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Abstract To reduce pollution emissions and achieve a cleaner production in paper industry, it is essential to understand the inner links between concentrations of gaseous pollutants, types of final products and production techniques. In this study, the concentrations of four potential gaseous pollutants, total volatile organic compounds (TVOC), formaldehyde (HCHO), hydrogen sulfide (H 2 S), and hydrocarbon compounds (C x H y), in the ambient air on thirty sampling points of five pulp and paper mills were detected and analyzed. The results were summarized with respect to the following aspects: (1) Levels of four gaseous pollutants on all the sampling points in five mills. It was obvious that the situation of gaseous pollution from pulp mill was much more serious than those from paper mills. (2) Gaseous pollutant distinctions due to different production processes. The levels of gaseous pollutants from five mills were greatly influenced by the utilized raw materials and the applied paper chemical agents (flavor, coating materials and additives) in mills. (3) Gaseous pollution comparisons at common points. At the two common points in five mills, i.e. the wet end of paper machine and the wastewater treatment process, TVOC and HCHO, C x H y and H 2 S were the main gaseous pollutants, respectively. In addition, some feasible measures to realize a cleaner production in pulping and papermaking process were also put forward in the article. Highlights • We analyze the levels of 4 gaseous pollutants on 30 sites of 5 pulp & paper mills. • Gaseous pollution distinctions due to different production processes are studied. • Feasible measures to realize the cleaner production in the processes are suggested. • Gaseous pollution are greatly affected by raw materials and added paper chemicals. [ABSTRACT FROM AUTHOR]

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]

Accurate and quantitative profiling of air particulate matter (PM) compositions is essential for assessing local pollution information. The method combining mobile aerial sampling using unmanned aerial vehicles (UAVs) and prompt analysis excels in this regard as it allows spatiotemporal mapping of air pollution, especially in the vertical direction. However, applications of the method are still scarce as it is limited by a lack of sampling reliability due to insufficient aerial sampling time and a lack of accurate, portable quantification techniques. In this work, by integrating mobile aerial sampling with in-flight tethered charging and smartphone-based colorimetric analysis in a cost-effective paper microfluidic device, we present a method for quantitative, reliable profiling of spatiotemporal variation in air PM compositions. The method extends aerial sampling time to 12–15 flight hours per deployment, thereby significantly improving sampling reliability while maintaining the maneuverability of the UAVs. Also, smartphone-based colorimetric analysis combined with paper-based microfluidics enables portable, economically efficient analysis and is well-suited for using in low-resource settings. We demonstrated the utility of the method by carrying out a spatiotemporal variation study of air PM trace metal components (Fe, Ni, and Mn) at 4 geographical locations in Fuzhou, China, for a period of 21 days, and the results were in good agreement with results obtained from using a commercial instrument. Beside air PM composition study, this method is universally applicable and holds great potential to be extended to multipollutant analysis, such as prompt detection of airborne viruses, bacteria, and others. [ABSTRACT FROM AUTHOR]

This study investigates the impact of air pollution on health outcomes in Middle Eastern countries, a region facing severe environmental challenges. As such, these are important in an effort to add up to policy-level as well as interventional changes that can be put in practice in the area of public health. Numeration analysis and association with health parameters was carried out by using Analytical tools such as, AIR Data, ARIMA,ANN, SVM and Exponential smoothing. Amongst the models, Support Vector Machine came again on top, with high accuracy yielding Mean Absolute Percentage Error of approximately 1%. Mortality of Air pollution in Qat from the case of Mortality of Air Pollution in Qatar is 959 while Auto regressive Integrated Moving average is 11.096, Exponential Smoothing 9.892 and Artificial Neural Networks are the source of inspiration for the development of this paper 4.61. The above perceptions indicate that there is need to adapt modeling strategies depending on the context and establish that it is possible to implement ML models in public health planning basket. This paper publishes the methodological frameworks for the purpose of modeling and analysis of the EHDs and serves as policy prescription for the policy makers to intending to reduce the effects of air borne pollution on health. [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]

