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The consequences of sustainable and greener mobility have been demonstrated previous year by the pandemic situation. One significant benefit was to reduce the level of noise, dust and air pollution from the traffic in many European countries due to reduced mobility most of the population. Positive phenomena was also recorded in the Slovak Republic. Today's urban mobility offers new, more sustainable ways of transport for residents, especially app-based and shared-ride services, which have become increasingly widespread and very popular. Undoubtedly offer a level of convenience unseen before in the urban mobility systems throughout the world. Although new forms of transport are developed in most Slovak cities, their operation is not very rational and therefore individual car transport still dominates at the expense of sustainable ways of transport. In our paper, we analyze the traffic situation in the city of Nitra, as the large number of trips in the course of the peak-hours often results in severe traffic congestion. Multimodality is one of the solutions to improve the current traffic situation, as it allows urban residents to choose from a range of alternative travel options. The main aim of this paper is to determinate the possibilities of multimodal urban mobility in the city of Nitra in addition to analyze how the potential change in the variability of choice of means of transport affects the urban mobility behaviour. In order to meet stated objectives, a marketing research was conducted. The research findings point to the fact that the current urban infrastructure and overall possibilities are limited, the consequence of which is that travel behaviour of citizens does not demonstrate the features of sustainable urban mobility. Our results point to major differences in attitudes of Nitra´s residents from different urban and sub-urban areas. [ABSTRACT FROM AUTHOR]

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

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]

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

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]

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

Air pollution is worsening almost everywhere in the world. According to the Health Effects Institute (HEI), more than 95% of the world population breathe polluted air, toxic to their cardiovascular and respiratory health, which caused the death of 4.2 million people worldwide in 2016. As a result, the air pollution has become one of the leading causes of death worldwide. Therefore, an early cost-efficient warning system based on precise forecasting tools must be put in place to measure and avoid the harmful effects of exposure to the main air pollutants. Thus, it is essential to obtain reliable analytical information on air quality in a specific time and place. This paper focuses on monitoring air quality using a distributed intelligence which is a cost-efficient solution that enables a flexible prediction process distributed within a network of nodes and devices using a cross-platform solution. The suggested architecture enables collaborative learning along with collective knowledge graph building and knowledge sharing using the state of the art in Internet of Things, distributed machine learning, and ontologies. The proposed architecture suggests a flexible prediction system personalized for each node based on its need of information. Similar nodes get together for collective learning which allows for resource optimization, knowledge reusability, and device interoperability. The paper describes the modeling framework of distributed intelligence monitoring and analysis system designed for urban regions. [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]

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 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 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]

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]

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]

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]

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]

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]

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 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]

The prevalence and mobility of smartphones make these a widely used tool for environmental health research. However, their potential for determining aggregated air quality index (AQI) based on PM2.5 concentration in specific locations remains largely unexplored in the existing literature. In this paper, we thoroughly examine the challenges associated with predicting location-specific PM2.5 concentration using images taken with smartphone cameras. The focus of our study is on Dhaka, the capital of Bangladesh, due to its significant air pollution levels and the large population exposed to it. Our research involves the development of a Deep Convolutional Neural Network (DCNN), which we train using over a thousand outdoor images taken and annotated. These photos are captured at various locations in Dhaka, and their labels are based on PM2.5 concentration data obtained from the local US consulate, calculated using the NowCast algorithm. Through supervised learning, our model establishes a correlation index during training, enhancing its ability to function as a Picture-based Predictor of PM2.5 Concentration (PPPC). This enables the algorithm to calculate an equivalent daily averaged AQI index from a smartphone image. Unlike, popular overly parameterized models, our model shows resource efficiency since it uses fewer parameters. Furthermore, test results indicate that our model outperforms popular models like ViT and INN, as well as popular CNN-based models such as VGG19, ResNet50, and MobileNetV2, in predicting location-specific PM2.5 concentration. Our dataset is the first publicly available collection that includes atmospheric images and corresponding PM2.5 measurements from Dhaka. Our codes and dataset are available at https://github.com/lepotatoguy/aqi. [ABSTRACT FROM AUTHOR]

