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Abstract: Burning joss paper, a common practice in temples in some Asian countries, can release toxic pollutants. This study investigated polychlorinated dibenzo-p-dioxin/dibenzofuran (PCDD/F) emissions and profiles from burning joss paper in a temple furnace connected to two wet scrubbers. The mean total PCDD/F content and corresponding toxic equivalent quantity (TEQ) in joss paper were 193 ng kg−1 and 0.645 ng I-TEQ kg−1, respectively, whereas those in bottom ash from burned joss paper were 18.5 ng kg−1 and 1.92 ng I-TEQ kg−1, respectively. The wet scrubbers decreased individual PCDD/F emissions by 26.7–71.0% and those of total PCDD/Fs and I-TEQ by 47.2% and 66.0%, respectively. The total PCDD/F TEQ emission factors before and after the wet scrubbers were 8.14 and 3.42 µg I-TEQ ton-feedstock−1, respectively. The estimated total PCDD/F and corresponding TEQ emissions were 5.29 g year−1 and 0.462 g I-TEQ year−1, respectively, in Taiwan. Burning joss paper in temple furnaces is a significant source of PCDD/F emissions. [Copyright &y& Elsevier]

One of the main global problems related to the environment is air pollution which not only affects the environment and the ecosystem but also affects human health. Many countries of the world did research on how to reduce air pollution so that the concentrations do not exceed the permissible thresholds which are set at that level so that neither human health nor the environment are exposed to further damage. One of the issues regarding air pollution is the concentrations of PM particles 2.5 and 10, PM particles are a big problem in developing countries such as the Republic of North Macedonia. The research paper was focused in processing the data that has been collected for daily averages of PM 2,5 and PM10 particles that contribute in air pollution. All analyzes and processing of the results are done in compliance with the current laws of the Republic of North Macedonia and with Directive 2008/50/EC, all for µg/m3. In the city of Skopje, numerous exceedances of the permissible thresholds of PM particles 2.5 and 10 were recorded at the two measurement stations where the results were obtained for the months of January, February, March, April, May and June of 2023. [ABSTRACT FROM AUTHOR]

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]

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

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

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

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

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

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

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

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

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

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

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

The State of Mexico is a Mexican entity facing socio-environmental problems: destruction of forest resources, water and air pollution, and poor quality of basic services. This paper analyzes its socio-urban metabolism between 2010 and 2020, considering it as a thermodynamic machine. Using Material Flow and Energy Flow Analysis (MFAE), indicators of consumption and emissions of electricity, fuel, water and social aspects (schooling, social security, economically active population and PBT) were obtained. Metabolic Indexes (MI) were calculated to indicate consumption by municipality. The results showed increases in: fuel consumption (275%), water (130%) and decrease in electricity consumption (-65.7%) due to costs, overcrowding and theft, indicating a governmental inability to provide services. On the social issue, the quality of education declined despite the decrease in the illiterate population. Access to social security rose (27%) in 2020, but public entities are bankrupt and 47.9% of the population is poor. The MI showed that the most consuming municipalities are those in the Toluca Valley and those in the suburbs of Mexico City. Despite the alignment of urban planning to the SDGs, they are not achieved due to methodologies that do not contemplate thermodynamic laws, socio-environmental complexity and, therefore, long-term sustainability. [ABSTRACT FROM AUTHOR]