Wildfires have short- and long-term impacts on the geoenvironment, including the changes to biogeochemical and mechanical properties of soils, landfill stability, surface- and groundwater, air pollution, and vegetation. Climate change has increased the extent and severity of wildfires across the world. Simultaneously, anthropogenic activities—through the expansion of urban areas into wildlands, abandonment of rural practices, and accidental or intentional fire-inception activities—are also responsible for a majority of fires. This paper provides an overall review and critical appraisal of existing knowledge about processes induced by wildfires and their impact on the geoenvironment. Burning of vegetation leads to loss of root reinforcement and changes in soil hydromechanical properties. Also, depending on the fire temperature, soil can be rendered hydrophobic or hydrophilic and compromise soil nutrition levels, hinder revegetation, and, in turn, increase post-fire erosion and the debris flow susceptibility of hillslopes. In addition to direct hazards, wildfires pollute air and soil with smoke and fire suppression agents releasing toxic, persistent, and relatively mobile contaminants into the geoenvironment. Nevertheless, the mitigation of wildfires' geoenvironmental impacts does not fit within the scope of this paper. In the end, and in no exhaustive way, some of the areas requiring future research are highlighted. [ABSTRACT FROM AUTHOR]

The aim of the study was to evaluate the TAD of Akure, Ondo State, Nigeria. In this study, rainwater samples were collected monthly in a polyethylene container attached to a funnel for a period of twelve (12) months at 40 designated sites (residential, industrial, and traffic-related areas). The rainwater samples were filtered, the masses of TAD on the filter paper were determined gravimetrically, and atmospheric deposition rates were calculated. Meteorological data were measured at a monitoring site at the Department of Meteorology, Federal University of Technology, Akure, Ondo State, Nigeria. From the results obtained in this study, it was observed that deposition rated ranged between 5 and 427 μg/m². The average temperatures, wind speed, relative humidity, rainfall and wind direction for the periods were 25.00oC, 0.63 mph, 83.74%, 0.03mm and 117.43oC (2015) and 26.2 oC, 0.74mph, 81.89%, 0.92mm, 118.32o (2016). There were relationships between TAD and meteorological parameters. The results showed that TAD in many parts of the studied areas exceeded the World Health Organization guideline standards (350 μg/m²). [ABSTRACT FROM AUTHOR]

The observed global climate change is an indisputable cause of the increased frequency of extreme weather events and related natural disasters. This phenomenon is observed all over the world including Poland. Moreover, Polish citizens as tourists are also exposed to climate phenomena that do not occur in our climate zone. Extreme weather events and related disasters can have a significant impact on people with allergic diseases, including asthma. These effects may be associated with the exposure to air pollution, allergens, and specific microclimate conditions. Under the auspices of the Polish Society of Allergology, experts in the field of environmental allergy prepared a statement on climate changes, natural disasters and allergy and asthma to reduce the risk of adverse health events provoked by climate and weather factors. The guidelines contain the description of the factors related to climate changes and natural disasters affecting the course of allergic diseases, the specific microclimate conditions and the recommendations of the Polish Society of Allergology for vulnerable population, patients suffering from asthma and allergy diseases, allergologists and authorities in the event of climate and weather hazards. [ABSTRACT FROM AUTHOR]

