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With the rapid development of urbanization in China, urban energy consumption increases rapidly, leading to energy shortages and environmental pollution, of which building operational energy consumption carbon emissions (BECCE) account for a large proportion. It has a vital impact on global warming and urban green and sustainable development. Chengdu city in Sichuan Province is taken as the research area in this paper. First, basic information and power data on four types of single buildings, including large-sized buildings, small- and medium-sized buildings, government agencies, and residential buildings, are collected. Second, the characteristics of the four types of buildings are extracted, and the calculation model of BECCE ("electricity-carbon" model) based on particle swarm optimization algorithm-support vector machine (PSO-SVM) is constructed, and the model is trained and verified using the method of five-fold cross-validation. Then, according to the mean absolute error (MAE), root mean square error (RMSE), and R² evaluation indicators, the constructed "electricity-carbon" model is compared and evaluated. Finally, the generalization ability of the "electricity-carbon" model is verified. The research results show that (1) the "electricity-carbon" model constructed in this paper has a high accuracy rate, and the fitting ability of the PSO-SVM model is significantly better than that of the support vector regression (SVR) model; (2) in the testing stage, the fitting situation of large buildings is the best, and MAE, RMSE, and R² are 858.7, 1108.6, and 0.91, respectively; and (3) the spatial distribution map of regional BECCE can be quickly obtained using the "electricity-carbon" model constructed in this paper. The "electricity-carbon" model constructed in this paper can provide a scientific reference for building emission reduction. [ABSTRACT FROM AUTHOR]

China is currently focusing on the cooperative control of air pollution and CO2 emissions, as well as the mitigation of economic policy uncertainty (EPU). By using panel data from 282 cities spanning from 2003 to 2017 and a newly constructed city-level EPU index, a spatial Durbin, two-way fixed-effects model is employed, with the aim of estimating the impact of EPU on the synergistic emissions intensity (SEI) of air pollutants and CO2. Additionally, this paper investigates the potential channels through which EPU influences SEI. It also explores how pressures related to environmental protection and economic development affect the impact of EPU on SEI. The results indicate that a unit increase in EPU will result in a rise in the SEI of local cities, adjacent cities, and total cities by 930.9%, 69,162.7%, and 70,093.6%, respectively. Moreover, the channel analysis suggests that EPU exacerbates SEI by undermining the upgrading of the industrial structure, augmenting industrial structure distortion, and escalating labor market distortion. Furthermore, the effect of EPU on SEI may be lessened by an increase in environmental protection pressure, while an increase in economic development pressure may exert a positive influence. Finally, this paper concludes by recommending that policymakers should prioritize the maintenance and stability of economic policies, facilitate the advancement of the industrial structure, enhance the efficiency of labor resource allocation, and underscore the significance of managing urban air pollution and CO2 emissions. [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]

Despite their key role in integrated waste management, direct (incineration) and indirect (gasification/pyrolysis) waste combustion processes are still opposed by some of the general public due to the past emission levels of air pollutants. In fact, although the release of air pollutants (especially dioxin) to the atmosphere from waste combustion processes has gradually decreased over the years, thanks to the introduction of stricter regulations and more advanced removal technologies, there is still an unsolved problem regarding the public acceptance of waste-to-energy facilities. The aim of this paper is to provide an overview of the state-of-the-art air pollution control (APC) technologies used in waste combustion facilities. Air pollution control technologies are designed to reduce or eliminate the emissions of harmful pollutants into the atmosphere. These technologies are important for safeguarding public health, protecting ecosystems, complying with regulations, and promoting a more sustainable and resilient future for both local and global communities. This paper will highlight the complexity behind emission control and the efforts made by this sector over the years. This paper will also propose suggested configurations based on the interactions/complementarity between different APC technologies and recent findings to improve their performance. [ABSTRACT FROM AUTHOR]

This document is a list of academic articles and research papers related to carbon emissions and their effects. The articles cover various topics such as the analysis of carbon emissions in different regions, the driving factors of carbon emissions, and the impact of carbon emissions on the environment. The articles provide valuable insights into the complex issue of carbon emissions and can be useful for library patrons conducting research on this topic. [Extracted from the article]

Effective air quality prediction models are crucial for the timely prevention and control of air pollution. However, previous models often fail to fully consider air quality's temporal and spatial distribution characteristics. In this study, Xi'an City is used as the study area. Data from 1 January 2019 to 31 October 2020 are used as the training set, while data from 1 November 2020 to 31 December 2020 are used as the test set. This paper proposes a multi-time and multi-site air quality prediction model for Xi'an, leveraging a deep learning network model based on APSO-CNN-Bi-LSTM. The CNN model extracts the spatial features of the input data, the Bi-LSTM model extracts the time series features, and the PSO algorithm with adaptive inertia weight (APSO) optimizes the model's hyperparameters. The results show that the model achieves the best results in terms of MAE and RMSE. Compared to the PSO-SVR, BPTT, CNN-LSTM, and GA-ACO-BP models, the MAE improved by 9.375%, 6.667%, 2.276%, and 4.975%, while the RMSE improved by 8.371%, 8.217%, 6.327%, and 5.293%. These significant improvements highlight the model's accuracy and its promising application prospects. [ABSTRACT FROM AUTHOR]