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

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

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

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

Contaminated air is unhealthy for people to breathe and live in. To maintain the sustainability of clean air, air pollution must be analyzed and controlled, especially after unhealthy events. To do so, the characteristics of unhealthy events, namely intensity, duration, and severity are studied using multivariate modeling. In this study, the vine copula approach is selected to study the characteristics data. Vine copula is chosen here because it is more potent than the standard multivariate distributions, and multivariate copulas, especially in modeling the tails related to extreme events. Here, all nine different vine copulas are analyzed and compared based on model fitting and the comparison of models. In model fitting, the best model obtained is Rv123-Joint-MLE, a model with a root nodes sequence of 123, and optimized using the joint maximum likelihood. The components for the best model are the Tawn type 1 and Rotated Tawn type 1 180 degrees representing the pair copulas of (intensity, duration), and (intensity, severity), respectively, with the Survival Gumbel for the conditional pair copula of (duration, severity; intensity). Based on the best model, the tri-variate dependence structure of the intensity, duration, and severity relationship is positively correlated, skewed, and follows an asymmetric distribution. This indicates that the characteristic's, including intensity, duration, and severity, tend to increase together. Using comparison tests, the best model is significantly different from others, whereas only two models are quite similar. This shows that the best model is well-fitted, compared to most models. Overall, this paper highlights the capability of vine copula in modeling the asymmetric dependence structure of air pollution characteristics, where the obtained model has a better potential to become a tool to assess the risks of extreme events in future work. [ABSTRACT FROM AUTHOR]

Due to the rapid development of the economy, increasing level of urbanization, and industrialization, air pollution has transformed from the traditional coal-fired type to the composite pollution of coal-fired type and motor vehicle exhaust. With this transformation, the excessive release of many gases results in drastic climatic changes, global warming, ozone layer depletion, and more, which dramatically affect human lives in many sectors including sports. Based on the above, this research employs the Generalized Additive Model (GAM) to study and predict the effect of atmospheric pollution on the health of soccer players. First, the paper evaluates the impact of air pollution on the physical health of soccer players. It starts with a rigorous evaluation of the influence of various atmospheric pollutants on the health of athletes. Second, it investigates data analysis using numerous tests, including the Augmented Dickey–Fuller test, to evaluate the stationarity of the data and choose the most suitable models for further examination. Third, the research explores the complex correlations between meteorological factors and atmospheric pollutants by considering the dynamic interplay of environmental variables on health results. This analysis offers a nuanced understanding of how meteorological conditions affect pollutant concentrations and their potential health implications. Finally, the research constructs a prediction model that incorporates these complicated associations by employing GAMs with cubic spline functions to capture the non-linear interactions among multiple factors influencing soccer players' health. This advanced modeling technique offers a full examination of air pollution's complex effects, opening the way for useful findings in the disciplines of public health, environmental management, and sports medicine. The experimental results show that the proposed GAM outperforms the other models in terms of accuracy (85%), precision (88%), F1-Score (87%), consistency (90%), recall (86%), specificity (91%), and sensitivity (82%). These remarkable achievements highlight the importance of the proposed GAM in predicting the effect of air pollution on soccer players' health. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is a new revolution that integrates multiple technologies to improve our lifestyle and healthcare. With the use of this technology, football players may be safeguarded from health issues such as a cardiopulmonary respiratory system that could arise during a game or practice. This paper investigates the effect of inhalable particle pollution on the number of cardiovascular and respiratory diseases during outdoor football games using an advanced IoT-enabled approach. First, this research work investigates the negative impacts of inhalable air particles on the health of IoT-enabled outdoor football players. Second, it designs a sophisticated IoT-based monitoring system for real-time health evaluation, with a particular emphasis on the impact of inhalable particle pollution on the cardiovascular and respiratory systems of the participants. This proposed system is made up of three interconnected layers: the Sensory Layer, which is outfitted with lightweight IoT sensors and meticulously stationed for real-time monitoring of the players' physiological parameters and stadium environmental conditions; the Network Layer, which is in charge of facilitating seamless data flow between the sensors and the Cloud Layer; and the Cloud Layer, which is in charge of secure data storage, intricate analysis, and the extraction of invaluable information. Third, it employs Kriging estimation as a multi-step method that includes variogram modeling to analyze spatial data dependency, providing insights into how data points relate to distance and direction. Kriging calculates the difference between data points and generates spatial concentration maps that illustrate the geographical distributions of particle pollutants. Finally, it employs Linear Regression (LR) analysis to investigate the relationship between inhalable particle pollution and cardiopulmonary respiratory disease in outdoor football players. The data are dimensioned before comparison to produce the correlation coefficient, which reveals the relationship between these variables. The experimental result shows that in the single pollution model of inhalable particulate pollutant PM2.5, the relative risk values of cardiorespiratory diseases in different populations of outdoor football are 1.0022, 1.0019, 1.0024, 1.0025, and 1.0028, respectively. In CO, SO2, NO2, O3, and other multi-pollutant models, the concentration level of inhalable particulate pollutant PM2.5 increased by 10 μg/m3, and the number of visits for cardiorespiratory diseases in the whole population increased by 0.339% (95% CI 0.220–0.458) after the 10th day of outdoor football. In addition, the number of visits for cardiorespiratory diseases in men and women who lagged on the 10th day of outdoor football increased by 0.322% (95% CI 0.198–0.446) and 0.345% (95% CI 0.223–0.467), respectively. The number of visits for cardiorespiratory diseases of the elderly and children who lagged on the 10th day of outdoor football increased by 0.373% (95% CI 0.234–0.512) and 0.364% (95% CI 0.229–0.500), respectively. [ABSTRACT FROM AUTHOR]