We are witnessing an explosion in carbon calculators for estimating the greenhouse gas (GHG) emissions (i.e., carbon footprint) of households, buildings, cities, and processes. Seeking to capitalize on the emergent 'green' consumer, corporations are leading the next iteration in carbon footprinting: consumer products. This potentially lucrative low-carbon frontier, however, faces steep challenges due to complexities of scale, largely a function of the number of actors and geographies involved in globalized commodity and energy networks, and scope, which increasingly demands inclusion of emissions due to land use change (e.g., biofuel production, timber harvest, livestock grazing, mining). Life cycle assessment (LCA)-the principal method behind product-level GHG emissions footprint protocols-frequently avoids these challenges by narrowly delineating system boundaries, thereby excluding the 'messiness' of space and place. Through a comparative model of energy sources and emissions in the globalized paper industry, this article reveals how complexities associated with geographic variation and land use change create indeterminacy in footprints based on these protocols. Using industry and trade data, the authors develop geographic information system transportation and energy models to map the globally dispersed pulp supply networks and to rescale Intergovernmental Panel on Climate Change GHG inventory guidelines to include carbon loss associated with land use change in the carbon footprint of coated paper. Given their integrative abilities to conceptualize and model coupled human-ecological systems, sophisticated understanding of time-space dynamics and critical theoretical insights, geographers have much to contribute to the LCA and product carbon footprinting enterprise, which to date has been largely the intellectual domain of engineers. [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]

Background: Epidemiological studies of long-term exposure to outdoor air pollution have consistently documented associations with morbidity and mortality. Air pollution exposure in these epidemiological studies is generally assessed at the residential address, because individual time-activity patterns are seldom known in large epidemiological studies. Ignoring time-activity patterns may result in bias in epidemiological studies. The aims of this paper are to assess the agreement between exposure assessed at the residential address and exposures estimated with time-activity integrated and the potential bias in epidemiological studies when exposure is estimated at the residential address. Main body: We reviewed exposure studies that have compared residential and time-activity integrated exposures, with a focus on the correlation. We further discuss epidemiological studies that have compared health effect estimates between the residential and time-activity integrated exposure and studies that have indirectly estimated the potential bias in health effect estimates in epidemiological studies related to ignoring time-activity patterns. A large number of studies compared residential and time-activity integrated exposure, especially in Europe and North America, mostly focusing on differences in level. Eleven of these studies reported correlations, showing that the correlation between residential address-based and time-activity integrated long-term air pollution exposure was generally high to very high (R > 0.8). For individual subjects large differences were found between residential and time-activity integrated exposures. Consistent with the high correlation, five of six identified epidemiological studies found nearly identical health effects using residential and time-activity integrated exposure. Six additional studies in Europe and North America showed only small to moderate potential bias (9 to 30% potential underestimation) in estimated exposure response functions using residence-based exposures. Differences of average exposure level were generally small and in both directions. Exposure contrasts were smaller for time-activity integrated exposures in nearly all studies. The difference in exposure was not equally distributed across the population including between different socio-economic groups. Conclusions: Overall, the bias in epidemiological studies related to assessing long-term exposure at the residential address only is likely small in populations comparable to those evaluated in the comparison studies. Further improvements in exposure assessment especially for large populations remain useful. [ABSTRACT FROM AUTHOR]

Many researchers have studied the role of air pollutants on cognitive function, changes in brain structure, and occurrence of dementia. Due to the wide range of studies and often contradictory results, the present systematic review was conducted to try and clarify the relationship between air pollutants and dementia. To identify studies for this review, a systematic search was conducted in Scopus, PubMed, and Web of Science databases (without historical restrictions) until May 22, 2023. The PECO statement was created to clarify the research question, and articles that did not meet the criteria of this statement were excluded. In this review, animal studies, laboratory studies, books, review articles, conference papers and letters to the editors were avoided. Also, studies focused on the effect of air pollutants on cellular and biochemical changes (without investigating dementia) were also excluded. A quality assessment was done according to the type of design of each article, using the checklist developed by the Joanna Briggs Institute (JBI). Finally, selected studies were reviewed and discussed in terms of Alzheimer's dementia and non-Alzheimer's dementia. We identified 14,924 articles through a systematic search in databases, and after comprehensive reviews, 53 articles were found to be eligible for inclusion in the current systematic review. The results showed that chronic exposure to higher levels of air pollutants was associated with adverse effects on cognitive abilities and the presence of dementia. Studies strongly supported the negative effects of PM2.5 and then NO2 on the brain and the development of neurodegenerative disorders in old age. Because the onset of brain structural changes due to dementia begins decades before the onset of disease symptoms, and that exposure to air pollution is considered a modifiable risk factor, taking preventive measures to reduce air pollution and introducing behavioral interventions to reduce people's exposure to pollutants is advisable. [ABSTRACT FROM AUTHOR]

