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Air pollution poses a great danger to human health and economic development, and understanding people's willingness to pay for improved air quality (WTPA) impacts environmental protection. This paper investigates WTPA based on the perspective of subjective well-being (SWB) and analyzes the mediating role of social interaction on the relationship between the two. This paper distinguishes social interactions into online and offline interactions and analyzes whether the mediating effect of the two different interactions on SWB and WTPA exists separately. Using data from the 2018 China General Social Survey (CGSS), we find that SWB has a significant positive effect on WTPA, individuals with higher well-being have higher pro-environmental willingness; there is no age, education level, sex of the person, or regional heterogeneity in the effect of SWB on WTPA; offline social interactions play a partially mediating role between SWB and WTPA, while online social interactions failed to mediate between the two. This paper's policy implication is that improving residents' subjective well-being is both an important development goal and an essential way to resolve the conflict between economic development and environmental protection. [ABSTRACT FROM AUTHOR]

Air pollution is now one of the world's most serious environmental problems. It represents a significant hazard to both health and the climate. Urban air quality is steadily declining, affecting not only the air itself but also impacting the quality of water and land. This paper explores the utilization of machine learning-based algorithms for analysis and prediction of air quality in smart cities. In this paper, smart cities for which air quality index (AQI) is calculated are Ahmedabad, Delhi, Lucknow, Gurugram, and Mumbai. The comparative analysis of different Machine Learning algorithms such as Random Forest Regression (RF), Decision Tree Regression, Linear regression, XgBoost and proposed hybrid model which is combination of Random forest and Xgboost model, have been discussed in the paper. The analysis has been carried out using a machine learning-based algorithm to determine which pollutant is the primary source of pollution in a smart city so that preventative steps can be implemented to reduce air pollution. [ABSTRACT FROM AUTHOR]

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]

The paper presents a specific method of environmental impact assessment applied in Strategic Environmental Assessment (SEA) for the National Emission Reduction Plan (NERP) in the Republic of Serbia, based on air quality. The specificity of the approach is in the application of a semiquantitative method of multicriteria evaluation based on air dispersion modeling and the integration of SEA goals, indicators and criteria for assessing the impact of the NERP on the quality of air and other environmental elements in this method. When predicting changes in air quality for the planning horizon to 2028, the physical, geographical and climatic characteristics of the area were taken into account, as well as technical measures to reduce SO2 emissions, since this was the dominant pollutant from the Serbian coal-fired power plants studied by the NERP. Air pollution modeling was carried out using the AERMOD software package based on the data collected, and the quantitative results obtained were used in a multicriteria evaluation as part of the SEA. The results of the research indicated the importance of applying this approach in order to significantly increase objectivity in the SEA process, since it is an important element of decision making at the strategic level. In addition, a comparative presentation of the modeling results before and after application of the NERP was an important part of the SEA process, and it provided a clear insight into expected changes in the air quality. This is a key argument for making appropriate policy decisions on spatial, energy, environmental and socio-economic development in the Republic of Serbia, which, like other developing countries, is sluggishly following global trends in energy transition. [ABSTRACT FROM AUTHOR]

Mitigation actions needed to achieve the ambitions of the Paris agreement to limit global temperature rise to 1.5 °C or below 2 °C have to align with sustainable development. In the near term, this implies a better understanding of context-specific challenges in integrating sustainability with climate policies during the designing, planning, implementation and financing stages. Through a review of selected studies across regions, this paper draws out conclusions focussing on mitigation–sustainable development goal (SDG) trade-offs, with implications on costs and equity for different development contexts. Studies show that trade-offs depend on how the option is implemented and at what scale; mitigation options such as afforestation, biomass production and digitalisation are examples of this. Some options could also result in significant adverse environmental impacts as in the case of battery waste and raw material resources for electric vehicle (EV) or air pollution associated with compact urban development. We find the most important factors influencing equity include unequal access (e.g. urban green spaces and public transportation), high costs (e.g. EV) and financial constraints. Major knowledge gaps include (i) limited empirical evidence of SDG-related trade-offs associated with scaling up mitigation options, (ii) limited understanding of the extent to which benefits are experienced by different groups, (iii) an understanding of the extent to which local context was considered when assessing mitigation–SDG interaction, including the engagement of stakeholders and (iv) synergies and trade-offs associated with cross-sectoral policies. The paper recommends ex-post analysis of detailed and place-based cases that document how synergies and trade-offs emerged and how these were addressed. [ABSTRACT FROM AUTHOR]