To analyze the temporal variation characteristics of PM2.5 concentration and its influencing factors in Hefei, this paper utilizes mixed frequency data of air pollution and socio-economic development from 2013 to 2021, and constructs an adaptive change point detection model and fusion of Lightgbm in Machine Learning Models and LGBNN in multilayer neural networks to examine the change point characteristics and significant factors affecting PM2.5 concentration changes in Hefei. The findings indicate evident periodic oscillation patterns in PM2.5 concentration in Hefei, with a greater number of decreasing variables compared to rising variables in the sequence, and the change points exhibit a distinct "phased" characteristic. Regarding the influencing factors, the feature selection analysis conducted on multiple machine learning models like LGBNN reveals that policy factors exhibit the highest prominence, followed by social economy development, air pollution, and meteorological factors. [ABSTRACT FROM AUTHOR]

Empirical analyses have demonstrated that individuals exposed to severe air pollution in utero have worse health outcomes during childhood. However, there is little evidence on the long-term health impacts of air pollution exposure. The objective of this paper is to estimate the effect of in utero exposure to the Great London Smog of 1952 (GLS) on five health outcomes identified through a scoping review to be those most likely affected: respiratory, circulatory, neoplasms, mental health, and nervous system conditions. We use the GLS, an extreme air pollution event in December 1952, as a quasi-natural experiment to estimate the effect of exposure to air pollution in utero on adulthood health. Data from the UK Biobank is analysed for a cohort of participants born from December 1952 to July 1956. Differences in health outcomes between adults exposed and not exposed to the GLS due to their birth dates, born inside and outside London, were explored. Our primary focus is hospitalization events between 1997 and 2020 (corresponding to ages 40 to 69), as recorded in linked administrative data from the National Health Service (NHS). Specifically, the five primary outcomes are binary variables indicating that the individual had at least one hospitalization where the main cause of hospitalization is related to respiratory, circulatory, neoplasms, mental health, or nervous system conditions. The analytical sample comprised 36,281 individuals. A positive effect on adulthood hospitalizations due to respiratory conditions was observed. If exposed to the GLS in utero, the probability of at least one respiratory health-related hospitalization between 1997 and 2020 increased by 2.58 percentage points (95% CI 0.08, 4.30, p = 0.03), a 23% increase relative to the sample mean. Small effects were found for all other outcomes, suggesting that these conditions were not affected by the GLS. We do not find heterogeneous effects by sex or childhood socioeconomic status. This study found that a 5-day pollution exposure event while in utero significantly increased respiratory-related hospitalizations at ages 40 to 69 but had no impact on hospitalizations due to circulatory, neoplasms, mental health, and nervous system conditions. [ABSTRACT FROM AUTHOR]