Understanding public awareness of environmental problems is vital for effectively formulating sustainable policies. This paper aims to investigate the impacts of two perspectives—external air pollution and individual health status—on public awareness by leveraging panel data from two waves of the China Family Panel Studies (CFPS) conducted between 2018 and 2020. The model integrates provincial-level PM2.5 concentration indicators and SO2, PMs, and NOx emissions. The results reveal a significantly positive correlation between air pollution and public awareness of environmental problems in China. Additionally, this study examines the impact of self-assessed health shock by categorizing it into worse and better health. The influence of better health is insignificant. Conversely, when individuals experience worse health, they may perceive it as a psychological loss, leading to a significant increase in public awareness of environmental problems. This study provides valuable insights for mitigating air pollution and reinforcing public health in developing countries. [ABSTRACT FROM AUTHOR]

Air quality is a fundamental component of a healthy environment for human beings. Monitoring networks for air pollution have been established in numerous industrial zones. The data collected by the pervasive monitoring devices can be utilized not only for determining the current environmental condition, but also for forecasting it in the near future. This paper considers the applications of different machine learning methods for the prediction of the two most widely used quantities. Particulate matter (PM) with a diameter of 2.5 and 10 µm, respectively. The data are collected via a proprietary monitoring station, designated as the Ecolumn. The Ecolumn monitors a number of key parameters, including temperature, pressure, humidity, PM 1.0, PM 2.5, and PM 10, in a timely manner. The data were employed in the development of multiple models based on selected machine learning methods. The decision tree, random forest, recurrent neural network, and long short-term memory models were employed. Experiments were conducted with varying hyperparameters and network architectures. Different time scales (10 min, 1 h, and 24 h) were examined. The most optimal results were observed for the Long Short-Term Memory algorithm when utilizing the shortest available time spans (shortest averaging times). The decision tree and random forest algorithms demonstrated unexpectedly high performance for long averaging times, exhibiting only a slight decline in accuracy compared to neural networks for shorter averaging times. Recommendations for the potential applicability of the tested methods were formulated. [ABSTRACT FROM AUTHOR]

Air pollution is an integral part of climatic, environmental, and socioeconomic current affairs and a cross-cutting component of certain United Nations Sustainable Development Goals (SDGs). Hence, reliable information on air pollution and human exposure is a crucial element in policy recommendations and decisions. At the same time, Earth Observation is steadily gaining confidence as a data input in the calculation of various SDG indicators. The current paper focuses on the usability of modern satellite remote sensing in the context of SDGs relevant to air quality. We introduce the socioeconomic importance of air quality and discuss the current uptake of geospatial information. The latest developments in Earth Observation provide measurements of finer spatial, temporal, and radiometric resolution products with increased global coverage, long-term continuation, and coherence in measurements. Leveraging on the two latest operational satellite technologies available, namely the Sentinel-5P and the Geostationary Environment Monitoring Spectrometer (GEMS) missions, we demonstrate two potential operational applications for quantifying air pollution at city and regional scales. Based on the two examples and by discussing the near-future anticipated geospatial capabilities, we showcase and advocate that the potential of satellite remote sensing as a, complementary to ground station networks, source of air pollution information is gaining confidence. As such, it can be an invaluable tool for quantifying global air pollution and deriving robust population exposure estimates. [ABSTRACT FROM AUTHOR]