Recently, there has been a global shift toward clean energy sources, and wind turbines (WT) play a crucial role as one of the most popular renewable generation sources. Despite their significant potential, WTs incur high operation and maintenance (O &M) costs due to the challenging operational conditions they face, such as pollution and atmospheric discharges. These stresses reduce the life expectancy of such equipment by increasing the occurrence of failures, thereby diminishing wind farm reliability. To mitigate these failure events, the prediction of the remaining useful life (RUL) of WTs is essential. This prediction, specifically direct RUL prediction, is often made by data-driven methods. However, to achieve competitive levels of accuracy, data-driven methods found in the literature often rely on extensive datasets or high-complexity deep learning models, because historical data containing failures in WTs are scarce, which presents challenges in practical implementation. This paper introduces a novel methodology for rotor RUL prediction in WTs. This method achieved an accuracy of over 80 % using simple machine learning algorithms trained with limited data, making it easy to implement and cost-effective. It is expected that this methodology will assist energy companies in optimizing their operation and maintenance planning processes and contribute to the national energy sector's progress toward achieving global sustainable goals. [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 lack of access to clean energy remains one of the major challenges in the global energy sector. Access to clean, sustainable and affordable energy, outlined in the seventh Sustainable Development Goals (SDG 7) of the United Nations, plays a crucial role in advancing health (SDG 3), as unclean cooking energy may endanger people's health by causing air pollution. However, due to endogeneity problems such as reverse causality, the health consequences of environmental pollution caused by unclean fuel usage are difficult to be scientifically and accurately evaluated. This paper aims to systematically assess the health cost of unclean fuel usage based on tackling endogeneity, using the data from Chinese General Social Survey. The ordinary least squares model, ordered regression methods, instrumental variable approach, penalized machine learning methods, placebo test, and mediation models are applied in this research. Analytical results demonstrate that households' unclean fuel use significantly damages people's health. Specifically, the use of dirty fuel leads to an average of about a one-standard-deviation decline in self-rated health, demonstrating its notable negative effect. The findings are robust to a series of robustness and endogeneity tests. The impact mechanism is that unclean fuel usage reduces people's self-rated health through increasing indoor pollution. Meanwhile, the negative effect of dirty fuel use on health has significant heterogeneity among different subgroups. The consequences are more prominent for the vulnerable groups who are female, younger, living in rural areas and older buildings, with lower socio-economic status and uncovered by social security. Therefore, necessary measures should be taken to improve energy infrastructure to make clean cooking energy more affordable and accessible as well as to enhance people's health. Besides, more attention should be paid to the energy needs of the above specific vulnerable groups faced with energy poverty. [ABSTRACT FROM AUTHOR]

With a large number of new energy vehicles being put into use, it is the general trend for traditional fuel vehicles to withdraw from the market in an orderly manner. Determining the optimal ratio between them in this process is of great significance to the low-carbon sustainable development of cities. Therefore, considering the constraints of urban automobile development planning and air pollution prevention and control policies, a multi-objective model to minimize pollutants and costs is constructed in this paper. Through model calculation and sensitivity analysis of dynamic impact relationship of different types of vehicles, it is determined that when new energy vehicles account for around 36% in Beijing, 57% in Shanghai and 46% in Guangzhou, the pollutant emissions can be minimized without causing a significant increase in social costs. Additionally, compared with 2030, Beijing, Shanghai and Guangzhou can achieve emission reductions of 320,000 tons, 200,000 tons and 250,000 tons, respectively, in 2050 if they implement the policy of banning the sale and delisting of fuel vehicles, which could provide suggestions for the guidance of the low-carbon development plan of the automobile industry. [ABSTRACT FROM AUTHOR]

Collaborative reduction of carbon and air pollutants can more efficiently achieve green technological change, industrial low-carbon transition, and high-quality economic and social development. As a typical environmental policy in China, the pilot carbon Emission Trading Scheme (ETS) has obvious advantages in achieving the collaborative reduction of carbon and air pollutants. Therefore, an evaluation of China's pilot carbon ETS from the perspective of collaborative reduction of carbon and air pollutants is performed in this paper. Compared with previous studies, first, this study innovatively uses the coupled coordination degree (CCD) model to measure the collaborative reduction level of carbon and air pollutants under different scenarios based on the panel data of China's 30 provincial-level regions during 2004–2018. Second, this study uses the DID method to evaluate the impact of China's pilot carbon ETS on the collaborative reduction of carbon and air pollutants and conducts some robustness checks and regional heterogeneity regressions. Third, this study uses the synthetic control method (SCM) further to examine the policy outcomes of the pilot carbon ETS. Scenario analysis shows that attaching importance to reducing air pollution will improve the collaborative reduction effect of carbon and air pollutants. Furthermore, the implementation of China's pilot carbon ETS exerts an effect of roughly 24.7% on reducing carbon, roughly 10.1% on reducing air pollutants, and roughly 22.0% on the collaborative reduction of carbon and air pollutants, ceteris paribus. Regional heterogeneity analysis shows that the impacts of the pilot carbon ETS are significant in all regions, except that the impact on reducing air pollutants in the central region is not significant. In addition, results from SCM indicate that the impacts of the pilot carbon ETS on the collaborative reduction of carbon and air pollutants are significantly efficient in Beijing, Tianjin, Shanghai, Hubei, and Chongqing, while not much efficient in Guangdong and Fujian. The main policy implications include strengthening the top-level design of the ETS in the collaborative reduction of carbon and air pollutants, attaching importance to the governance of air pollution, making the regional governance more targeted, and improving energy efficiency. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has posed a severe challenge to the global economic recovery and China's economic growth. Although China has achieved stage victory against the epidemic, the impact and influence of the epidemic on China's economy will linger. On the positive side, the epidemic has led to a dramatic reduction in air pollution levels and a sharp slowdown in greenhouse gas emissions such as CO2. Forecasting China's CO2 emissions under the impact of the COVID-19 epidemic is crucial for formulating policies that contribute to smooth economic development and rational energy consumption. To this end, this paper improves on the traditional grey model, GM(1, N), by developing a novel fractional multivariate nonlinear grey model, FMNGM(q, N). These improvements include two key points. First, different power exponents are assigned to each relevant factor variable to explain their nonlinear, uncertain, and complicated relationships with the system characteristic variables. Second, the integer accumulation is changed to fractional accumulation to preprocess the data, and the Caputo-type fractional derivative is used to characterize the endogenous relationships among the system characteristic data. In addition, the quantum particle swarm optimization (QPSO) algorithm is used to optimize the above parameters with the goal of minimizing MAPE. The collected data on China's CO2 emission from 2001 to 2018 were divided into two parts according to different stages of economic development: 2001–2009 and 2010–2018. Both sets of data were modelled and analysed separately and compared with other models. Results showed that the proposed model had better prediction accuracy than other models. Finally, the model built using the 2010–2018 data was used to forecast China's CO2 emissions. Results show that China's CO2 emissions in 2020 under the impact of COVID-19 will decrease by approximately 3.15% from 2019 to 9893 million tons. The results bear important policy implications for planners in investment in clean energy and infrastructure to achieving the goal of a low-carbon transition. [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]