Earthquakes can cause serious damage to buildings, roads and other infrastructure. The large amount of dust and particulate matter generated when these structures collapse and are damaged can quickly enter the air, leading to a decline in air quality. At the same time, earthquakes may cause secondary disasters such as fires and landslides, which will also produce large amounts of soot and particulate matter, which will have a negative impact on air quality. Therefore, earthquake disaster risk assessment studies are carried out to identify potentially hazardous areas and facilities in advance in order to reduce the air pollution problems that may be caused by earthquakes. Existing research on earthquake disaster risk assessment mainly evaluates earthquake risk from the perspective of geology or seismology, but there are few studies based on multidisciplinary assessment that integrates geology, seismology, engineering and social sciences into socioeconomic factors. To this end, based on remote sensing and GIS technology, this paper takes Qinghai Province, a seismically active area, as the research area, and integrates land use data, natural environment data, social environment data and seismic parameter zoning data to construct a comprehensive assessment model for earthquake disaster vulnerability and risk. The results showed that there were 5 very high-risk areas, 7 high-risk areas, 10 medium-risk areas, 11 low-risk areas and 12 very low-risk areas in Qinghai Province. The high-risk areas are mainly distributed in the central and western parts of Qinghai Province, where the earthquake breeding environment is sufficient, the scale of active faults is huge and the adaptability of the carrier is low. The results of an earthquake disaster risk assessment can provide a reference for the government to formulate environmental protection policies. According to the assessment results, the government can formulate targeted measures to strengthen air pollution control and improve air quality. [ABSTRACT FROM AUTHOR]

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

Developing air quality management systems to control the impacts of air pollution requires reliable data. However, current initiatives do not provide datasets with large spatial and temporal resolutions for developing air pollution policies in Brazil. Here, we introduce the Brazilian Atmospheric Inventories (BRAIN), the first comprehensive database of air quality and its drivers in Brazil. BRAIN encompasses hourly datasets of meteorology, emissions, and air quality. The emissions dataset includes vehicular emissions derived from the Brazilian Vehicular Emissions Inventory Software (BRAVES), industrial emissions produced with local data from the Brazilian environmental agencies, biomass burning emissions from FINN – Fire INventory from the National Center for Atmospheric Research (NCAR), and biogenic emissions from the Model of Emissions of Gases and Aerosols from Nature (MEGAN) (10.57760/sciencedb.09858, Hoinaski et al., 2023a; 10.57760/sciencedb.09886, Hoinaski et al., 2023b). The meteorology dataset has been derived from the Weather Research and Forecasting Model (WRF) (10.57760/sciencedb.09857, Hoinaski and Will, 2023a; 10.57760/sciencedb.09885, Hoinaski and Will, 2023c). The air quality dataset contains the surface concentration of 216 air pollutants produced from coupling meteorological and emissions datasets with the Community Multiscale Air Quality Modeling System (CMAQ) (10.57760/sciencedb.09859, Hoinaski and Will, 2023b; 10.57760/sciencedb.09884, Hoinaski and Will, 2023d). We provide gridded data in two domains, one covering the Brazilian territory with 20×20 km spatial resolution and another covering southern Brazil with 4×4 km spatial resolution. This paper describes how the datasets were produced, their limitations, and their spatiotemporal features. To evaluate the quality of the database, we compare the air quality dataset with 244 air quality monitoring stations, providing the model's performance for each pollutant measured by the monitoring stations. We present a sample of the spatial variability of emissions, meteorology, and air quality in Brazil from 2019, revealing the hotspots of emissions and air pollution issues. By making BRAIN publicly available, we aim to provide the required data for developing air quality policies on municipal and state scales, especially for under-developed and data-scarce municipalities. We also envision that BRAIN has the potential to create new insights into and opportunities for air pollution research in Brazil.​​​​​​​ [ABSTRACT FROM AUTHOR]

The increased demands for driver comfort and stringent pollution control measures have resulted in a revival of planetary gearboxes for road applications, due to their possibility to change the transmission ratio under load in synchronism with engine operation. Modern boxes provide as many transmission ratios as possible from the least possible number of simple component planetary gear trains (PGTs) by providing links between elements of multiple component PGTs. The application conditions decide to prioritize either the maximum number of transmission ratios, or ruggedness and reliability. Power circulation, hollow shafts, or complex planet carrier arrangements are avoided if possible. This paper deals with multispeed complex PGTs composed of at least two interconnected simple component PGTs controlled by brakes and clutches. Several variants of complex PGTs and the placement of brakes and clutches on external shafts of the gear trains are examined, and the transmission ratio functions derived. The kinematics of multi speed gear trains are obtained as combinations of two or more two-speed gear trains. An analysis of several contemporary gearbox layouts is provided together with the transmission ratio functions, together with an overview of the procedure for the calculation of creation of multi-speed gear trains is given. [ABSTRACT FROM AUTHOR]