It is observed that the exposure to environmental factors such as indoors and outdoors air pollution, cigarette smoke, and allergens are highly related to asthma attacks. It is also reported that limited exposure to asthma riggers, cure due to medicine, the attacks of asthma can be minimized. In this paper, we formulate the dynamics of asthma with smoking and environmental factors classes in the fractional Caputo-Fabrizio (CF) framework to visualize its dynamical behaviour. We delineate the important properties of the CF derivative for the analysis of our model. The model is then analyzed for the basic properties and the uniqueness and existence of the hypothesized asthma system are investigated via the theory of fixed point. Furthermore, a novel numerical scheme is presented for the solution of our fractional system to illustrate the time series of asthma model. The dynamical behaviour of our asthma model is then highlighted numerically to show the impact of fractional-order ϧ on the system and to visualize the role of input factors on the dynamics of asthma disease. [ABSTRACT FROM AUTHOR]

The authors explore the trends in the human health risks caused by air pollution and changing levels of weather and climate comfort on the territory of Russia in 2020–2050 under two climate change scenarios. Generally, the dynamics of risks is expected to be moderate. It will presumably be characterized by inter-scenario variability and dispersion by the country's climate zones. In certain areas, primarily in Siberia, very noticeable trends have been revealed. The paper shows a need for additional attention when planning the measures of adaptation to climate change in southern and central regions of European Russia, most of Western Siberia, the Magadan oblast and Kamchatka. [ABSTRACT FROM AUTHOR]

Combine harvesters (CHs) use large amounts of diesel fuel and they contribute significantly to greenhouse gas (GHG) emissions and air pollutants. A reduction in fuel consumption of agricultural machines has immediate economic benefits but can also results in longer term environmental benefits. Savickas et al. (Precis Agric 2023. https://doi.org/10.1007/s11119-023-10060-6) introduced extensive telematics data analysis and field tests for identifying the potential sustainable use of CHs. This paper follows on from that research and discusses the need for an IT tool for the management of economic and environmental indicators of a CH. The innovative tool allows the comparison of the data of specific agricultural machines with the multi-year telematics data stored in the database. It offers opportunities to reduce environmental impact and evaluate the results of field trials, presenting them in terms of environmental impact, expressed as CO2 equivalents. [ABSTRACT FROM AUTHOR]

Sustainable development and the creation of smart, green cities requires cooperation in many scientific fields, including those related to ecology, mobility, or sustainable management, among others. Environmental protection is a particularly important element here. Atmospheric pollution, due to air movements, spreads over very large areas; therefore, air quality monitoring is crucial to ensure protection from harmful substances. One of the most severe sources of air pollution, accounting for as much as approximately 25% of total annual emissions within the EU, is road transport. Therefore, the European Union has set an ambitious target to reduce total emissions to 55% for cars and 50% for vans by 2030. In recent years, unmanned aerial vehicles (UAVs) have become increasingly popular in many scientific fields, including environmental protection and photogrammetry. The use of UAVs to identify harmful pollutants allows them to gain an advantage over conventional detection methods, due to the possibility of remote (therefore safe for humans), faster, and area-based measurements. Given the ever-expanding scale of the use of this technology, this paper presents the possibilities of using UAVs to identify and visualize (map) pollution. The examples presented in the foreign literature, as well as our own research, in imaging the altitude distribution of air pollutants; gaseous pollutants: C6H6, HCHO, SO2; and particulate matter: PM1, PM2.5, PM10 demonstrate the validity of such measures. This research was carried out in the area of one of Poland's key A4 highways. The maps obtained allow for an area-wise and altitude-wise presentation of one of the significant air pollutants in the EU. In addition, they can be a valuable source of information for the implementation of future projects and the improvement of road infrastructure, thus contributing to the reduction of air pollution and the creation of so-called "green cities". [ABSTRACT FROM AUTHOR]