Amidst the global upsurge in industrial robot deployment, there remains a notable gap in our understanding of their environmental impact. This paper explores how the introduction of industrial robots has changed air quality at both the local and neighborhood levels in China. Using the Spatial Durbin Model, we investigate the regional spillovers of PM 2.5 concentration and the diffusion of this innovative technology. Our findings reveal that the rise of robots significantly reduces air pollution in the local area, while exacerbating it in neighboring regions. This contrast is mainly because pollution-intensive industries are more inclined to relocate to neighboring regions than their cleaner counterparts, after the local use of robots increases. Throughout the process, internal costs rather than external costs dominate firms' relocation decisions. This study provides novel insights into the complex environmental externalities associated with the spread of industrial robots and highlights the critical issue of growing environmental inequality in the era of emerging technologies. [ABSTRACT FROM AUTHOR]

Public investments in subway systems are often motivated by improving local air quality. Recent studies, however, have reached different conclusions on the air quality benefits of subway investment. To reconcile these findings, this paper examines the air quality effects of all 359 subway line openings in China between 2013 and 2018. The machine learning method adopted in this paper substantially improves the consistency and precision of the estimates by purging seasonality, volatility, and the nonlinear effects of meteorological conditions in air quality data. The empirical results suggest an insignificant short-term effect and a significant long-term effect, which is expected as the adjustment of commuting mode takes time. Using the causal forest approach, the heterogeneity analysis find that a city that is experiencing rapid economic growth from a lower income level and currently has fewer subway lines is more likely to experience statistically significant improvements in air quality from a subway opening. These findings help reconcile the different findings in the literature and shed light on air pollution reduction as one of the objectives of public transit investment. [ABSTRACT FROM AUTHOR]

This study delves into the repercussions of the 2023 earthquake in Turkey, particularity its impact on air pollution. A year post-event, it is evident that scientific literature has paid limited attention to monitoring the situation. However, the release of hazardous substances, such as asbestos, lead, and other toxins, from damaged structures poses a significant threat by contaminating nearby air, soil, and water sources, thereby jeopardizing ecosystems and public well-being. The improper disposal of waste post-earthquake and the presence of mining and oil refinery sites in the region contribute to potential air pollutants. These circumstances create challenging environments conducive to the spread of respiratory diseases, with potential long-term health and social consequences. Unfortunately, existing data gaps hinder a comprehensive understanding of the situation. This paper pioneers the reporting and analysis of data regarding potential sources of air pollution resulting from the earthquake in Turkey. It also pinpoints gaps in knowledge, outlining areas that demand further investigation. To effectively prevent and mitigate air pollution risks and associated health concerns linked to earthquakes, strategic recommendations are proposed. A key suggestion is the establishment of post-disaster air pollution monitoring systems capable of swiftly identifying emerging health issues, facilitating efficient responses, and curtailing potential long-term effects of the disaster. The paper underscores the necessity for continuous health monitoring of the affected population to mitigate possible adverse impacts on human health. These strategies play a pivotal role in reducing the likelihood of air pollution, supporting emergency response and recovery initiatives, and fostering new dedicated scientific studies. [ABSTRACT FROM AUTHOR]