During the winter of 2019, the global outbreak of COVID-19 prompted extensive research on urban air pollution under lockdown measures. However, these studies predominantly focused on winter conditions, thereby limiting investigations into changes in urban air pollutants during other seasons that were also subject to lockdown restrictions. Shanghai, China, has undergone two COVID-19 lockdown periods in two seasons: winter 2019 and spring 2022. The seasonal variations and human activities were represented by meteorological factors and nighttime light brightness in this paper, respectively. The reduction in human-related emissions during the two lockdown periods was estimated based on the targets outlined in China's Air Pollution Prevention and Control Action Plan. The results showed significant reductions in NO2 and PM particles during the two lockdown periods, both accompanied by a notable increase in O3 concentration. In comparison to the winter lockdown, there was an approximate 40% decrease in the NO2 and PM2.5 concentrations in the spring, while the O3 concentration exhibited an increase of 48.81%. Furthermore, due to shifting wind patterns during the two lockdowns from winter to spring, the high-pollution core areas shifted 20–25 km southeastward in the spring. The PM particles and NO2 concentrations exhibited a considerable impact from human activities, whereas the O3 concentration was affected mostly by seasonal change and interactions among air pollutants. Compared to the corresponding non-lockdown condition, the concentration of CO decreased during the winter lockdown; however, it increased during the spring lockdown. The different change in CO concentration during the two lockdown periods was found to have a lower effect on the O3 concentration than that caused by changes in meteorological factors and nitrogen oxide (NO, NO2) concentrations. In summary, the impact of COVID-19 lockdown periods on urban air pollutants was more pronounced in spring compared to winter, and the interactions among air pollutants also underwent alterations. [ABSTRACT FROM AUTHOR]

During rapid urbanization, microclimate environment deterioration through events such as haze pollution and heat waves has continuously occurred in cities, which greatly affects the living environment, production activities, and health of urban residents. Therefore, it is particularly necessary to explore methods for controlling and optimizing the urban microclimate environment. In this paper, based on the mechanism of the effect of urban spatial structure at street-level on the distribution of atmospheric particulate matter, an indicator system that can be employed to comprehensively describe and quantify urban morphological structure at street-level was constructed from eight aspects: the spatial morphology of street-valleys, intensity of land use and development, geometric structure of buildings, inhomogeneity of buildings, roughness of the underlying surface, distribution of ecological landscapes, 3D architectural landscape morphology, and ventilation potential. Furthermore, using satellite remote sensing images and vector thematic maps of Shanghai, indicator factors were quantified by applying GIS technique. The intrinsic mechanism of the influence of the urban morphology on the diffusion and transport of atmospheric particulate matter was comprehensively analyzed by combining statistical methods and data mining algorithm, and eight key dominant factors were identified that can be considered to improve the urban ventilation conditions and help control urban air pollution, namely, the land use intensity, urban canopy resistance, vegetation cover, spatial congestion rate, comprehensive porosity, height-to-gross floor area ratio, building density, and average building volume ratio. As such, according to the quantitative analysis results for various combinations of the dominant factors, a spatial optimization strategy at street-level that can help improve the urban air quality was proposed in terms of identifying the pathways through which urban spatial elements affect the distribution of particulate matter, i.e., controlling the source–flow diversion–flow convergence process. [ABSTRACT FROM AUTHOR]

Air pollution poses a grave threat to human health and everyday life. Accurate air-quality prediction plays a crucial role in effectively preventing and controlling air pollution. A multi-graph spatial-temporal attention network is proposed to predict the air quality in a given area by analyzing the interconnections between stations and the individual characteristics of each station. Firstly, this paper constructs multi-scale spatial-temporal graphs from spatial and temporal perspectives to effectively capture the interconnections between stations, thereby comprehensively understanding the spatial-temporal relationships among them. Secondly, incorporating the nonlinear temporal correlation of station data, this paper proposes a temporal multi-graph attention-fusion module to integrate information from both neighboring stations and the station itself. Finally, predictions are made using a spatial-temporal graph network. The experiments in this paper were based on air-quality data from Beijing and Tianjin, and the following experimental results were obtained. (1) The proposed model outperforms the baselines significantly in both single-step and multi-step prediction tasks. (2) The ablation study confirms that the graph constructed in this paper contributes to improving the performance of the model. (3) Comparing the performance of attention components, the proposed attention mechanism exhibits better performance than other attention mechanisms. [ABSTRACT FROM AUTHOR]

Dielectric barrier discharge (DBD)-based technology is considered a promising alternative for controlling indoor air pollutants. However, its limited processing capacity and lack of design techniques have restricted its use in practical applications. This paper introduces a methodology for designing upscaled DBD reactors with a processing capacity of up to 1000 L/min for treating high-flow-rate gases to mitigate indoor air pollution. A ten-layer high-flow-rate DBD reactor was constructed, with fundamental characterizations, including electrical and spectroscopic measurements, conducted to verify the feasibility of the proposed methodology. In particular, the flow paths of the ten-layer DBD reactor were optimized by incorporating an air diffuser and perforated metal plates, all without significant modifications. Computational fluid dynamics simulations showed a remarkably improved velocity uniformity (0.35 m/s to 0.04 m/s, as evidenced by the velocity standard deviation) in the 10 flow channels. These simulation results were consistent with the experimental results, wherein the velocity standard deviation reduced from 1.38 m/s to 0.13 m/s. Moreover, multi-gas plasma ignition for up to six gas species and high-flow-rate plasma generation of up to 1000 L/min were achieved. These results provide the foundation for developing DBD technologies for practical applications in high-flow-rate gas treatment, particularly for controlling indoor air pollution. [ABSTRACT FROM AUTHOR]