Electric vehicles (EVs) have emerged as a promising solution to address environmental concerns and transform the transportation sector. This paper examines the state-of-the-art EVs, encompassing their history, definition, and different types. The advantages and limitations of each type are compared and contrasted, providing insights into their unique characteristics and potential applications. Critical components of EVs are thoroughly analyzed. The paper explores various types of batteries, emphasizing their features and potential for improving energy density, range, and charging capabilities. Charging infrastructure is discussed, highlighting the importance of establishing a robust network of charging stations. The impact of EVs on power grids is examined, emphasizing grid integration, load management, capacity upgrades, and power quality. Challenges and barriers hindering the wide adoption of EVs are identified, including technological limitations, infrastructure constraints, economic considerations, consumer perception, and policies. The paper delves into the environmental and economic impacts of EVs, including lifecycle analysis, air pollution reduction, and cost-effectiveness. Thus, the paper provides valuable insights into the current state of EVs and their potential to shape the future. The findings highlight the need for collaborative efforts from industry stakeholders, governments, researchers, and consumers to overcome challenges and accelerate the transition to a sustainable transportation system. [ABSTRACT FROM AUTHOR]

We need energy. But what are potential solutions to achieving net zero? This paper presents a Christian perspective and overview of nuclear energy in the UK, and other countries, within the context of government policies to achieve net-zero emissions while ensuring national security. The growth of renewables is well-known but nuclear energy’s positive contribution is often unrecognised in our energy mix for producing electricity. Nuclear provides clean, low-carbon baseload electricity and has its place alongside variable renewables in tackling climate change. Besides large reactors, small modular reactors (SMRs) are being considered within energy strategies. Beyond electricity generation, future nuclear plants offer cogeneration. Furthermore, nuclear plants do not create the air pollution associated with fossil-fuel emissions. This paper encourages people to become more informed about the place of nuclear energy by looking at an evidence-based approach and encourages discussion in an open, honest, and respectful way about its merits in protecting people and our planet. [ABSTRACT FROM AUTHOR]

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]

Air quality has a significant impact on human health, in order to alleviate the air pollution and improve the ability to predict the air quality. In this paper, a prediction model of air quality index composed of variational mode decomposition and temporal convolutional network was proposed. First, in order to reduce the non-stationarity and randomness of the time series, the original air quality index sequence was decomposed by variational mode decomposition, and the decomposition number was determined by the central frequency method to decompose into multiple relatively stable sub-sequences with different frequency scales. Then, the decomposed sub-stable sequence was predicted by the time convolutional network. Finally, the prediction data were integrated and reconstructed to obtain the final prediction results. Comparing the results of other forecasting models by performance evaluation metrics, the combined forecasting model proposed in this paper reduced RMSE by 20.9%, 19.2%, 5.1%, 29.9%, 23.7% on the Beijing dataset. MAPE reduced by 26.6%, 22.3%, 19.5%, 28.9%, 15.0%, respectively. MAE decreased by 19.1%, 20.6%, 9.6%, 29.5%, 23.5%. R2 increased by 4.6%, 4.0%, 0.8%, 14.9%, 5.5% respectively. This proves the accuracy and reliability of the proposed model. [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]

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]

How atmospheric pollution is perceived by urban dwellers has long been a topic of interest within geography and the social sciences, whether to draw attention to environmental injustices, to better understand the materialities and affects associated with polluted air, or to grasp how people ‘tune in’ to polluted matter. In this paper, we draw on three interrelated geographical and social science literatures on polluted air to inform our exploration of how residents of an industrial town in the UK encounter and perceive localised ambient air pollution. Using creative methods, we explore residents' narrative accounts of everyday life in the town, revealing how their engagements with the matter of pollution over time are drawn from multiple registers, giving rise to a plurality of perceptions filled with tensions between near and far, and between past, present and future, producing an ambiguous atmosphere all of its own. The paper contributes to geographic explorations of urban atmospheres an understanding of how they are differently experienced and known, and how residential perceptions might persist or change over different timescales. [ABSTRACT FROM AUTHOR]