This paper aims to improve and extend the improved spatial Moran's I theory by analyzing multi-observation samples. By constructing an expanded spatial weight matrix, a vector definition of the improved spatial Moran's I is given. In order to improve the judgment basis of the improved spatial Moran's I, the range of the improved spatial Moran's I is derived using the non-negativity of variance. Since the improved Moran's I is only applicable to the analysis of a single variable with unknown distribution, a Moran's I matrix suitable for analyzing the spatial autocorrelation of multiple variables is proposed. The distribution of the elements of the Moran's I matrix is studied by Monte Carlo simulation. The simulation results show that only the elements on the non-main diagonal follow a normal distribution when the sample size is small. Any element follows a normal distribution when the sample size is large. Then it is proved that the Moran's I matrix follows a Wishart distribution when the spatial weight matrix is a positive definite matrix. Finally, several comprehensive evaluation indicators suitable for the theory of multivariate spatial autocorrelation are proposed based on the algebraic meaning of the Moran's I matrix. Spatial autocorrelation analysis is carried out in combination with multi-dimensional air pollution data. [ABSTRACT FROM AUTHOR]

Atmospheric Boundary Layer (ABL) height variability, along with meteorological parameters and pollutant concentrations, are studied during different months in Mumbai region for the period of November 2020 to October 2021. ABL height is measured using a monostatic SODAR (SOnic Detection And Ranging) system. The capabilities, behaviour, and benefits of employing this ground-based remote sensing system in Mumbai are examined in this preliminary report after a year of SODAR monitoring. SODAR dataset is also compared with Radiosonde input ERA5 reanalysis data. The ventilation coefficient (VC) analysis for the same time period is also covered in the paper. Especially in the months of November, December, January, February, August, September, and October, the ABL height often approached 700 m. The ABL height is increased to 1000–1200 m in April because of the strong winds and warm weather. The month of April has the greatest monthly average height (418 m), while the month of February has the lowest monthly average height (179 m). In April, when the wind speed is 7.2 m/s, the convection duration is observed to be the longest, while in December, when the wind speed is 1.75 m/s, it is the shortest. Research highlights: For the first time, real-time hourly atmospheric boundary layer height data measured from SODAR system are analysed over the Mumbai region. The ABL height often reached 700 m, especially in the months of November, December, January, February, August, September, and October. Due to the high wind and warmth in April, the ABL height is extended to 1000–1200 m. The highest ventilation coefficient values around 7095 m2/s are recorded in the month of April, while the lowest values around 2912 m2/s are recorded in the months of November and December. [ABSTRACT FROM AUTHOR]

Air pollution is a leading cause of human diseases. Accurate air quality predictions are critical to human health. However, it is difficult to extract spatiotemporal features among complex spatiotemporal dependencies effectively. Most existing methods focus on constructing multiple spatial dependencies and ignore the systematic analysis of spatial dependencies. We found that besides spatial proximity stations, functional similarity stations, and temporal pattern similarity stations, the shared spatial dependencies also exist in the complete spatial dependencies. In this paper, we propose a novel deep learning model, the spatiotemporal adaptive attention graph convolution model, for city-level air quality prediction, in which the prediction of future short-term series of PM2.5 readings is preferred. Specifically, we encode multiple spatiotemporal dependencies and construct complete spatiotemporal interactions between stations using station-level attention. Among them, we design a Bi-level sharing strategy to extract shared inter-station relationship features between certain stations efficiently. Then we extract multiple spatiotemporal features with multiple decoders, which it is extracted from the complete spatial dependencies between stations. Finally, we fuse multiple spatiotemporal features with a gating mechanism for multi-step predictions. Our model achieves state-of-the-art experimental results in several real-world datasets. [ABSTRACT FROM AUTHOR]

Reducing air pollution is a major policy challenge, especially in densely populated urban areas where human exposure to emissions is considerable. This paper develops and examines a series of scenarios for the evolution of transport-related emissions in the area of Santiago, Chile. The study spans a time window up to 2050 and focuses on seven air pollutants and CO2. It compares a reference scenario with policy counterfactual scenarios involving a rapid electrification of public buses, a carbon pricing scheme and a kilometer charge differentiated by vehicle type. The reference scenario predicts a 55–80% reduction in air pollutants emitted by urban transport systems, by 2050. The corresponding reduction of CO2 lies at 6%, highlighting the asymmetric evolution in tailpipe filtering and carbon capture technologies. The analysis suggests that ramping up the efforts to electrify the bus fleet may eliminate 25% of the CO2 and at least 10% of the remaining air pollutant emissions in 2050. These figures increase to 45 and 30%, respectively, if rapid electrification is accompanied by one of the two tax schemes. With a distinct program to recycle the revenue from the tax schemes, the policy reform can be welfare improving for all groups and its environmental objectives can be achieved without adverse distributional consequences. The paper highlights the synergies of policies curbing climate change and tacking air pollution from the viewpoint of urban transport. [ABSTRACT FROM AUTHOR]