Accurate prediction of air quality change is essential for air pollution control and human daily mobility. Due to the strong spatial and temporal correlation of air quality changes, existing air quality prediction methods often face the problem of low prediction accuracy due to insufficient extraction of spatio-temporal features. In this paper, we proposed a self-tuning spatio-temporal neural network (ST2NN) to enhance air quality prediction. ST2NN model consisted of four modules. First, ST2NN model constructed a temporal feature extraction module and a spatial feature extraction module based on gated recurrent unit (GRU) and graph convolutional neural network (GCN), respectively, and the two feature extraction modules adopted a parallel structure, which could effectively extract the spatio-temporal features in data. Additionally, ST2NN model constructed a feature fusion module based on gating mechanism to delineate the contribution of spatio-temporal features to the predicted values. Further, ST2NN model constructed a Hyperband hyperparameter optimization module based on Hyperband optimization algorithm to automatically adjust the network hyperparameters. The structure of ST2NN model endowed it with excellent spatio-temporal feature extraction and parameter adaptability. ST2NN model was evaluated and compared with existing models, including convolutional long short-term memory neural network (ConvLSTM), GRU, combined convolutional neural network and long short-term memory neural network (CNN-LSTM), and GCN-LSTM for air quality index (AQI) prediction using data from twelve monitoring stations in Beijing, China. Across all four evaluation indexes, ST2NN model outperformed the comparative models, improving prediction accuracy by 0.51%-10.18% (measured using R 2 ). From the experimental results, it can be seen that ST2NN model constructed from the perspective of spatio-temporal feature extraction has better prediction performance compared with the existing air quality prediction model, which provides a new method for air quality prediction and has certain application value. [ABSTRACT FROM AUTHOR]

The insatiable demand for rock supplies has enticed numerous building and construction enterprises to participate in stone quarrying. This paper examines the environmental impact of quarrying on air quality in Ebonyi State, Nigeria. To achieve the main aim of the study, an objective was set to detect air pollutants at the quarry sites. A total of 220 air samples were measured from six points around the quarry locations and recorded in situ for analysis. The samples were measured three times a day (morning, afternoon, and evening) for three days. Gas monitors were used to monitor air pollutants. The generated data were subjected to completely random design (CRD) sampling techniques. The separation of means and tests was performed using Fisher's Least Significant Difference (FLSD) at a significance probability level of 5%. Based on statistical analysis, the findings detected significantly higher concentration levels of particulate matter, nitrogen dioxide, hydrogen sulfide, carbon monoxide, sulfur dioxide, chlorine, volatile organic compounds, ammonia, and hydrogen cyanide in the quarry areas than the value detected in the control area. The findings also confirmed higher noise levels in the locations. It was also observed that the concentration levels of the parameters differed from point to point and at different times of the day. This really means the occurrence of a high rate of air pollution in the study locations. Based on the above findings, it is highly recommended that air pollution control equipment be installed at quarry sites in order to reduce gaseous (pollutant) emissions. [ABSTRACT FROM AUTHOR]

The use of non-linear loads, such as electro static precipitator (ESP) and variable speed drive (VSD), draws much distorted currents and voltages. They lead to generation of voltage/current harmonics and consume reactive power. Al-Tahady ESP had been designed and manufactured at Al-Zawraa state company as an air pollution control system. This paper presents the solution for this problem, firstly a separation of power supplies led to proper operation of the system, this secondly encourages to think of harmonics generated by variable speed drive. The three-phase harmonic filter was used to compensate harmonics generated by non-linear load (electro static precipitator and variable speed driver that derives the fan). The instantaneous harmonic reduction theory that uses Fourier series was used to design the harmonic filter. Power SIMulation (PSIM) and MATLAB have verified the harmonic distortion and the active filter control scheme. [ABSTRACT FROM AUTHOR]

In a changing climate, it is vital to focus on the co-benefits of the pollution control and carbon emission reduction. Based on calculation of emission equivalent, the synergy coefficient is further calculated to quantitatively analyze the co-benefits of air pollution control and carbon reduction in the Yangtze River Delta; Scenario analysis in co-benefits in the Yangtze River Delta from 2026–2035 is thoroughly proposed after STIRPAT model is designed based on influencing factors confirmation including population size, economic scale, industrialization level, urbanization rate and energy intensity from measuring dimensions of synergy coefficient. The results show that the Yangtze River Delta region can partially achieve synergistic emission reduction by 2026 and realize comprehensive synergistic emission reduction of air pollution and carbon emissions not late than 2030, which provides a reference for promoting the decision-making of the new stage of long-term carbon and pollution reduction, and further, realizing carbon peak regulation and carbon neutrality. [ABSTRACT FROM AUTHOR]

Air pollution has emerged as a substantial threat to China's sustainable development. What remains overlooked is the resource dependence on the control of air pollution. At the same time, the resource curse may be broken by the growing digital economy, which will in turn reduce air pollution. To examine this problem, a total of 225 Chinese prefecture-level cities were selected from 2011 to 2018 to investigate the associations among resource dependence, the digital economy, and air pollution, and further, explore the mediating effect of income inequality and industrial upgrading. The primary findings are enumerated as follows: First, resource dependence is positively correlated to air pollution; second, the digital economy negatively regulates the effect of resource dependence on air pollution, that is, as the digital economy grows, air pollution decreases via resource dependence as a whole. However, a noteworthy phenomenon is that the negative moderating effect is evidently heterogeneous and is especially prominent in central China and non-resource-based cities; third, income inequality and industrial upgrading play a mediating role between resource dependence and air pollution; fourth, the digital economy can significantly influence the process where resource dependence affects air pollution via income inequality and industrial upgrading. These findings indicate the direction in the development of new strategies for different regions. Alleviating resource dependence is an important way of air pollution control. In particular, the main focus on mitigating air pollution should be adjusted to the level of income inequality and industrial structure. And the empirical results call for a strong emphasis on the digital economy for higher air quality in resource-based cities. [ABSTRACT FROM AUTHOR]

