anthropogenic air pollution. OC has both primary and secondary origins: primary OC particulate is formed during combustion and emitted mainly as submicron particles, whereas secondary OC particulate originates from gas-to-particle conversion processes. This SI aims to deepen the state of the art on this important topic ranging from theory to the development of new instrumentation useful for this determination, to the chemical–physical problems in the atmosphere, to the health and toxicological implications related to exposure to these pollutants as well as papers showing historical data series both in urban, rural, and remote areas are also appreciated. [ABSTRACT FROM AUTHOR]

This paper presents the development of a miniaturized sensor device for selective detection of pathogens, specifically Influenza A Influenza virus, as an enveloped virus is relatively vulnerable to damaging environmental impacts. In consideration of environmental factors such as humidity and temperature, this particular pathogen proves to be an ideal choice for our study. It falls into the category of pathogens that pose greater challenges due to their susceptibility. An impedance biosensor was integrated into an existing platform and effectively separated and detected high concentrations of airborne pathogens. Bio-functionalized hydrogel-based detectors were utilized to analyze virus-containing particles. The sensor device demonstrated high sensitivity and specificity when exposed to varying concentrations of Influenza A virus ranging from 0.5 to 50 μg/mL. The sensitivity of the device for a 0.5 μg/mL analyte concentration was measured to be 695 Ω· mL/μg. Integration of this pathogen detector into a compact-design air quality monitoring device could foster the advancement of personal exposure monitoring applications. The proposed sensor device offers a promising approach for real-time pathogen detection in complex environmental settings.

Eight candidate genes selected in this study were previously associated with gene-environment interactions in asthma in an urban area. These genes were analyzed in a familial collection from a founder and remote population (Saguenay-Lac-Saint-Jean; SLSJ) located in an area with low air levels of ozone but with localized areas of relatively high air pollutant levels, such as sulphur dioxide, when compared to many urban areas. Polymorphisms (SNPs) were extracted from the genome-wide association study (GWAS) performed on the SLSJ familial collection. A transmission disequilibrium test (TDT) was performed using the entire family sample (1,428 individuals in 254 nuclear families). Stratification according to the proximity of aluminium, pulp and paper industries was also analyzed. Two genes were associated with asthma in the entire sample before correction (CAT and NQO1) and one was associated after correction for multiple analyses (CAT). Two genes were associated when subjects were stratified according to the proximity of aluminium industries (CAT and NQO1) and one according to the proximity of pulp and paper industries (GSTP1). However, none of them resisted correction for multiple analyses. Given that the spatial pattern of environmental exposures can be complex and inadequately represented by a few stationary monitors and that exposures can also come from sources other than the standard outdoor air pollution (e.g., indoor air, occupation, residential wood smoke), a new approach and new tools are required to measure specific and individual pollutant exposures in order to estimate the real impact of gene-environment interactions on respiratory health.

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]

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]