In recent years, China has implemented several measures to improve air quality. The Beijing-Tianjin-Hebei (BTH) region is one area that has suffered from the most serious air pollution in China and has undergone huge changes in air quality in the past few years. How to scientifically assess these change processes remain the key issue in further improving the air quality over this region in the future. To evaluate the changes in major air pollutant emissions over this region, this paper employs ensemble Kalman filtering (EnKF) for integrating the national ground monitoring pollutant observation data and the Nested Air Quality Prediction Modeling System (NAQPMS) simulation data to inversely estimate the emission rates of SO2, NOX, CO, and primary PM2.5 over BTH region in February from 2014 to 2019. The results show that SO2, NOX, CO, and primary PM2.5 emissions in the BTH region decreased in February from 2014 to 2019 by 83%, 37%, 41%, and 42%, while decreases in Beijing during this period were 86%, 67%, 59%, and 65%, respectively. Compared with the prior emission inventory, the inversion emission inventory reduces the uncertainty of multi-pollutant simulation in the BTH region, with simulated root mean square errors of the monthly average concentrations of SO2, NOX, PM2.5, and CO reduced by 41%, 30%, 31%, and 22%, respectively. The average uncertainties of SO2, NOX, PM2.5, and CO inversion emissions in 2014–19 are ±14.03% yr−1, ±28.91% yr−1, ±126.15% yr−1, and ±43.58% yr−1. Compared with the uncertainty of MEIC emission, the uncertainties of all species changed by +2% yr−1, −2% yr−1, −26% yr−1, and −4% yr−1, respectively. The spatial distribution results illustrate that air pollutant emissions are mainly distributed over the eastern and southern BTH regions. The spatial gap between the inversion emissions and MEIC emissions was further closed in 2019 compared to 2014. The results of this paper can provide a new reference for assessing changes in air pollution emissions over the BTH region in recent years and validating a bottom-up emission inventory. [ABSTRACT FROM AUTHOR]

This paper investigates externalities generated by air transportation pollution on health. As a source of exogenous variation, we use an unannounced 5-month trial that reallocated early morning aircraft landings at London Heathrow Airport. Our measure of health is prescribed medications spending on conditions known to be aggravated by pollution, especially sleep disturbances. We observe a significant and substantial decrease in prescribed drugs for respiratory and central nervous system disorders in the areas subjected to reduced air travel between 4:30 am and 6.00 am compared with the control regions. Our findings suggest a causal influence of aviation on health conditions. [ABSTRACT FROM AUTHOR]

In the context of the severe environmental pollution, it is of significant academic and practical value to study the environmental protection behavior of individuals. This paper uses the ordinary least squares method (OLS) and utilizes the data of Chinese General Social Survey (CGSS2013) and the air pollution monitoring data provided by Columbia University’s International Earth Science Information Network Center (CIESIN), the findings demonstrate a positive and significant impact of air pollution on environmentally friendly behavior. Furthermore, the stepwise regression method reveals that environmental knowledge is a crucial mediating variable in the relationship between air pollution and environmental behavior. Heterogeneity analysis reveals that age, income, gender, health status, and place of residence significantly impact environmentally friendly behavior. The results remain robust even after controlling for urban fixed effects. The implications of this research suggest that it is opportune to leverage adverse macro events such as regional environmental pollution to promote and enhance public environmental awareness and behaviors, especially for those who are susceptible to environmental pollution. This is crucial for promoting public environmental behaviors and ultimately building an environment-friendly society. [ABSTRACT FROM AUTHOR]

Forecasting time series data is a big challenge due to the temporal and multivariate dependencies in the data. In this paper, we present a new approach named as TPPM25 (Transformer-based Prediction of PM 2.5 ) for forecasting PM 2.5 , a key air quality indicator. It is based on the state-of-the-art Transformer neural network and various data embedding techniques. By performing attention calculations among features over time steps, TPPM25 mimics cognitive attention and selectively enhances essential parts of the input data while diminishing other parts. TPPM25 is able to effectively capture temporal relations to multiple influencing meteorological features. Experiments demonstrate its effectiveness by comparing with a cutting-edge ensemble deep learning model from Zhang et al. (Inf Sci 544:427–445, 2021). Our TPPM25 model outperforms Zhang et al.'s model under the same experimental setting on a well-researched benchmark dataset. As Zhang et al.'s model is restricted to univariate PM 2.5 prediction, our TPPM25 model bypasses this restriction and further improves the prediction accuracy when considering more influencing meteorological features. Moreover, our TPPM25 model is able to maintain high prediction accuracy over longer periods of time as compared to the Long-Short Term Memory (LSTM) and Bidirectional LSTM models. [ABSTRACT FROM AUTHOR]