Introduction Recycling of domestic waste and a number of employees in the recycling industry is expected to increase. This study aims to quantify current exposure levels of inhalable dust, endotoxin, and microorganisms and to identify determinants of exposure among recycling workers. Methods This cross-sectional study included 170 full-shift measurements from 88 production workers and 14 administrative workers from 12 recycling companies in Denmark. The companies recycle domestic waste (sorting, shredding, and extracting materials from waste). We collected inhalable dust with personal samplers that were analysed for endotoxin (n = 170) and microorganisms (n = 101). Exposure levels of inhalable dust, endotoxin, and microorganisms and potential determinants of exposure were explored by mixed-effects models. Results The production workers were 7-fold or higher exposed to inhalable dust, endotoxin, bacteria, and fungi than the administrative workers. Among production workers recycling domestic waste, the geometric mean exposure level was 0.6 mg/m3 for inhalable dust, 10.7 endotoxin unit (EU)/m3 for endotoxin, 1.6 × 104 colony forming units (CFU)/m³ of bacteria, 4.4 × 104 CFU/m³ of fungi (25 °C), and 1.0 × 103 CFU/m³ of fungi (37 °C). Workers handling paper or cardboard had higher exposure levels than workers handling other waste fractions. The temperature did not affect exposure levels, although there was a tendency toward increased exposure to bacteria and fungi with higher temperatures. For inhalable dust and endotoxin, exposure levels during outdoor work were low compared to indoor work. For bacteria and fungi, indoor ventilation decreased exposure. The work task, waste fraction, temperature, location, mechanical ventilation, and the company size explained around half of the variance of levels of inhalable dust, endotoxin, bacteria, and fungi. Conclusion The production workers of the Danish recycling industry participating in this study had higher exposure levels of inhalable dust, endotoxin, bacteria, and fungi than the administrative workers. Exposure levels of inhalable dust and endotoxin among recycling workers in Denmark were generally below established or suggested occupational exposure limits (OEL). However, 43% to 58% of the individual measurements of bacteria and fungi were above the suggested OEL. The waste fraction was the most influential determinant for exposure, and the highest exposure levels were seen during handling paper or cardboard. Future studies should examine the relationship between exposure levels and health effects among workers recycling domestic waste. [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]

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]

Abstract: The paper deals with the application of size exclusion chromatography (SEC) for the studies of paper degradation phenomena. The goal is to solve some of the technical problems connected with the calibration of multi-detector SEC system and to find the correlation between SEC and viscometric results of degree of polymerization of cellulose. The results gathered for the paper samples degraded by acidic air pollutant (NO2) are used as an example of SEC–MALLS application. From the correlation between intrinsic viscosities and absolute value of molecular masses obtained with SEC/MALLS (Multi Angle Laser Light Scattering) technique, Mark-Houwink coefficients for cellulose in cupri-ethylenediamine solution were determined. Thus obtained coefficients were used for the determination of viscometric degree of polymerization (molecular mass) of the aged samples. An excellent correlation was found between the chromatographic values of molecular masses obtained with SEC–UV/VIS detection and the viscometric ones utilizing the improved values of Mark-Houwink coefficients. [ABSTRACT FROM AUTHOR]

Human exposure to particulate matter (PM) air pollution has been linked with respiratory, cardiovascular, and neurodegenerative diseases, in addition to various cancers. Consistent among all of these associations is the hypothesis that PM induces inflammation and oxidative stress in the affected tissue. Consequently, a variety of assays have been developed to quantify the oxidative activity of PM as a means to characterize its ability to induced oxidative stress. The vast majority of these assays rely on high-volume, fixed-location sampling methods due to limitations in assay sensitivity and detection limit. As a result, our understanding of how personal exposure contributes to the intake of oxidative air pollution is limited. To further this understanding, we present a microfluidic paper-based analytical device (μPAD) for measuring PM oxidative activity on filters collected by personal sampling. The μPAD is inexpensive to fabricate and provides fast and sensitive analysis of aerosol oxidative activity. The oxidative activity measurement is based on the dithiothreitol assay (DTT assay), uses colorimetric detection, and can be completed in the field within 30 mm following sample collection. The μPAD assay was validated against the traditional DTT assay using 13 extracted aerosol samples including urban aerosols, biomass burning PM, cigarette smoke, and incense smoke. The results showed no significant differences in DTT consumption rate measured by the two methods. To demonstrate the utility of the approach, personal samples were collected to estimate human exposures to PM from indoor air, outdoor air on a clean day, and outdoor air on a wildfire-impacted day in Fort Collins, CO. Filter samples collected on the wildfire day gave the highest oxidative activity on a mass normalized basis, whereas typical ambient background air showed the lowest oxidative activity. [ABSTRACT FROM AUTHOR]