Individuals' sensitivity to climate hazards is a central component of their vulnerability to climate change. In this paper, we introduce and outline the utility of a new intraindividual variability construct, affective sensitivity to air pollution (ASAP)–defined as the extent to which an individual's affective states fluctuate in accordance with daily changes in air quality. As such, ASAP pushes beyond examination of differences in individuals' exposures to air pollution to examination of differences in individuals' sensitivities to air pollution. Building on known associations between air pollution exposure and adverse mental health outcomes, we empirically illustrate how application of Bayesian multilevel models to intensive repeated measures data obtained in an experience sampling study (N = 150) over one year can be used to examine whether and how individuals' daily affective states fluctuate with the daily concentrations of outdoor air pollution in their county. Results indicate construct viability, as we found substantial interindividual differences in ASAP for both affect arousal and affect valence. This suggests that repeated measures of individuals' day-to-day affect provides a new way of measuring their sensitivity to climate change. In addition to contributing to discourse around climate vulnerability, the intraindividual variability construct and methodology proposed here can help better integrate affect and mental health in climate adaptation policies, plans, and programs. [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]

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]

Abstract: Cooling tower emissions are harmful for several reasons such as air polluting, wetting, icing and solid particle deposition, but mainly due to human health hazards (i.e. Legionella). There are several methods for measuring drift drops. This paper is focussed on the sensitive paper technique, which is suitable in low drift scenarios and real conditions. The lack of an automatic classification method motivated the development of a digital image process algorithm for the Sensitive Paper method. This paper presents a detailed description of this method, in which, drop-like elements are identified by means of the Canny edge detector combined with some morphological operations. Afterwards, the application of a J48 decision tree is proposed as one of the most relevant contributions. This classification method allows us to discern between stains whose origin is a drop and stains whose origin is not a drop. The method is applied to a real case and results are presented in terms of drift and PM10 emissions. This involves the calculation of the main features of the droplet distribution at the cooling tower exit surface in terms of drop size distribution data, cumulative mass distribution curve and characteristic drop diameters. The Log-normal and the Rosin–Rammler distribution functions have been fitted to the experimental data collected in the tests and it can been concluded that the first one is the most suitable for experimental data among the functions tested (whereas the second one is less suitable). Realistic PM10 calculations include the measurement of drift emissions and Total Dissolved Solids as well as the size and number of drops. Results are compared to the method proposed by the U.S. Environmental Protection Agency assessing its overestimation. Drift emissions have found to be 0.0517% of the recirculating water, which is over the Spanish standards limit (0.05%). [Copyright &y& Elsevier]

This paper presents the findings from a collaborative interlaboratory comparison exercise designed to assess oxidative potential (OP) measurements conducted by 20 laboratories worldwide. This study represents an innovative effort as the first exercise specifically aimed at harmonising this type of OP assay, setting a new benchmark in the field. Over the last decade, there has been a noticeable increase in OP studies, with numerous research groups investigating the effects of exposure to air pollution particles through the evaluation of OP levels. However, the absence of standardised methods for OP measurements has resulted in variability in results across different groups, rendering meaningful comparisons challenging. To address this issue, this study engages in an international effort to compare OP measurements using a simplified method (with a dithiothreitol (DTT) assay). Here, we quantify the OP in liquid samples to focus on the protocol measurement itself, while future ILCs should aim to assess the full-chain process, including the sample extraction. We analyse the similarities and discrepancies observed in the results, identifying the critical parameters (such as the instrument used, the use of a simplified protocol, the delivery and analysis time) that could influence OP measurements, and provide recommendations for future studies and interlaboratory comparisons. Even if other crucial aspects, such as sampling PM methods, sample storage, extraction methods and conditions, and the evaluation of other OP assays, still need to be standardised. This collaborative approach enhances the robustness of the OP-DTT assay and paves the way for future studies to build on a unified framework. This pioneering work concludes that interlaboratory comparisons provide essential insights into the OP metric and are crucial to move toward the harmonisation of OP measurements. [ABSTRACT FROM AUTHOR]

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]