Atmospheric resources provide important support for human economic and social systems through their unique ecosystem service functions. Implementing the off-office auditing of atmospheric resources for leading cadres holds great strategic significance for fundamentally solve air pollution problems. Exploring the compilation of an atmospheric resource balance sheet is a necessary pre-step in the implementation of outgoing audits. In this paper, atmospheric resources are innovatively divided into atmospheric capacity resources and atmospheric quality resources from the perspective of the ecosystem service functions of atmospheric resources. The value of atmospheric resource assets is calculated by combining the environmental capacity value method and the environmental loss evaluation method. This study evaluates atmospheric ecological achievements based on the atmospheric resources balance sheet and related accounts as the data carrier and opens up the key "blocking point" of the off-office auditing of atmospheric resources. This paper takes Anhui Province as an example and applies the accounting results of the prepared atmospheric resource balance sheet to evaluate the atmospheric resource ecological achievements of leading cadres from 2016 to 2020. The results clearly reflect the ecological achievements and shortcomings of local leading cadres during their tenure. [ABSTRACT FROM AUTHOR]

Introduction: Considering the adverse carcinogenic and non-carcinogenic health effects of Volatile Organic Compounds (VOC) and specially Benzene, Toluene, Ethylbenzene and Xylenes (BTEX) which are widely used in vehicle manufacturing industry, it is clear that monitoring and control of occupational exposure to these substances is of utmost significance. More often than not the studies on occupational exposure to VOCs in vehicle manufacturing industry—specifically BTEX—have been carried out exclusively in paint shop units. The purpose of this study was to assess occupational BTEX exposure & health risk in different units of a vehicle manufacturing industry. Material and methods: Foam injection unit (FIU), gluing unit (GU), repair shop unit (RSU), molding unit (MU) as well as paint shop units (PSU) were selected for this study. Sampling and analysis were conducted based on National Institute for Occupational Safety and Health (NIOSH) 1501 guidelines by utilizing MSA personal sampling pumps set to 0.2 l/m and Gas Chromatography equipped with Flame Ionization Detector (GC-FID). Risk characterization and assessment was performed using Environmental Protection Agency Risk Assessment Information System (EPA RAIS). Results and conclusion: The results revealed that the concentration of benzene in gluing and paint shop units was above occupational exposure limits. Carcinogenic risk levels in 8 out of 10 units registered in the 1E-2 to 1E-4 range, and only two units registered a risk level below EPA's acceptable risk level (10–6). The highest risk levels were measured in gluing, paint shop and repair shop units. Furthermore, the total non-carcinogenic hazard quotient was measured higher than 1 in 8 out of 10 studied units. Considering the abundance and wide use of VOCs -most notably BTEX- and the adverse carcinogenic and non-carcinogenic health effects of these compounds, it is advised to: change the type of paint used in paint shops, improve the existing ventilation systems, develop new and more efficient ventilation systems and conduct consistent training. Highlights: In this paper, we report on the levels of occupational exposure workers encounter on a daily basis to a specific group of Volatile Organic Compounds (VOC): Benzene, Toluene, Ethylbenzene and Xylene(s) (BTEX) in various sections of a vehicle manufacturing industry and the resulting health risks. Reviewing previous studies revealed that as a result of low occupational exposure to BTEX in vehicle manufacturing industry, more often than not the studies on said compounds and the ensuing health effects were exclusively carried out in paint shop units, ignoring health risks in other units & creating the assumption that health risks in other units are negligible. The purpose of this study was to assess occupational BTEX exposure and its associated health risk in different units of a vehicle manufacturing industry, including foam injection unit (FIU), gluing unit (GU), repair shop unit (RSU), molding unit (MU) as well as paint shop units (PSU). Revealing the possibility of exposure to BTEX not only in paint shop units but also other previously disregarded units, the health risk calculated in some units such as gluing unit was higher than the EPA acceptable risk range (10-4). Moreover, cancer risk levels of molding (Unit No 6) and repair shop (Unit No 7) units registered in the 10-4 to 10-2 range despite meeting the TLV-TWA (0.174 PPM and 0.449 PPM respectively), suggesting the need for implementation of a health risk assessment in conjunction with exposure assessment. [ABSTRACT FROM AUTHOR]

This paper uses the panel data of 275 prefecture-level cities in China in 2003–2019 and spatial Durbin model to verify the impact of environmental regulation and industrial agglomeration on air pollution. This paper finds that the enforcement intensity of environmental regulations (ER) is not consistent among cities. The effects of strict ER on air pollution in local city are the inverted-U-shape curve. The effects of more stringent ER in adjacent cities j on air pollution in city i are the U-shape curve. The more stringent ER in local city may lead to the decrease in agglomeration of manufacturing sectors (AM), thereby reducing air pollution. The polluting firms may shift production from neighboring cities j with stringent ER to city i with lax ER, thereby leading to the increase in AM, which aggravate air pollution in city i. The more stringent ER in local city i may lead to the increase in agglomeration of productive service industry (AS), thereby reducing air pollution. To avoid the transfer of polluting industrial sectors, the paper suggests that the more stringent implementation of ER should become nationwide actions. [ABSTRACT FROM AUTHOR]