All experiments were carried out in a prototype vortexing fluidized bed combustor (VFBC). The dimension of the combustion chamber is 0.7 x 1.4 x 2 m, and the freeboard section is 1 m i.d. and 4 m in height. Paper sludge was used as the feeding material. Two types of coal particles were employed as the supplementary fuel. In order to understand the characteristics of the VFBC system for paper sludge incineration, the effect of various operating parameters, such as the primary airflow, excess air ratio, and secondary airflow rates, on temperature distribution, ash elutriation, combustion efficiency, and pollutant emissions were investigated. [ABSTRACT FROM AUTHOR]

Combustion of spent vacuum residue hydrodesulfurization catalyst and incineration of paper sludge were carried out in thermo-gravimetric analyzer and an internally circulating fluidized-bed (ICFB) reactor. From the thermogravimetric analyzer-differential thermo-gravimetric curves, the pre-exponential factors and activation energies are determined at the divided temperature regions, and the thermo-gravimetric analysis patterns can be predicted by the kinetic equations. The effects of bed temperature, gas velocity in the draft tube and annulus, solid circulation rate, and waste feed rate on combustion efficiency of the wastes have been determined in an ICFB from the experiments and the model studies. The ICFB combustor exhibits uniform temperature distribution along the bed height with high combustion efficiency (<90%). The combustion efficiency increases with increasing reaction temperature, gas velocity in the annulus region, and solid circulation rate and decreases with increasing waste feed rate and gas velocity in the draft tube. The simulated data from the kinetic equation and the hydrodynamic models predict the experimental data reasonably well. [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]

Air pollution is a serious problem that affects economic development and people's health, so an efficient and accurate air quality prediction model would help to manage the air pollution problem. In this paper, we build a combined model to accurately predict the AQI based on real AQI data from four cities. First, we use an ARIMA model to fit the linear part of the data and a CNN-LSTM model to fit the non-linear part of the data to avoid the problem of blinding in the CNN-LSTM hyperparameter setting. Then, to avoid the blinding dilemma in the CNN-LSTM hyperparameter setting, we use the Dung Beetle Optimizer algorithm to find the hyperparameters of the CNN-LSTM model, determine the optimal hyperparameters, and check the accuracy of the model. Finally, we compare the proposed model with nine other widely used models. The experimental results show that the model proposed in this paper outperforms the comparison models in terms of root mean square error (RMSE), mean absolute error (MAE) and coefficient of determination (R2). The RMSE values for the four cities were 7.594, 14.94, 7.841 and 5.496; the MAE values were 5.285, 10.839, 5.12 and 3.77; and the R2 values were 0.989, 0.962, 0.953 and 0.953 respectively. [ABSTRACT FROM AUTHOR]

The great majority of workplace respirator efficacy studies have measured total inward leakage (TIL) for particulate contaminants. One of the first such studies, designated the Harris study, was conducted in the early 1970s in US underground coal mines. As in other particle-based studies, inside-the-facepiece dust sampling was continuously conducted across the inhalation and exhalation phases of the breathing cycle, although unlike in other studies, respirable dust cyclones were used in air sampling. Because exhaled air was partially depleted of dust particles due to deposition in the respiratory tract, the measured time-averaged dust concentration inside the facepiece underestimated the time-averaged dust concentration inspired into the facepiece. In turn, the reported TIL values underestimated the true TIL values experienced, which is to say, overestimated respirator efficacy. This paper describes a method to correct the Harris study's reported TIL values for respiratory tract deposition while accounting for particle size-selective sampling by the cyclone devices. Given the estimated coal mine particle size distribution outside the respirator, it is shown that the reported TIL values should be increased by the multiplicative factor 1.69. This paper also discusses the assigned protection factor (APF) of five for the quartermask respirator class and shows that 4/5 quartermasks in the Harris study did not meet the criterion for complying with this APF value when using the corrected TIL values. [ABSTRACT FROM AUTHOR]