Low‐cost sensing and the Internet of Things (IoT), present new possibilities for unconventional monitoring of environmental parameters. This paper describes a series of intersecting networks of particulate matter sensors that were deployed across the Birmingham conurbation for a 12‐month period. The networks consisted of a combination of commercially available sensors and University developed sensors. Data from these networks were assimilated with data from a third‐party Zephyr deployment, along with the DEFRA AURN network, which was hosted on an open‐source online platform. This nesting of sensor networks allowed for new insights into sensor performance, including the accuracy of a large network to detect regional concentrations and the number of sensors needed for effective monitoring beyond indicative measurements. After comprehensive data validation steps, the sensors were shown to perform well during co‐location with reference instrumentation (exhibiting slopes of 0.74–1.3). The sensors demonstrated good capability of detecting temporal patterns of regional PM2.5 with the mean of the entire sensor network recording an annual mean PM2.5 concentration within 0.2 μgm−3 of the regulatory network annual mean observation. Network‐derived statistics for estimating urban background concentrations compared to a reference site increase in‐line with the number of sensors available, however when assessing this for near‐source concentrations the importance of sensor location rather than the number of sensors is highlighted. Overall, the network provided novel insights into local concentrations, detecting similar hotspots to those identified by a high‐resolution model. The increased spatial coverage afforded by the sensor network has the potential to support higher resolution evaluation of models and provide unprecedented spatial evidence for air pollution management interventions. [ABSTRACT FROM AUTHOR]

In times of growing concern about the impacts of air pollution across the globe, lower-cost sensor technology is giving the first steps in helping to enhance our understanding and ability to manage air quality issues, particularly in regions without established monitoring networks. While the benefits of greater spatial coverage and real-time measurements that these systems offer are evident, challenges still need to be addressed regarding sensor reliability and data quality. Given the limitations imposed by intellectual property, commercial implementations are often "black boxes", which represents an extra challenge as it limits end users' understanding of the data production process. In this paper we present an overview of the QUANT (Quantification of Utility of Atmospheric Network Technologies) study, a comprehensive 3-year assessment across a range of urban environments in the United Kingdom, evaluating 43 sensor devices, including 119 gas sensors and 118 particulate matter (PM) sensors, from multiple companies. QUANT stands out as one of the most comprehensive studies of commercial air quality sensor systems carried out to date, encompassing a wide variety of companies in a single evaluation and including two generations of sensor technologies. Integrated into an extensive dataset open to the public, it was designed to provide a long-term evaluation of the precision, accuracy and stability of commercially available sensor systems. To attain a nuanced understanding of sensor performance, we have complemented commonly used single-value metrics (e.g. coefficient of determination, R2 ; root mean square error, RMSE; mean absolute error, MAE) with visual tools. These include regression plots, relative expanded uncertainty (REU) plots and target plots, enhancing our analysis beyond traditional metrics. This overview discusses the assessment methodology and key findings showcasing the significance of the study. While more comprehensive analyses are reserved for future detailed publications, the results shown here highlight the significant variation between systems, the incidence of corrections made by manufacturers, the effects of relocation to different environments and the long-term behaviour of the systems. Additionally, the importance of accounting for uncertainties associated with reference instruments in sensor evaluations is emphasised. Practical considerations in the application of these sensors in real-world scenarios are also discussed, and potential solutions to end-user data challenges are presented. Offering key information about the sensor systems' capabilities, the QUANT study will serve as a valuable resource for those seeking to implement commercial solutions as complementary tools to tackle air pollution. [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]

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 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]

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]

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]

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]

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]

Experiencing rapid development and growth, cities worldwide face a surge in air pollution, primarily driven by the increased concentrations of the particulate matter (PM) originating from various anthropogenic sources, such as traffic, household fuel combustion, and industrial and construction activities. Urban green spaces can naturally filter PM through physicochemical processes, serving as effective urban planning instruments for the improvement of the air quality. Focusing on a street in Novi Sad, the second-largest city in Serbia, this study investigates the efficiency of vegetation in mitigating air pollution, specifically PM10 emissions from traffic and construction activities. Using the contemporary monitoring and modelling techniques for measuring and predicting PM10 concentrations, the focus of this research is to evaluate the efficacy of vegetation in affecting and minimizing detected PM concentrations. The results indicate a significant reduction in the monitored PM10 concentrations behind the green barrier compared to the modelled concentrations near the pollution source (on the road) for both traffic and construction-related emissions. The paper highlights the capacity of green elements to act as natural air pollution mitigators and suggests better integration of strategic environmental management into urban planning to foster the development of healthier and more sustainable cities, providing recommendations to facilitate this objective. [ABSTRACT FROM AUTHOR]