This review is aimed at researchers in air pollution control seeking to understand the latest advancements in volatile organic compound (VOC) removal. Implementing of plasma-catalysis technology for the removal of volatile organic compounds (VOCs) led to a significant boost in terms of degradation yield and mineralization rate with low by-product formation. The plasma-catalysis combination can be used in two distinct ways: (I) the catalyst is positioned downstream of the plasma discharge, known as the "post plasma catalysis configuration" (PPC), and (II) the catalyst is located in the plasma zone and exposed directly to the discharge, called "in plasma catalysis configuration" (IPC). Coupling these two technologies, especially for VOCs elimination has attracted the interest of many researchers in recent years. The term "synergy" is widely reported in their works and associated with the positive effect of the plasma catalysis combination. This review paper investigates the state of the art of newly published papers about catalysis, photocatalysis, non-thermal plasma, and their combination for VOC removal application. The focus is on understanding different synergy sources operating mutually between plasma and catalysis discussed and classified into two main parts: the effect of the plasma discharge on the catalyst and the effect of the catalyst on plasma discharge. This approach has the potential for application in air purification systems for industrial processes or indoor environments. [ABSTRACT FROM AUTHOR]

This paper examines the impact of air pollution control policies targeting key polluting enterprises, highlighting a strategic shift towards precision pollution control that concentrates on high-emission, high-risk businesses. The paper explores the efficacy of these policies and their potential spatial spillover effects, utilizing panel data from 259 Chinese cities from 2013 to 2021. Employing the difference-in-differences (DID) model and spatial Durbin model, the study analyzes both the direct local effects and the broader spatial consequences of these regulatory measures on air quality. The findings indicate a significant reduction in air pollutant concentrations in urban areas, attributing this improvement to factors such as industrial restructuring, increased investment in science and technology, and economic growth. Spatial econometric analysis further reveals a substantial positive correlation in air quality among Chinese cities. However, estimates of the spillover effect indicate that while such policies successfully reduce pollution locally, they could unintentionally degrade air quality in adjacent areas. The study highlights the need for nuanced policy strategies to mitigate unintended spatial spillovers and enhance overall effectiveness. It recommends tailored policies that integrate environmental and socioeconomic objectives, national and regional coordination for consistent enforcement, technology-driven compliance strategies, and incentives for sustainable enterprise practices. • Assessed impact of policies on key polluters in 259 Chinese cities. • Employed DID and spatial Durbin models for in-depth policy impact analysis. • Found significant reduction in urban air pollution from policies. • Revealed spatial spillover effects, aligning with "pollution haven" hypothesis. • Proposed nuanced approaches for local and spatial policy impacts. [ABSTRACT FROM AUTHOR]

Accurate prediction of air pollutant concentrations can be effective in controlling and preventing air pollution, which is crucial for both the government's policy response and the public's reasonable ability to avoid air pollution. In this study, we propose a model based on the principle of big data correlation and deep learning techniques for predicting the concentration of air pollutant concentrations. The proposed model, named STARes-SaLSTM, consists of a residual neural network with spatiotemporal attention (STAResNet) and a temporal self-attention based on long short-term memory network (SaLSTM). The spatiotemporal attention module in STAResNet enhances the original input information by weighting it in both spatial and temporal dimensions, meanwhile the features of the spatial distribution of pollutant and meteorological information across many cities are deeply extracted using ResNet. A novel self-attention method known as temporal self-attention is developed to extract the temporal dependency of air pollutants. LSTM is used as a sequence encoder in this temporal attention method to calculate queries, keys, and values to acquire a more comprehensive temporal dependence than is possible with normal self-attention. The STARes-SaLSTM model successfully predicts air pollution concentrations in the target city for the future by extracting spatiotemporal correlation of feature sequence. In comparison to previous neural network models and conventional methods, the model increases the accuracy of pollutant concentration prediction. The suggested prediction model works well for the one to 3 h prediction job, with a root mean square error (RMSE) ranging from 6.716 to 11.648. Furthermore, even for the 1–24 h prediction assignment, we executed multi-scale prediction in the target city and obtained a reasonable performance, with an average RMSE value of 20.576. • This paper solves the problem of difficult extraction of spatiotemporal features. • ResNet allows for the extraction of spatially relevant features; • The spatiotemporal attention mechanism is used to spatiotemporal extract features; • The temporal self-attention mechanism obtain temporal dependence of data. [ABSTRACT FROM AUTHOR]