Protecting people from air pollution is an important task for developing countries. Over the past several decades, different types of policies have been enacted in developing countries to improve air quality, with policy impact analysis being conducted as well. As for China, while there is some existing literature on the impact of short-term action on air quality, there is much less research to investigate the roles of long-term air quality control mechanism on air quality change. This paper uses the "2 + 26" city cluster in China as a case study area to particularly analyze the roles played by short-term campaigns that compose the Winter Action Plan and a long-term mechanism referred to as the environmental accountability system in pollution control. Based on econometric and descriptive statistical analyses, it is found that the implementation of the Winter Action Plan has significantly improved the air quality, and the environmental accountability system seems to have exerted an impact, as cities with better air quality appear to establish a more stringent environmental accountability system. The paper provides the following insights for other developing countries: short-term campaigns can quickly reduce pollution and protect human health, while long-term mechanisms are needed to ensure sustainable environmental protection. [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]

Situated at the intersection between environmental entrepreneurship and urban sustainability, our paper seeks to investigate the links between city-level green venture ecosystems and the ability of urban centres to reduce air pollution. Using a large dataset of 12,834 urban centres from around the world and their associated yearly average particulate matter (PM2.5), we show that an increase in the cumulative number of green start-ups drives the lowering of PM2.5 levels. Looking closely at the subsectors that drive the results, we observe that the urban centres which hosted increased numbers of innovators in smart grid technologies, energy efficiency and wind energy generation (the low carbon energy sector overall) also experienced a decrease in air pollution over the 2010–2019 period. Thus, our study is a global analysis of the environmental impact of green entrepreneurship on local air pollution. Plain English Summary: This research seeks to understand the link between green venture-backed start-ups and air pollution within urban centres around the world. We find that there is a direct link between the number of green start-ups, venture capital (VC) funding and air quality. The air quality of an urban area improves with the number of green start-ups and with the number of VC funding. We use multiple tests and the passing of legislations to determine the causality of that relationship. We also run an analysis to determine the start-up sectors that have the highest impact on the air quality of their urban area, they were energy efficiency, materials, smart grid and wind start-ups. Surprisingly, some of the sectors from which we would have expected the highest impact, such as air quality start-ups, did not improve the quality of the air in their urban centres. [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]

One of the increasingly common methods to counteract the increased fuel consumption of vehicles is start-stop technology. This paper introduces a methodology which presents the process of measuring and creating a computational model of CO2 emissions using artificial intelligence techniques for a vehicle equipped with start-stop technology. The method requires only measurement data of velocity, acceleration of vehicle, and gradient of road to predict the emission of CO2. In this paper, three methods of machine learning techniques were analyzed, while the best prediction results are shown by the gradient boosting method. For the developed models, the results were validated using the coefficient of determination, the mean squared error, and based on visual evaluation of residual and instantaneous emission plots and CO2 emission maps. The developed models present a novel methodology and can be used for microscale environmental analysis. [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 is aimed at developing an air quality monitoring system using machine learning (ML), Internet of Things (IoT), and other elements to predict the level of particulate matter and gases in the air based on the air quality index (AQI). It is an air quality assessor and therefore a means of achieving the Sustainable Development Goals (SDGs), in particular, SDG 3.9 (substantial reduction of the health impacts of hazardous substances) and SDG 11.6 (reduction of negative impacts on cities and populations). AQI quantifies and informs the public about air pollutants and their adverse effects on public health. The proposed air quality monitoring device is low-cost and operates in real-time. It consists of a hardware unit that detects various pollutants to assess air quality as well as other airborne particles such as carbon dioxide (CO2), methane (CH4), volatile organic compounds (VOCs), nitrogen dioxide (NO2), carbon monoxide (CO), and particulate matter with an aerodynamic diameter of 2.5 microns or less (PM2.5). To predict air quality, the device was deployed from November 1, 2022, to February 4, 2023, in certain bauxite-rich areas of Adamawa and certain volcanic sites in western Cameroon. Therefore, machine learning algorithm models, namely, multiple linear regression (MLR), support vector regression (SVR), random forest regression (RFR), XGBoost (XGB), and K-nearest neighbors (KNN) were applied to analyze the collected concentrations and predict the future state of air quality. The performance of these models was evaluated using mean absolute error (MAE), coefficient of determination (R-square), and root mean square error (RMSE). The obtained data in this study show that these pollutants are present in selected localities albeit to different extents. Moreover, the AQI values obtained range from 10 to 530, with a mean of 132.380 ± 63.705, corresponding to moderate air quality state but may induce an adverse effect on sensitive members of the population. This study revealed that XGB regression performed better in air quality forecasting with the highest R-squared (test score of 0.9991 and train score of 0.9999) and lowest RMSE (test score of 1.5748 and train score of 0. 0073) and MAE (test score of 0.0872 and train score of 0.0020), while the KNN model had the worst prediction (lowest R-squared and highest RMSE and MAE). This embryonic work is a prototype for projects in Cameroon as measurements are underway for a national spread over a longer period of time. [ABSTRACT FROM AUTHOR]