Severe air pollution poses a significant threat to public safety and human health. Predicting future air quality conditions is crucial for implementing pollution control measures and guiding residents' activity choices. However, traditional single-module machine learning models suffer from long training times and low prediction accuracy. To improve the accuracy of air quality forecasting, this paper proposes a ISSA–LSTM model-based approach for predicting the air quality index (AQI). The model consists of three main components: random forest (RF) and mRMR, improved sparrow search algorithm (ISSA), and long short-term memory network (LSTM). Firstly, RF–mRMR is used to select the influential variables affecting AQI, thereby enhancing the model's performance. Next, ISSA algorithm is employed to optimize the hyperparameters of LSTM, further improving the model's performance. Finally, LSTM model is utilized to predict AQI concentrations. Through comparative experiments, it is demonstrated that the ISSA–LSTM model outperforms other models in terms of RMSE and R2, exhibiting higher prediction accuracy. The model's predictive performance is validated across different time steps, demonstrating minimal prediction errors. Therefore, the ISSA–LSTM model is a viable and effective approach for accurately predicting AQI. [ABSTRACT FROM AUTHOR]

Advances in air monitoring methods have made it possible to analyze large-scale air pollution phenomena. Mining potential air pollution information from large-scale air pollution data is an important issue in the current environmental field. Although direct data visualization provides an intuitive presentation, the method is less applicable in long-time domains with high temporal resolution. To better meet the analysis needs of domain experts, we design a visual analysis framework based on friendly multi-view interactions and novel visual view designs. This framework can explore the spatiotemporal dynamics of multiple pollution data. In this paper, a two-stage cluster analysis method is proposed to extract possible transport patterns from large-scale pollutant transport trajectories. This method will be substantially helpful for domain experts to make relevant decisions. At the same time, the index is constructed from long-time series data at the grid point in the specific transport trajectories. This structure can help experts complete the sketch match with custom time resolution. It can assist domain experts in extracting key possible time-varying features. Finally, we verified the validity through spatial and temporal case analysis for pollutant data. [ABSTRACT FROM AUTHOR]

It is shown that distribution of PM2.5 concentration recorded by eight air quality monitoring stations during 2021, covering a large part of the Santiago metropolitan region of Chile, can be explained by a q-Weibull function, which has been related in some papers to complex and non-equilibrium statistical systems. It was found that this function has an excellent fit performance on non-filtered data, which cannot be reached by other physical-statistical functions widely used to modeling air pollution distribution in the literature. Finally, relevant interpretations from the fit parameters are shown as well. [ABSTRACT FROM AUTHOR]

To review and discuss recent findings on the associations between pediatric/early-life exposures to ambient air pollution and the risk of pediatric-onset inflammatory bowel diseases (IBD). A scoping review was conducted using the Peters Micah et al. framework. We searched, selected, extracted, and reviewed information from published peer-reviewed papers from three bibliographic databases, chosen to cover a broad range of disciplines. Limits on date (last decade), language, and subject were placed on the database search. The search identified 109 papers from 2010 to June 2021. After screening, we identified nine articles with data on air pollution as a risk factor for IBD, but only four epidemiologic studies directly investigated the association between air pollution and IBD development in children and young adults. These four papers show that air pollution components have different associations with pediatric IBD (pIBD) incidence. Consequently, sulfur dioxide (SO2), nitrogen dioxide (NO2), and the oxidant capacity of air pollution (Ox) were positively associated with pIBD incidence, whereas the association effects of particulate matter (PM) and ozone (O3) exposures were not clear. Despite good scientific rationale and some studies, the evidence on the role that air pollution has in IBD development is limited, highlighting the need for further investigation. Future studies should include the epidemiology of air pollutants and its sources, identifying and understanding mechanisms linking air pollution and pIBD, and identifying signatures of biological responses to air pollutants. [ABSTRACT FROM AUTHOR]

Satellite-derived measurements are negatively impacted by cloud cover and surface reflectivity. These biases must be discarded and significantly increase the amount of missing data within remote sensing images. This paper expands the application of a partial convolutional neural network (PCNN) to incorporate depthwise convolution layers, conferring temporal dimensionality to the imputation process. The addition of a temporal dimension to the imputation process adds a state of successive existence within the dataset, which spatial imputation cannot capture. The depthwise convolution process enables the PCNN to independently convolve the data for each channel. The deep learning system is trained with the Community Multiscale Air Quality model-simulated tropospheric column density of Nitrogen Dioxide (TCDNO2) to impute TROPOspheric Monitoring Instrument TCDNO2. The depthwise PCNN model achieves an index of agreement of 0.88 and outperforms the default PCNN models, with and without temporal dimensionality of data, and conventional data imputation methods such as inverse distance weighting by 4–7 and 10–15% in the index of agreement and correlation, respectively. The model demonstrates more consistency in the reconstruction of TROPOspheric Monitoring Instrument tropospheric column density of NO2 images. The model has also demonstrated the accurate imputation of remote sensing images with over 95% of the data missing. PCNN enables the accurate imputation of remote sensing data with large regions of missing data and will benefit future researchers conducting data assimilation for numerical models, emission studies, and human health impact analyses from air pollution. [ABSTRACT FROM AUTHOR]