[Display omitted] • Lack of functional groups on porous carbon surface results in low adsorption capacity for pollutants. • Doping heterogeneous atoms into porous carbon is the most effective methods to enhance functional groups on porous carbon. • Doping N atom can alter surface charge distribution and polarity of porous carbon and produce nitrogen functional groups. • AOTs can induce free radicals to doping O atom to porous carbon to produce rich O functional groups. • Multi-elements co-doping can achieve better enhancement effect than single element doping due to synergistic effect. Due to simple and clean process, and renewable adsorbent, adsorption of gaseous pollutants using porous carbon is a research hotspot in the field of air pollution control. However, the lack of highly active sites and functional groups on porous carbon surface results in low adsorption capacity for gaseous pollutants, hindering the development of this technology. Doping heterogeneous atoms into porous carbon is one of the most effective methods to enhance the surface active sites and functional groups of porous carbon. This paper reviews the latest research progress in doping heterogeneous atoms (e.g., N, O, S, P, halogens, etc.) into porous carbons and adsorption of gaseous pollutants (e.g, SO 2 , NO x , H 2 S, Hg0, VOCs, etc.) by heteroatom-doped porous carbons. Different heteroatom doping methods, doping effects and mechanisms, and applications are summarized. The adsorption performance and mechanism of heteroatom-doped porous carbons for different gaseous pollutants are also commented on. Results show that doping nitrogen atoms can alter the surface charge distribution and the polarity of porous carbon and produce nitrogen functional groups to improve the van der Waals, dispersive interaction, electrostatic interaction, etc. on porous carbon surface, thereby enhancing pollutants adsorption. Advanced oxidation technologies (AOTs) can induce highly active free radicals to doping oxygen atoms into porous carbon to produce rich oxygen functional groups, which have advantages of high efficiency and clean process, and thus have good development prospects. Multi-elements co-doping can achieve better enhancement effect than single element doping due to synergistic effect between multi-elements and different functional groups. Some suggestions and prospects for development of doping heterogeneous atoms and adsorption of gaseous pollutants using heteroatom-doped porous carbons are also discussed. [ABSTRACT FROM AUTHOR]

Many environmental variables, in particular, related to air or water quality, are measured in a limited number of points and often for a limited time span. This forbids the development of accurate models for interesting locations with missing or insufficient data and poses the question of whether a model developed for another measurement site can be reliably applied. Such a question is particularly critical when the model is entirely data-driven, such as a neural network. In this context, the paper proposes a procedure to evaluate the expected performance of an existing neural network model applied to a new unmonitored station. This transferability assessment is exemplified by the problem of forecasting ozone concentrations in different environmental settings around the Alpine Arc. Long Short-Term Memory (LSTM) neural network models are applied for predicting hourly concentrations in 20 stations of different types (urban, rural, and mountain). The analysis of the results allows us to determine the expected performance of such models in new cases and reduce the transferability uncertainty when the existing models can be partitioned into clusters. The LSTM models demonstrate the possibility of high accuracy in ozone forecasting at all sites. Given the significant impacts of this gas on human health and the environment, this can contribute to better decision-making and mitigation strategies for air pollution control. • We investigate the possibility of training a model in a site and reuse it elsewhere. • We introduce the concept of transferability matrix and transferability indicators. • The transfer learning reliability is enhanced by clustering the sites. • Ozone concentration forecasting for 20 locations in the Alpine region is considered. [ABSTRACT FROM AUTHOR]

The air pollution process and impact mechanism of megacities located in complex terrain are particularly complex. As a typical megacity in China, Chengdu has unique topographical and meteorological conditions, and its pollution control is difficult. This study systematically elucidated the variation characteristics of six criteria air pollutants in Chengdu between 2014 and 2020. Besides, the PM2.5/PM10 and NO2/SO2 ratios were discussed. Furthermore, a detailed analysis of the correlation between air pollutants was carried out. Finally, the collaborative path of carbon reduction and air pollution control is discussed. The results indicated that SO2, PM2.5, PM10 and CO were significantly decreased by 62.9%, 50.8%, 45.5%, and 36.7%, respectively. PM2.5 and O3 compliance rates are very low, and O3 increases with fluctuations. SO2, NO2, CO, PM showed a "U-shaped" seasonal variation, and there was a "seesaw" phenomenon between O3 and PM2.5. The continuous changing trends also found in the ratios of PM2.5/PM10 and NO2/SO2. The results highlight the importance of coordinated reduction of carbon emissions and pollutants in Chengdu. This research can improve the prediction accuracy of air pollution in complex terrain areas under global warming, and improve the understanding of the formation mechanism of air pollution in special terrains around the world. [ABSTRACT FROM AUTHOR]

Energy-consuming rights trading policies (ECRTPs) represent a significant institutional innovation for China aimed at achieving the dual control targets of total energy consumption and energy consumption intensity. However, the effectiveness of these policies in curbing air pollution remains uncertain. This study treats ECRTPs as a quasi-natural experiment to empirically analyze their impact on air pollution, utilizing panel data encompassing 277 prefecture-level cities in China covering the period from 2011 to 2021. Analytical methods applied include a Difference-in-Differences model, a mediation effects model, and a triple differences model to explore the effects of ECRTPs on air pollution. The findings reveal that ECRTP can significantly suppress air pollution, and this conclusion remains valid even after conducting robustness tests. Mechanism analysis indicates that ECRTPs suppress air pollution by boosting energy efficiency, advancing industrial structure upgrading, and facilitating technological innovation. Further heterogeneous studies show that ECRTPs have a more pronounced inhibitory effect on air pollution in cities that are economically and socially developed, exhibit greater energy-saving potential, are characterized as resource-based cities, and serve as key regions for the prevention and control of air pollution. The research conclusion provides empirical evidence and policy implications for evaluating the environmental effects of ECRTPs and further improving China's energy-consuming rights trading system, as well as offering references and guidance for other developing countries to put forward ECRTPs. [ABSTRACT FROM AUTHOR]