Uncontrolled acetylene release during production processes, transportation, or storage can lead to explosions and detonations endangering safety of people and material assets. This paper investigates the impact of accidental release of acetylene gas in surrounding areas. The ALOHA software has been used in this paper to modelling of acetylene release. The modelling was performed for an accidental release of 2,000 kg acetylene from direct source for one minute. F or a typical average atmospheric condition in location, this accidental acetylene release would cause a red zone of 197 m (15,000 ppm) and yellow zone of 483 m (2,500 ppm) to downwind from the source. Inadequate storage can lead to accidental situations and negative impact on people and the environment. [ABSTRACT FROM AUTHOR]

The paper discusses modeling the dynamics of nickel concentration in soils, water, and the bottom sediments of lakes caused by atmospheric emissions from the Pechenganickel plant, Kola Peninsula, throughout its whole operation period. The applied technology of balanced identification makes it possible to use a mathematical description of heterogeneous geochemical processes in ecosystems to combine heterogeneous experimental data and build up a computer model with an optimal balance of its complexity and fitting quality of the data. The model is used to analyze the spatial and temporal variability of natural objects in the zone of distribution of atmospheric pollution (nickel) from the Pechenganickel plant. The paper presents and discusses results of this study, including estimates of the retrospective state of the simulated objects (before the start of the intense studies) and a forecast of their dynamics until 2030. According to the model calculations, the intensity of Ni accumulation in the soil and bottom sediments was 2.35 and 4.48 mg/(m2 year) during the maximum deposition periods (1980–2005), whereas the model predicts a decrease in the intensity of Ni accumulation in the bottom sediments (0.23 mg/(m2 year)) and slow Ni leaching from the soil (0.19 mg/(m2 year)) after the shutdown of the plant. [ABSTRACT FROM AUTHOR]

Atmospheric air can be polluted as a result of the ingress of various chemicals into it, which have a harmful effect on human health, causing an exacerbation of cardiovascular, airborne diseases, diseases of the gastrointestinal tract and the endocrine system, and exceeding the norms of dust content in the air leads to an increase in incidence of allergies and bronchial asthma. The relevance of the subject under study lies in the investigation of the sources of air pollution, as they lead to an increase in the degree of morbidity and mortality among the population. This, in turn, requires a radical change in the way people live, as well as the adoption of specific measures to ensure the reduction of air pollution. In this regard, the objective of this study was to perform a comprehensive analysis of the ecological and hygienic conditions of atmospheric air and evaluate its impact on human health due to pollution. The study incorporated empirical research methods such as empirical description and comparison to analyze emissions of pollutants, assess population morbidity rates, and conduct correlation and regression analyses. Official data from the State Committee of Uzbekistan on Statistics and scientific papers were utilized, alongside statistical analysis techniques and logical reasoning to investigate atmospheric air pollution and its impact on public health. The study analysed the indicators of emissions of pollutants into the atmosphere in the Republic of Uzbekistan as a whole and by regions. Furthermore, the paper provides a detailed analysis of the general morbidity of the population of the Republic of Uzbekistan and indicators of the incidence of cardiovascular diseases, based on which a correlation and regression analysis was performed. In addition, the morbidity rates of the population with diseases of the respiratory system and eye diseases throughout the Sirdaryo Region were analysed. The study revealed that Uzbekistan experiences high levels of environmental pollution, with a direct correlation found between atmospheric pollutants and the incidence of cardiovascular diseases, respiratory organ issues, and eye conditions. To protect the atmosphere, reducing pollutant emissions and implementing sustainable development principles, such as industrial modernization and the use of alternative energy sources, are necessary. The results highlight the importance of identifying pollutant types, their properties, and release conditions for effective pollution assessment. The practical significance of this study is to optimize the data of environmental monitoring of urban air pollution in the Republic of Uzbekistan, where in general there is a high level of pollutants in the atmosphere, which affects the health of the population. [ABSTRACT FROM AUTHOR]