The weekend effect, i.e., the concentration of air pollutants is different from weekends to weekdays, has been explored since the 1970 s. In most studies, the weekend effect is referred to the change of O 3 , i.e., lower emission of NO x on weekends leads to a higher concentration of O 3 . Determining whether it is true can provide insight into the strategy of air pollution controlling. In this study, we explore the weekly cycle of cities of China based on the weekly cycle anomaly (WCA), which is proposed in this paper. The advantage of using WCA is that we can avoid the influence of other change components such as the daily cycle and the seasonal cycle. The p values of the significant tests from all cities are analyzed for obtaining a whole picture of the weekly cycle of air pollution. The result shows that the concept of the weekend effect is not proper for cities in China, as many cities reach the valley phase of the emission on weekdays but not weekends. Thus, researches should not pre-assume that the weekend is the low emission scenario. We focus on the anomaly of O 3 on the peak and the valley of the emission scenario estimated by the NO 2 concentration. Through the analysis of the distribution of the p values of all cities, we show that almost all cities in China have weekly cycle of O 3 corresponding to the weekly cycle emission of NO x , i.e., O 3 of the peak time of NO 2 is less than valley time. The cities of the strong weekly cycle are located in four regions, i.e., the Beijing-Tianjing-Hebei region, the Shandong Peninsula Delta, the Yangtze River Delta, and the Pearl River Delta, and these regions are also the regions with relatively severe pollution levels. [ABSTRACT FROM AUTHOR]

Exposure to ambient air pollution is a global health burden, and assessing its relationships to health effects requires predicting concentrations of ambient pollution over time and space. We propose a spatiotemporal penalized regression model that provides high predictive accuracy and greater computation speed than competing approaches. This model uses overfitting and time-smoothing penalties to provide accurate predictions when there are large amounts of temporal missingness in the data. When compared to spatial-only and spatiotemporal universal kriging models in simulations, our model performs similarly under most conditions and can outperform the others when temporal missingness in the data is high. As the number of spatial locations in a data set increases, the computation time of our penalized regression model is more scalable than either of the compared methods. We demonstrate our model using total particulate matter mass ( PM 2.5 and PM 10 ) and using sulfate and silicon component concentrations. For total mass, our model has lower cross-validated RMSE than the spatial-only universal kriging method, but not the spatiotemporal version. For the component concentrations, which are less frequently observed, our model outperforms both of the other approaches, showing 15% and 13% improvements over the spatiotemporal universal kriging method for sulfate and silicon. The computational speed of our model also allows for the use of nonparametric bootstrap for measurement error correction, a valuable tool in two-stage health effects models. Supplementary materials accompanying this paper appear online. [ABSTRACT FROM AUTHOR]

Mountain glaciers are of immense importance for the balanced natural ecosystem. Billions of human life depend on this natural reserve. However, there is an alarming situation worldwide to save this resource. The melting rate of glaciers is a matter of serious concern and one of the driving forces is air pollution. Many research studies have suggested that the deposition of aerosol particles is closely associated with the intensification of glacier retreat. We are witnessing the imperative necessity to understand the regional role of aerosol pollutants in glaciers melting. The third largest glacier occupier, the Himalayan mountain glacier has also unexceptionally countersigned many calamities such as glacial lake outburst floods, flash floods, landslides and biodiversity loss. Since the geography, climate, population, human activities, culture and pollution patterns are divergent at different mountain glaciers, thus regional studies should be encouraged to understand the role of pollutants on glaciers. On-site monitoring requires studying the regional aerosols behavior in mountain glaciers specially the most sensitive regions such as Sikkim Himalaya region. Sikkim Himalaya region has experienced a rise in temperature and an increase in glacial lakes number as well as area. This review paper suggests that the in-situ study of aerosols in ecologically sensitive mountain glaciers region is our utmost need in the current research time. As many glaciers are on the verge of disappearing and different types of aerosols deposition blamed for it at different places. The regional aerosol information may help in saving this precious natural reserve. [ABSTRACT FROM AUTHOR]