The objective of this umbrella review was to investigate comprehensive and synthesized evidence of the association between ambient air pollution and obesity based on the current systematic reviews and meta-analyses. Related studies from databases including PubMed, EMBASE, Web of Science, and the Cochrane Library, published before July 16, 2023, were considered in the analysis. All selected systematic reviews and meta-analyses were included in accordance with PRISMA guidelines. The risk of bias and the methodological quality were evaluated using the AMSTAR 2 tool. The protocol for this umbrella review was documented in PROSPERO with the registration number: CRD42023450191. This umbrella review identified 7 studies, including 5 meta-analyses and 2 systematic reviews, to assess the impacts of air pollutants on obesity. Commonly examined air pollutants included PM1, PM2.5, PM10, NO2, SO2, O3. Most of the included studies presented that air pollution exposure was positively associated with the increased risk of obesity. The impact of air pollution on obesity varied by different ambient air pollutants. This study provided compelling evidence that exposure to air pollution had a positive association with the risk of obesity. These findings further indicate the importance of strengthening air pollution prevention and control. Future studies should elucidate the possible mechanisms and pathways linking air pollution to obesity. [ABSTRACT FROM AUTHOR]

The effectiveness of national policies for air pollution control has been demonstrated, but the relative effectiveness of short-term emission reduction measures in comparison with national policies has not. Here we show that short-term abatement measures during important international events substantially reduced PM2.5 concentrations, but air quality rebounded to pre-event levels after the measures ceased. Long-term adherence to strict emission reduction policies led to successful decreases of 54% in PM2.5 concentrations in Beijing, and 23% in atmospheric nitrogen deposition in China from 2012 to 2020. Incentivized by "blue skies" type campaigns, economic development and reactive nitrogen pollution are quickly decoupled, showing that a combination of inspiring but aggressive short-term measures and effective but durable long-term policies delivers sustainable air quality improvement. However, increased ammonia concentrations, transboundary pollutant flows, and the complexity to achieving reduction targets under climate change scenarios, underscore the need for the synergistic control of multiple pollutants and inter-regional action. In response to the rebound of air pollution during large events in China, the authors propose that a combination of ambitious short-term measures and enduring long-term pollution control policies is a sustainable solution for improving air quality. [ABSTRACT FROM AUTHOR]

Research background: Cross-national sanctions are external factors with high uncertainty. The current situation of green innovation is not optimistic and the future of the uncertain development is worth exploring. Purpose of the article: This study intends to investigate how cross-national sanctions affect green innovations by examining data from 130 countries from 1990 to 2020. We use six different forms of sanctions (including those from the U.S., EU, UN, as well as unilateral, plurilateral, and economic sanctions) as the primary explanatory variables, with environmental management innovations serving as a proxy for green innovations. Methods: Difference-in-Difference (DID) technique; Logit model. Findings & value added: The number of environmental management inventions produced by a nation's inventors is negatively affected by plurilateral and economic sanctions, as well as by the US and the EU sanctions, while other types of sanctions have weaker effects. In terms of sub-indicators, plurilateral and the US sanctions have a serious negative impact on air pollution control and water pollution control inventions, while plurilateral, The EU and economic sanctions have a negative impact on waste management innovations. However, unilateral and the UN sanctions have no apparent effect on any type of environmental technology. Further logit model tests show that the US and economic sanctions lead to a decrease in green innovations. Moreover, the difference between Asian and African countries is that the negative effects of plurilateral, the U.S., the EU, and economic sanctions are more obvious in African countries, while cross-national sanctions have no significant impact on Asian countries. The results of our study should help countries under sanctions to respond more effectively to environmental progress. [ABSTRACT FROM AUTHOR]

Most existing coal-fired power plants were designed for sustained operation at full load to maximize efficiency, reliability, and revenue, as well as to operate air pollution control devices at design conditions. Depending on plant type and design, these plants can adjust output within a fixed range in response to plant operating or market conditions. The need for flexibility driven by increased penetration of variable and non-dispatchable power generation, such as wind and solar, is shifting the traditional mission profile of thermoelectric power plants in three ways: more frequent shutdowns when market or grid conditions warrant, more aggressive load ramp rates (rate of output change), and a lower minimum sustainable load, which provides a wider operating range and helps avoid costly plant shutdowns. Recent studies have shown that the flexibility of a coal-fired power plant can be improved by energy storage. The objective of this work was to analyze a set of energy storage options and determine their impact on the flexibility and economics of a representative coal-fired power plant. The effect of three energy storage systems integrated with a coal power plant on plant flexibility and economics was investigated. The results obtained in this project show that energy storage systems integrated with a thermal power plant improve plant flexibility and participation in the energy and ancillary services markets, which improves plant financial performance. The study was funded by the U.S. Department Office of Fossil Energy FE-1 under award number DE-FE0031903. [ABSTRACT FROM AUTHOR]

• The mechanism of SO 2 adsorption, sulfation, and migration on different catalysts is summarized. • The regeneration methods of sulfur-poisoned catalysts are compared. • Anti-sulfur poisoning modification methods for diverse catalysts are discussed. • Future research directions on the SO 2 resistance of VOCs catalysts are proposed. Emissions of volatile organic compounds (VOCs) cause serious hazards to human health and environmental quality and have become the main focus of air pollution control in many countries and around the world. Catalytic oxidation is widely used to control emissions of VOCs, and the catalysts are the core of catalytic oxidation technology. The presence of trace sulfur species in the actual waste gas inevitably causes toxicity to the catalysts. This phenomenon affects the removal efficiency of VOCs and significantly increases the operating cost, limiting the application of catalytic oxidation technology. This paper first discusses the effect of sulfur dioxide on VOCs oxidation catalysts. The detailed processes of SO 2 adsorption, migration, and conversion on catalysts are summarized. The regeneration methods of sulfur poisoning catalysts are discussed, and various methods for the development of anti-sulfur poisoning catalysts, such as element doping and acidification, are summarized. Finally, the effects of some process parameters on catalyst sulfur poisoning in practical industrial applications are concluded. It provides a reference value for future anti-sulfur poisoning strategies, mechanism analysis, and practical industrial applications of VOCs oxidation catalysts. [ABSTRACT FROM AUTHOR]