This paper estimates the causal effect of air pollution on firm innovation based on a nationwide sample of Chinese manufacturing firms. Using thermal inversion as instrument variable (IV), we demonstrate that air pollution significantly hurts firm innovation performance: a 1% increase in air pollution (PM2.5) in the past year, from its mean value, amounts to a 1.5% decrease in the number of invention patents compared to the average between 1999 and 2013. We also provide the casual estimate using regression discontinuity design (RDD) by exploiting the exogenous variation in pollution level caused by China's Huai River policy: the decrease in firms' invention patents is approximately 2.2% in the regions north of the Huai River Line. The underlying mechanism analyses reveal that poor air quality has detrimental effects on labor productivity, firms' human capital accumulation, and financing ability. These findings highlight the substantial costs associated with air pollution, which are greater than previously thought, as it hinders innovation, a crucial driver of long-term economic growth. [ABSTRACT FROM AUTHOR]

The impact of air pollution is one of the hotspots attracting continuous scholarly attention, but the comprehensive statistical and visual analysis reviews are few. Employing the method of bibliometric analysis, this paper took the relevant literature from 1996 to April 2022 on the Web of Science as the research object. Through the methods of keyword co-occurrence analysis and burst analysis, the spatiotemporal evolution trend, cooperation network, outstanding scholars, knowledge base, and research focus of air pollution impact research are visually presented. Via constructing a common word matrix of high-frequency words, clustering analysis is used to aggregate high-frequency keywords into 24 clusters. By the strategic coordinate analysis method, the relationships within and between clusters were revealed. The main findings include (1) research on the impact of air pollution mainly focusing on human health; (2) the six keywords with the highest centrality are California, hydrocarbons, dioxide, generation, Asia, and diesel; (3) these 11 clusters may be developed into future research hotspots: particulate matter, nitrogen oxides, mortality, ozone, pollution, air quality, asthma, children, epidemiology, aerosols, and polycyclic aromatic hydrocarbons; and (4) seven topics are research hot: daily mortality, long-term exposure, coronary heart disease, concentration, North China plain, traffic-related air pollution, and air pollution. [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]

Delhi's need for pure water is growing with its growing population. Along with the need for drinking water, useful sewage treatment is much needed. Another immense problem that the city faces is air pollution that causes health problems to the local community mainly caused by energy traditional plants. The patent Natural Biological System (NBS) developed by Ayala Water and Ecology offered Delhi and many other locations around the globe a practical, on-site method for the simultaneous purification of soil, water, and air with a negative carbon footprint. The current paper describes how those systems were applied to the on-ground situation after an extensive environmental survey that was conducted to identify the needs and difficulties characterizing the city. The solutions provided to Delhi are an example of how urban eras can benefit from the NBS system improving their ecosystem and supporting better and healthier life for their residents. [ABSTRACT FROM AUTHOR]

The management and improvement of air quality are global challenges aimed at protecting human health and environmental resources. For this purpose, in addition to legislative and scientific indications, numerous tools are available: measurement methods and tools for estimating and forecasting. As a collection of data presenting an emission of a pollutant (to air), emission inventories support the knowledge of sources impacting air quality by estimating atmospheric emissions within a specific (wide or limited) reference area. There are several methodological approaches for their definition, which can be classified into bottom–up or top–down methods. This paper aims to review the methodological approaches described in the literature that apply the top–down approach for the disaggregation of atmospheric emissions with high spatial and temporal resolution. The proxy variables used to apply this approach are identified, as well as the spatial and temporal resolution obtained by the authors. The results show that population density and land use are the most common parameters with respect to most of the emission sources and for numerous atmospheric pollutants. The spatial resolution of the disaggregation described in the literature varies from a few hundred metres to several kilometres, in relation to the territorial extension of the study areas. The results of the review help support the selection of the best and most popular proxy variables used to scale emissions inventories. [ABSTRACT FROM AUTHOR]

Classical monitoring of air pollution provides information on environmental quality but involves high costs. An alternative to this method is the use of bioindicators. The purpose of our work was to evaluate atmospheric aerosol pollution by selected polycyclic aromatic hydrocarbons conducted as part of annual active biomonitoring ("moss-bag" technique) with the use of three moss species: Pleurozium schreberi, Sphagnum fallax, and Dicranum polysetum. The gas chromatography-mass spectrometry (GC–MS) was utilized to determine certain 13 polycyclic aromatic hydrocarbons (PAHs). Three seasonal variations in PAH concentrations have been observed as a result of the study. A fire on the toilet paper plant caused an increase of five new compounds: benzo(k)fluoranthene (BkF), benzo(a)pyrene (BaP), indeno(1.2.3)-cd_pyrene (IP), dibenzo(a.h)anthracene (Dah), and benzo(g.h.i)perylene (Bghi) in proximity after 8 months of exposure compared to previous months. The effect of meteorological conditions on the deposition of PAHs (mainly wind direction) in mosses was confirmed by principal component analysis (PCA). Dicranum polysetum moss accumulated on average 26.5% more PAHs than the other species, which allows considering its broader use in active biomonitoring. The "moss-bag" technique demonstrates its feasibility in assessing the source of PAH air pollution in a long-term study. It is recommended to use this biological method as a valuable tool in air quality monitoring. [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]