Purpose: Understanding the relationship between an environmental determinant and a given health outcome is key to inform public health policies. The short-term mortality and morbidity responses to outdoor air pollutants are traditionally assessed as a log-linear relationship, but few studies suggest a possible deviation from linearity. This paper investigates the shape of the relationship between ozone, NO2 and fine particulate matter (PM10 and PM2.5), mortality and hospital admissions in 18 French cities between 2000 and 2017. Method: A multi-centric time series design, using quasi-Poisson generalized additive models, was used. Four approaches were compared to model concentration–response curves (log-linear, piecewise-linear with a priori defined breakpoints, piecewise-linear with no a priori breakpoint and cubic spline). Results: All the models indicated evidence of supra-linearity between PM10, PM2.5, NO2, mortality and hospital admissions. For instance, with a log-linear model, a 10 µg/m3 increase in PM2.5 was associated with a 0.4% [95% CI 0.2; 0.7] increase in non-accidental mortality. When using a piecewise model with a priori set breakpoint at 10 µg/m3, the mortality increase was 3.8% [4.4; 6.3] below 10 µg/m3, and 0.3% [0; 0.6] above. Non-significant impacts of ozone were found for concentrations below 90 µg/m3 to 120 µg/m3, with some variability in the identified threshold across the heath indicator studied. Conclusion: The supra-linearity of the relationship between PM10, PM2.5, NO2, mortality and hospital admissions supports the need to further reduce air pollution concentrations well below regulatory values. [ABSTRACT FROM AUTHOR]

Air pollution is one of the significant environmental issues. It can cause adverse health effects such as cancer, cardiovascular diseases, and high mortality rates. High population density is a huge contributory factor of air pollution in cities and urbanized areas. Other sources of air pollution are transport, local heating, and possibly a pollution transfer from neighboring industrial regions. Information about the opening and closing of industrial plants, stubble burning, and fireworks can be considered an added value to this work. In recent past years, among the Delhi/National Capital Region. The pollution level has been raised multiple times during November and March months. The most significant root cause of this trouble is stubble-burning air pollution in the neighboring cities like Punjab and Haryana. Another concern is the burning of fireworks in these months due to Diwali and other festivals in the cities. This research paper aims to perform a data-based analysis on the PM2.5 and PM10 concentration levels of the past six years month-wise from the authenticated sources to find out the causes of extreme air pollution levels. This research paper deals mainly with long-term time series, including air pollutants PM2.5, PM10, and other meteorological variables. It models and analyzes the PM2.5 and PM10 values for 2014–2018. The present research work also deals with the facts and methods to handle the high air pollution rate in Delhi/NCR caused by stubble burning and analysis the effect on the environment by the call of Honorable Prime Minister of India Shri Narender Modi Ji for the illusion of candles as a symbolic fight against COVID 19. [ABSTRACT FROM AUTHOR]

Air quality index (AQI) is an essential measure of air pollution evaluation, which describes the air pollution degree and its impact on health, so the accurate prediction of AQI is significant. This paper presents an AQI prediction model based on Convolution Neural Networks (CNN) and Improved Long Short-Term Memory (ILSTM), named CNN-ILSTM. ILSTM deletes the output gate in LSTM and improves its input gate and forget gate, and introduces a Conversion Information Module (CIM) to prevent supersaturation in the learning process. ILSTM realizes efficient learning of historical data, improves prediction accuracy, and reduces the training time. CNN extracts the eigenvalues of input data effectively. This paper uses air quality data from 00:00 on January 1, 2017, to 23:00 on June 30, 2021, in Shijiazhuang City, Hebei Province, China, as experimental data sets, and compares this model with eight prediction models: SVR, RFR, MLP, LSTM, GRU, ILSTM, CNN-LSTM, and CNN-GRU to prove the validity and accuracy of CNN-ILSTM prediction model. The experimental results show the MAE of CNN-ILSTM is 8.4134, MSE is 202.1923, R2 is 0.9601, and the training time is 85.3 s. In this experiment, the performance of this model performs better than other models. [ABSTRACT FROM AUTHOR]