Since 2010, the Chinese government has developed several batches of low-carbon pilot provinces and cities continuously. And in the backdrop of carbon peak and carbon neutralisation recently, the carbon emission reduction policies and achievements of China have attracted more and more worldwide attention. In the field of human health, this paper investigates whether the Low-carbon Province Pilot Policy of China has positive effects on residents' health based on the micro data of China Family Panel Studies (CFPS) from 2010 to 2016. Using the time-varying difference-in-difference model to solve the endogeneity problem commonly faced in the literature, we find that Low-carbon Province Pilot Policy has a positive impact on public health through air quality improvement and the unemployment level alleviation mainly. And in urban areas, the policy effect is more obvious than that in rural regions. This paper expands the content of national low-carbon governance effects to residents' health further and contribute to China's low-carbon development. Specifically, it reminds us to focus on the dynamic adjustment and effective continuation of the policy, and also the all-round support for 'the disadvantaged' in environmental governance process. [ABSTRACT FROM AUTHOR]

Background: Ambient air pollution can be harmful to the fetus even in countries with relatively low levels of pollution. Most of the established literature estimates the association between air pollution and health rather than causality. In this paper, I examine the causal effects of ambient air pollution on birth outcomes in Norway. Methods: With the large sample size and geographic division of sub-postal codes in Norway, I can control for a rich set of spatio-temporal fixed effects to overcome most of the endogeneity problems caused by the choice of residential area and date of delivery. After controlling for a rich set of spatio-temporal fixed effects, my paper uses the variance in ambient air pollutant concentrations over narrow time intervals and in a small geographic area of Norway to determine how prenatal air pollution exposure affects birth outcomes. My data contain extensive information about parents as well as meteorological conditions that can be used to control for potential confounding factors. Results: I find that prenatal exposure to ambient nitric oxide in the last trimester causes significant birth weight and birth length loss under the same sub-postcode fixed effects and calendar month fixed effects, whereas other ambient air pollutants such as nitrogen dioxide and sulfur dioxide appear to be at safe levels for the fetus in Norway. In addition, the marginal adverse effect of ambient nitric oxide is larger for newborns with disadvantaged parents. Both average concentrations of nitric oxide and occasional high concentration events can adversely affect birth outcomes. Conclusions: Prenatal exposure to NO pollution has an adverse effect on birth outcomes. This suggests that government and researchers should pay more attention to examining NO pollution and that health care providers need to advise pregnant women about the risks of air pollution during pregnancy. [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]

Abstract: This paper discusses the relationship between environmental and economic performance and the influence of corporate strategies with regard to sustainability and the environment. After formulating a theoretical model, results are reported from an empirical analysis of the European paper manufacturing industry. New data are used to test hypotheses derived from the theoretical model, using environmental performance indices representing different corporate environmental strategy orientations. In particular, an emissions-based index largely reflecting end-of-pipe strategies and an inputs-based index reflecting integrated pollution prevention are distinguished. For the emissions-based index, a predominantly negative relationship between environmental and economic performance is found, whereas for the inputs-based index no significant link is found. This is consistent with the theoretical model, which predicts the possibility of different relationships. The results also show that for firms with pollution prevention-oriented corporate environmental strategies, the relationship between environmental and economic performance is more positive, thus making improvements in corporate sustainability more likely. Based on this last insight, managerial implications of this are discussed with regard to strategy choices, investment decisions and operations management. [Copyright &y& Elsevier]