This paper looks at the relationship between enterprise size and pollution abatement costs using firm-level datasets in China, where size-dependent distortions may exist in the distribution of enterprises of different sizes. We find that the pollution abatement costs decrease with enterprise size. Compared to the smallest size, the magnitudes of marginal decreases in pollution abatement costs per 1000 CNY of industrial output in the largest size are 3.29 CNY, 0.54 CNY and 2.60 CNY for gas, SO2 and dust, respectively. Moreover, we further explore the potential mechanisms for the decrease of pollution abatement costs from three perspectives: environmental protection-related investment, regulation intensity, and technological capability. Our findings suggest that the decrease in the intensity of environmental protection-related investment for large enterprises may be directly reduce the intensity of pollution abatement costs. Although large enterprises have low intensity of pollution abatement costs, this does not mean that they are subject to weaker environmental regulation. More importantly, we find that large enterprises may have more adoption of abatement technologies. For SO2 and dust, the pollutant removal efficiency of the largest enterprises is 4.92% and 1.82% higher than that of the smallest enterprises, respectively. Our results are robust to different specifications. These findings help us enact appropriate policies for enterprises based on their size, thereby improving the efficacy of pollution abatement and achieving the optimal level of pollution abatement for the entire society. [ABSTRACT FROM AUTHOR]

Recent research suggests that short-term exposure to air pollution is associated with an elevated prevalence of respiratory infectious disease. In this paper, we examine the relationship between the air quality index and weekly cases of COVID-19 and influenza-like illnesses (ILI) in the United States. We address potential bias from omitted variables and measurement error with an instrumental variable approach using atmospheric temperature inversions. Unlike other recent studies, we find no relationship between air quality and either COVID-19 or ILI cases. [ABSTRACT FROM AUTHOR]

Introduction: The NIH All of Us Research Program has enrolled over 544,000 participants across the US with unprecedented racial/ethnic diversity, offering opportunities to investigate myriad exposures and diseases. This paper aims to investigate the association between PM2.5 exposure and cancer risks. Materials and methods: This work was performed on data from 409,876 All of Us Research Program participants using the All of Us Researcher Workbench. Cancer case ascertainment was performed using data from electronic health records and the self-reported Personal Medical History questionnaire. PM2.5 exposure was retrieved from NASA's Earth Observing System Data and Information Center and assigned using participants' 3-digit zip code prefixes. Multivariate logistic regression was used to estimate the odds ratio (OR) and 95% confidence interval (CI). Generalized additive models (GAMs) were used to investigate non-linear relationships. Results: A total of 33,387 participants and 46,176 prevalent cancer cases were ascertained from participant EHR data, while 20,297 cases were ascertained from self-reported survey data from 18,133 participants; 9,502 cancer cases were captured in both the EHR and survey data. Average PM2.5 level from 2007 to 2016 was 8.90 μg/m3 (min 2.56, max 15.05). In analysis of cancer cases from EHR, an increased odds for breast cancer (OR 1.17, 95% CI 1.09–1.25), endometrial cancer (OR 1.33, 95% CI 1.09–1.62) and ovarian cancer (OR 1.20, 95% CI 1.01–1.42) in the 4th quartile of exposure compared to the 1st. In GAM, higher PM2.5 concentration was associated with increased odds for blood cancer, bone cancer, brain cancer, breast cancer, colon and rectum cancer, endocrine system cancer, lung cancer, pancreatic cancer, prostate cancer, and thyroid cancer. Conclusions: We found evidence of an association of PM2.5 with breast, ovarian, and endometrial cancers. There is little to no prior evidence in the literature on the impact of PM2.5 on risk of these cancers, warranting further investigation. [ABSTRACT FROM AUTHOR]

The increase in air pollutants and its adverse effects on human health and the environment has raised significant concerns. This implies the necessity of predicting air pollutant levels. Numerous studies have aimed to provide new models for more accurate prediction of air pollutants such as CO2, O3, and PM2.5. Most of the models used in the literature are deep learning models with Transformers being the best for time series prediction. However, there is still a need to enhance accuracy in air pollution prediction using Transformers. Alongside the need for increased accuracy, there is a significant demand for predicting a broader spectrum of air pollutants. To encounter this challenge, this paper proposes a new hybrid deep learning-based Informer model called "Gelato" for multivariate air pollution prediction. Gelato takes a leap forward by taking several air pollutants into consideration simultaneously. Besides introducing new changes to the Informer structure as the base model, Gelato utilizes Particle Swarm Optimization for hyperparameter optimization. Moreover, XGBoost is used at the final stage to achieve minimal errors. Applying the proposed model on a dataset containing eight important air pollutants, including CO2, O3, NO, NO2, SO2, PM10, NH3, and PM2.5, the Gelato performance is assessed. Comparing the results of Gelato with other models shows Gelato's superiority over them, proving it is a high-confidence model for multivariate air pollution prediction. [ABSTRACT FROM AUTHOR]