Air pollution exposure has been related to negative gross motor development in children. However, there is currently a lack of conclusive evidence for such a relationship. We carried out a systematic review and meta-analysis using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) criteria to examine whether exposure to air pollution has an impact on children's gross motor development. Of the 9746 papers found, 7 studies examined the impact of air pollutant characteristics, such as PM2.5, PM10, and NO2, on children's gross motor development. The results of the study reveal a significant association between air pollutants and an increased likelihood of negative gross motor development. PM10 was discovered to be considerably riskier for children's gross motor development (effect: − 1.83, 95% CI: − 3.04, − 0.62, p value = 0.002). Additionally, NO2 exhibited indications of a tendency to be connected to a detrimental impact on children's gross motor development (effect − 0.18, 95% CI: − 0.42, 0.07, p value = 0.097). Conclusion: Our study indicates that exposure to PM10, PM2.5, and NO2, especially PM2.5 and PM10, is negatively associated with children's gross motor development. However, further research is required to determine how exposure to prenatal air pollution affects children's gross motor development. What is Known - What is New: • In this study, we provide an overview of emerging data related to PM10, PM2.5, and NO2 exposure in child development, especially on the gross motor function that continues to emerge, and key findings are highlighted. • Additionally, we summarize the evidence on the underlying effect of air pollution on gross motor function from human studies.. • Overall, we emphasize that evidence from human studies is critical in suggesting detrimental child health outcomes of an action to promote preventive strategies that will effectively protect children's health.. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of deciding the locations of dry ports by providing an intermodal rail-road p-hub median model, adopting a bi-level programming approach. In the proposed model, direct transportation and shipment between nodes are allowed instead of transportation merely through the hubs. In the bi-level programming approach, at the top level, the government/authority will decide the locations of the dry ports to increase the utilization of railways and minimize the construction and maintenance costs of dry ports as one of the important transportation infrastructures. Freight forwarders who are considered at the lower level aim to minimize the shipping costs by deciding the optimal shipping routes. A matheuristic approach based on the Genetic Algorithm (GA) is proposed to solve the given problem. Numerical analysis confirms that the proposed algorithm can provide satisfactory solutions for large instances where commercial solvers are not capable of finding the near optimal solutions in a reasonable computational time. Finally, the experimental results show that using the proposed transportation network model can decrease the total transportation costs along with significantly reduction of air pollution. [ABSTRACT FROM AUTHOR]

Cars are dominating urban traffic in cities around the world, even though daily trips in many cities are often realized with active modes of transportation or public transport. Urban transport planning processes need to adapt to this reality and the necessity of climate change mitigation. Against this background, the research project "Mobility Reporting", a joint undertaking of the district Pankow in Berlin and researchers from TU Berlin and TU Dresden, established a new, goal-driven, and participative planning process. The process identified local mobility as one of the central planning goals. The 15-min city (FMC) was thus adduced as a benchmark to analyze the district's current mobility system and development potential. We conducted extensive accessibility analyses to examine the status quo concerning the FMC. We calculated travel times to essential destinations in daily life by foot, public transport, and car. This analysis was accompanied by a mixed online and paper–pencil survey conducted to evaluate the perceived accessibility of people in Pankow. The survey results shed light on the question of which walking time thresholds constitute a "very good" or "good" accessibility. Further analyses included environmental and social variables, allowing us to check whether areas with different accessibility levels also differ regarding the socio-economic characteristics of their inhabitants. For example, do socially advantaged neighborhoods have better local accessibility? Is there a trade-off between exposure to environmental pollution and good accessibility? With this contribution, we shed light on what an FMC is and ought to be. Results from the survey support the normative and political vision of the FMC. Pankow generally offers the merits of a walkable city, showing the expected travel time differences between the dense inner city and the outskirts. Socially disadvantaged neighborhoods are not consistently less accessible. However, there seems to be a trade-off between good accessibility (especially PT accessibility) and correlated externalities of transport, namely air pollution and noise. [ABSTRACT FROM AUTHOR]

Numerous studies have examined the associations between long-term exposure to fine particulate matter (PM 2.5 ) and adverse health outcomes. Recently, many of these studies have begun to employ high-resolution predicted PM 2.5 concentrations, which are subject to measurement error. Previous approaches for exposure measurement error correction have either been applied in non-causal settings or have only considered a categorical exposure. Moreover, most procedures have failed to account for uncertainty induced by error correction when fitting an exposure response function (ERF). To remedy these deficiencies, we develop a multiple imputation framework that combines regression calibration and Bayesian techniques to estimate a causal ERF. We demonstrate how the output of the measurement error correction steps can be seamlessly integrated into a Bayesian additive regression trees (BART) estimator of the causal ERF. We also demonstrate how kernel-weighted smoothing of the posterior samples from BART can be used to create a more accurate ERF estimate. Our proposed approach also properly propagates the exposure measurement error uncertainty to yield accurate standard error estimates. We assess the robustness of our proposed approach in an extensive simulation study. We then apply our methodology to estimate the effects of PM 2.5 on all-cause mortality among Medicare enrollees in New England from 2000 to 2012. Supplementary materials accompanying this paper appear on-line [ABSTRACT FROM AUTHOR]