This study introduces an applicable colorimetric sensor array for the detection of pesticides in the vapor phase. The array consisted of six metal nanoparticles spotted on the piece of filter paper. 3D-origami pattern was used for the fabrication of a paper-based sensor to decrease the effect of the nanoparticles leaching after exposure to analytes. Exposure to pesticide aerosols caused changes in the color of the array due to the aggregation of nanoparticles. These changes provided selective responses to thion pesticides such as malathion, parathion, chlorpyrifos, and diazinon. The sensing assay could also differentiate between aliphatic and aromatic thions and discriminate amine-containing compounds from the other studied analytes. These finding results are clearly confirmed by both visual detection and multivariate statistical methods. The proposed sensor was successfully developed for the quantitative measurement of pesticide aerosols at a very low concentration. The limit of detection of this method determined for malathion, parathion, chlorpyrifos and diazinon were 58.0, 103.0, 81.0 and 117.0, respectively. Moreover, the array could be employed to simultaneously analyze four studied pesticides. The statistcal results confirmed that the method has high performance for concurrent detection of thions as a major air pollutant without the interference of other species. [ABSTRACT FROM AUTHOR]

The study of Masclaux et al., in this issue of the Annals of Occupational Hygiene, describes measurement of Staphylococcus bacteria in pig farms using classical viable measurement techniques and molecular techniques. Molecular techniques find application in hygiene field studies more often and create new opportunities. Common dust sampling approaches can be used in combination with molecular techniques and high numbers of samples can be processed creating opportunities for hygiene control and for exposure assessment in the context of epidemiological surveys. Hygienists should be aware of the advantages and disadvantages of the use of these techniques. Aspects of microbial sampling are briefly reviewed. [ABSTRACT FROM PUBLISHER]

According to the World Health Organization (WHO), PM2.5 and PM10 are a leading cause of air pollution and have been identified as having detrimental effects on human health even at low concentrations. By reducing exposure to these particles, countries can significantly decrease the incidence of both short- and long-term illnesses, as well as the overall burden of disease. This paper discusses the impact of the size and concentration of particles from braking systems, tires, and road on the respiratory, cardiovascular, and nervous systems of humans. The paper also presents methods for detecting and measuring non-exhaust emissions from motor vehicles, as well as United Nations Economic Commission for Europe (UNECE) regulations for defining standardised laboratory procedures for testing particle emissions resulting from the wear of brakes in light vehicles with a maximum permissible weight of up to 3500 kg. Based on the reviewed literature, possible mitigation measures to reduce fine particulate emissions are presented. In particular, an adaptation of an adequate braking process can significantly mitigate emissions and subsequently reduce harmful effects on human health and the environment. [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]

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]

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]

Atmospheric chemistry research has been growing rapidly in China in the last 25 years since the concept of the "air pollution complex" was first proposed by Professor Xiaoyan TANG in 1997. For papers published in 2021 on air pollution (only papers included in the Web of Science Core Collection database were considered), more than 24 000 papers were authored or co-authored by scientists working in China. In this paper, we review a limited number of representative and significant studies on atmospheric chemistry in China in the last few years, including studies on (1) sources and emission inventories, (2) atmospheric chemical processes, (3) interactions of air pollution with meteorology, weather and climate, (4) interactions between the biosphere and atmosphere, and (5) data assimilation. The intention was not to provide a complete review of all progress made in the last few years, but rather to serve as a starting point for learning more about atmospheric chemistry research in China. The advances reviewed in this paper have enabled a theoretical framework for the air pollution complex to be established, provided robust scientific support to highly successful air pollution control policies in China, and created great opportunities in education, training, and career development for many graduate students and young scientists. This paper further highlights that developing and low-income countries that are heavily affected by air pollution can benefit from these research advances, whilst at the same time acknowledging that many challenges and opportunities still remain in atmospheric chemistry research in China, to hopefully be addressed over the next few decades. [ABSTRACT FROM AUTHOR]