Based on the spatially correlated effects of air pollution on regional innovation, theoretical hypotheses are proposed, and this paper employs a spatial Durbin model to conduct empirical tests using panel data from 267 Chinese cities from 2003 to 2019, and investigates the mediating effect of human capital. Research has shown that (1) air pollution significantly reduces regional innovation output and has a negative spatial spillover effect significantly in the short term; (2) in the process of regional innovation impacted by air pollution, human capital acts as a mediator role; and (3) analysis of heterogeneity reveals that, from the regional perspective, air pollution has significantly damaged regional innovation in eastern and middle cities, but not significantly influences western cities, and in terms of innovation types, there is a stronger detrimental effect on invention patents exerted by air pollution compared to non-innovation patents. The study's findings provide theoretical and empirical evidence to strengthen environmental governance, enhance regional innovation and promote the coordinated development of regional innovation. [ABSTRACT FROM AUTHOR]

Increasing air pollution not only affects the population's health but also changes its consumption structures and patterns. Using China Family Panel Studies, this study investigates the relationship between air pollution and household consumption. The findings reveal that household consumption is considerably affected by air pollution: One standard deviation rise of PM2.5 concentration will decrease the household consumption by 8.7%. Moreover, this effect is irreversible in the short term. What is more, air pollution has significantly changed consumption structure and patterns. Heterogeneous analysis indicates that the influence of air pollution on consumption generates the so-called Matthew Effect, wherein medium and low-income and rural households are exposed to a greater negative effect. Mechanism tests indicate that air pollution may reduce household consumption through three channels: increase negative emotions, decrease outdoor activities, and depress future expectations. The conclusions drawn in this paper enrich our understanding of the economic impact caused by air pollution and bring important significance to the government in promoting the coordination and sustainable development of the environment and economy. [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]

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]

Ground-level ozone (O3) is one of the major air pollutants. A large body of literature has linked O3 air pollution to various adverse human health effects. The objective of this study is to attain a comprehensive and in-depth understanding of the progress and frontiers of research on O3 and human health. We used bibliometric methods to summarize publications on O3 air pollution and public health between 1990 and 2022 obtained from the Web of Science Core Collection database. VOSviewer and R software were used for bibliometric analysis and visualization. A total of 4501 relevant papers were included in the analysis. There has been a significant increase in the number of publications since 2013, with the USA being the major contributor, followed by China and England. Harvard University was the most prolific research institution, followed by the US Environmental Protection Agency and the University of North Carolina System. Professor Joel Schwartz was the most published author and has established a complex network of national and international collaborations. Co-occurrence analysis of keywords suggested evolving research hotspots, from toxicological studies to population-based epidemiological studies and from the respiratory system to the extra-pulmonary system. Research on O3 and its human health effects has progressed rapidly over the past few decades, but academic disparities still persist between developed and developing countries. There is an urgent need to strengthen international cooperation to address the public health challenges posed by rising O3 air pollution in the future. [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]

Air pollution is one of the most serious environmental problems worldwide. Especially, PM2.5 cannot be blocked by filtration system of respiratory tract to cause serious threat to public health. Considering the serious impact of air pollution on human health and life, air pollutant concentration forecast has been drawn great attention because it can provide people with information of air quality. In order to improve the accuracy of air pollutant concentration forecast, this study proposes a new Ensemble Empirical Mode Decomposition (EEMD) multi-step forecasting model is proposed. Initially, input data are decomposed by enhanced empirical wavelet transform (EEWT) to increase the dimensionality of the data. Next, EEWT-NLSTM, EEWT-DLSTM and EEWT-Bi-LSTM models are constructed using wavelet decomposition results and Nested Long short-term memory neural network (NLSTM), Deep Long short-term memory neural network (DLSTM), and Bi-directional long-short term memory neural network (Bi-LSTM), respectively, followed by comparison and contrast of the prediction results. Three single prediction models are incorporated into combined weighted forecasting model by weight assignment, it improves the prediction accuracy. To accurately assess effectiveness of the experimental model, three evaluation metrics, Mean Absolute Percentage Error (MAPE), Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE), are used in this paper to quantitatively evaluate prediction performance of proposed model. For the prediction of PM2.5, the proposed model achieved a Mean Absolute Error (MAE) of 0.047428. [ABSTRACT FROM AUTHOR]

The low-carbon city (LCC) pilot policy is significant in tackling carbon emission reduction and environmental protection. Several studies have demonstrated that LCC policy dramatically decreases air pollution in cities for environmental preservation, while there is a shortage of research on banking development. Using city-level data from 2005 to 2018 in China, we examine the impact of the LCC pilot policy on the growth of the banking sector by using a difference-in-difference (DID) model and analyze the underlying transmission mechanism. The empirical result demonstrates that the construction of LCC significantly impedes the growth of the regional banking sector, and the conclusions above hold up to a battery of tests. First, the LCC policy drastically reduces the scale of industries in the pilot cities, including secondary industry output, tertiary industry output, fixed asset investment, and fixed asset balance. Second, the LCC policy encourages industrial reorganization in the pilot cities, as demonstrated by a change in market demand from high carbon-emitting enterprises and non-state-owned enterprises to low carbon-emitting enterprises and state-owned enterprises, respectively. The results indicate that the LCC policy produces credit risk at enterprises and conveys it to banks, resulting in a retarding effect on banking development. [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]