The numerical treatment of an atmospheric chemical scheme, which contains 56 species, is discussed in this paper. This scheme is often used in studies of air pollution levels in different domains, as, for example, in Europe, by large-scale environmental models containing additionally two other important physical processes—transport of pollutants in the atmosphere (advection) and diffusion phenomena. We shall concentrate our attention on the efficient numerical treatment of the chemical scheme by using Implicit Runge–Kutta Methods combined with accurate and efficient advanced versions of the Richardson Extrapolation. A Variable Stepsize Variable Formula Method is developed in order to achieve high accuracy of the calculated results within a reasonable computational time. Reliable estimations of the computational errors when the proposed numerical methods are used in the treatment of the chemical scheme will be demonstrated by presenting results from several representative runs and comparing these results with "exact" concentrations obtained by applying a very small stepsize during the computations. Results related to the diurnal variations of some of the chemical species will also be presented. The approach used in this paper does not depend on the particular chemical scheme and can easily be applied when other atmospheric chemical schemes are selected. [ABSTRACT FROM AUTHOR]

For centuries, scientists have observed nature to understand the laws that govern the physical world. The traditional process of turning observations into physical understanding is slow. Imperfect models are constructed and tested to explain relationships in data. Powerful new algorithms can enable computers to learn physics by observing images and videos. Inspired by this idea, instead of training machine learning models using physical quantities, we used images, that is, pixel information. For this work, and as a proof of concept, the physics of interest are wind-driven spatial patterns. These phenomena include features in Aeolian dunes and volcanic ash deposition, wildfire smoke, and air pollution plumes. We use computer model simulations of spatial deposition patterns to approximate images from a hypothetical imaging device whose outputs are red, green, and blue (RGB) color images with channel values ranging from 0 to 255. In this paper, we explore deep convolutional neural network-based autoencoders to exploit relationships in wind-driven spatial patterns, which commonly occur in geosciences, and reduce their dimensionality. Reducing the data dimension size with an encoder enables training deep, fully connected neural network models linking geographic and meteorological scalar input quantities to the encoded space. Once this is achieved, full spatial patterns are reconstructed using the decoder. We demonstrate this approach on images of spatial deposition from a pollution source, where the encoder compresses the dimensionality to 0.02% of the original size, and the full predictive model performance on test data achieves a normalized root mean squared error of 8%, a figure of merit in space of 94% and a precision-recall area under the curve of 0.93. [ABSTRACT FROM AUTHOR]

This paper presents a framework for the development of a computationally-efficient surrogate model for air pollution dispersion. Numerical simulation of air pollution dispersion is of fundamental importance for the mitigation of pollution in Seveso-type accidents, and, in extreme cases, for the design of evacuation scenarios for which long-range forecasting is necessary. Due to the high computational load, sophisticated simulation programs are not always useful for prompt computational studies and experimentation in real time. Surrogate models are data-driven models that mimic the behaviour of more accurate and more complex models in limited conditions. These models are computationally fast and enable efficient computer experimentation with them. We propose two methods. The first method develops a grid of independent dynamic models of the air pollution dispersion. The second method develops a reduced grid with interpolation of outputs. Both are demonstrated in an example of a realistic, controlled experiment with limited complexity based on an approximately 7 km radius around the thermal power plant in Šoštanj, Slovenia. The results show acceptable matching of behaviour between the surrogate and original model and noticeable improvement in the computational load. This makes the obtained surrogate models appropriate for further experimentation and confirms the feasibility of the proposed method. [ABSTRACT FROM AUTHOR]

This paper considers the level of atmospheric air pollution of the 20 largest cities in Russia in 2019–2020. The data used for the study is initially collected by a TROPOMI instrument (on the Sentinel-5P satellite), including measurements of carbon monoxide, formaldehyde, nitrogen dioxide, sulfur dioxide, and aerosol (aerosol index). The measurements were obtained using the cloud-based platform, Google Earth Engine, which presents L3 level data available for direct analysis. The Tropomi Air Quality Index (TAQI) integrates available TROPOMI measurements into a single indicator. The calculation results showed that most of the cities under consideration (15 out of 20) have a low or higher than usual level of pollution. Formaldehyde (35.7%) and nitrogen dioxide (26.4%) play the main role in the composition of pollution particles. A significant share is occupied by sulfur dioxide (16.4%). The contribution of carbon monoxide and aerosol averages 10.8 and 10.6%, respectively. Air pollution in cities is caused by both natural (wildfires, dust storms) and anthropogenic (seasonal migrations of the population, restrictions due to the COVID-19 pandemic) factors. Estimating atmospheric pollution levels in urban areas using an integral index based on remote data (such as TAQI) can be considered as a valuable information addition to existing ground-based measuring systems within the multisensory paradigm. [ABSTRACT FROM AUTHOR]