This editorial discusses the issue of air pollution and its impact on public health and economic costs. The World Health Organization recognizes air pollution as a major environmental health risk, causing millions of premature deaths annually. The editorial highlights 17 articles that provide valuable insights into the interdisciplinary aspects of air pollution, covering topics such as air quality assessment, economic growth, and spatial effects. The research emphasizes the importance of understanding the sources and impacts of air pollution to develop effective strategies for mitigation and improve public health. The article also explores the relationship between economic growth, industrial pollution, and environmental degradation, particularly in China. The findings contribute to a deeper understanding of air pollution and provide valuable insights for policymakers, researchers, and practitioners. The document concludes by discussing the need to reduce harmful emissions and improve air quality for the sake of public health and sustainable development. Additionally, the article summarizes various studies on the impact of air pollution on human health, including its association with metabolic syndrome, negative effects on semen quality, and increased risk of liver cancer. The research highlights the economic and welfare costs of air pollution and the importance of implementing effective policies to mitigate its adverse health effects. [Extracted from the article]

Accurate air quality prediction is paramount in safeguarding public health and addressing air pollution control. However, previous studies often ignore the geographic similarity among different monitoring stations and face challenges in dynamically capturing different spatial–temporal relationships between stations. To address this, an air quality predictive learning approach incorporating the Third Law of Geography with SAM–CNN–Transformer is proposed. Firstly, the Third Law of Geography is incorporated to fully consider the geographical similarity among stations via a variogram and spatial clustering. Subsequently, a spatial–temporal attention convolutional network that combines the spatial attention module (SAM) with the convolutional neural network (CNN) and Transformer is designed. The SAM is employed to extract spatial–temporal features from the input data. The CNN is utilized to capture local information and relationships among each input feature. The Transformer is applied to capture time dependencies across long-distance time series. Finally, Shapley's analysis is employed to interpret the model factors. Numerous experiments with two typical air pollutants (PM2.5, PM10) in Haikou City show that the proposed approach has better comprehensive performance than baseline models. The proposed approach offers an effective and practical methodology for fine-grained non-stationary air quality predictive learning. [ABSTRACT FROM AUTHOR]

This study uses bibliometric techniques and social network analysis to evaluate 318 publications on air quality management in cities worldwide. The relevant data were retrieved from the Scopus database and analyzed using the VOSviewer 1.6.7 tools. The analysis included the number of publications, citation analyses, research study area analyses, and the most common keywords from 1975 to November 6, 2022. In addition, based on the results of the cluster analysis, we developed co-occurrence networks that enable a more specific keyword classification. The visualization showed the existing relationships between key terms, research study areas, and publications dealing with air quality management in cities. China, the United States, and the United Kingdom led in terms of the number of scientific publications and overall strength of connections during the study period. The most productive journal was Science of the Total Environment, followed by Atmospheric Environment, and the International Journal of Environmental Research and Public Health. Several limitations of the study are described and recommendations are made for future studies to increase the scope of studies and facilitate urban air quality management. [ABSTRACT FROM AUTHOR]

Air quality forecasting is an important application area of the time series forecasting problem. The successful prediction of the air quality of a place well in advance can able to help administrators to take the necessary steps to control air pollution. The administrator can also warn the citizens about the adverse effect of air pollution in advance. In this study, an air quality forecasting method is proposed to successfully forecast the air quality of a place. Here the type-2 fuzzy time series (FTS) forecasting method is applied to predict air quality. The performance of any FTS heavily depends on the selection of its hyperparameters. In this letter, a fuzzy time series optimization (FTSBO) algorithm is proposed to optimize all the hyperparameters of the FTS forecasting method. The proposed FTSBO algorithm originated from the butterfly optimization technique. In this work, the performance of the proposed forecasting method is also compared to the well-known forecasting methods. The simulation results established that the proposed forecasting method produces satisfactory performance, and its performance is better in comparison to other well-known forecasting methods. [ABSTRACT FROM AUTHOR]

Communities all around the world have a growing concern and need to make it possible for people to walk more frequently, to more places, and to feel safe while doing so, whether for leisure or practical reasons. Increasing the number of individuals who walk will improve physical health, reduce air pollution and traffic congestion, and give support for more walkable communities and neighborhoods, among other benefits. a neighborhood that is walkable, prioritizes people over cars, and is constructed on a human scale. It promotes a diverse, dynamic, enjoyable, healthy, and cozy setting for living and strolling. It reinstates rights for those who are most in need, with a special emphasis on kids and those with disabilities. Where stores and services in a community that is walkable are accessible by foot. By creating walkable towns and neighborhoods the number of car trips and the number of people who use them will be reduced, which will reduce air pollution, noise pollution, and CO2 emissions. The wellbeing of the residents will undoubtedly change, and their social lives will be improved, if there are fewer cars on the street and more green spaces, pedestrian walkways, and bicycle lanes are constructed on a portion of the street. Walking communities are less likely to be classified as "unsafe communities." Walking people inside the street and reducing the number of cars on the road will boost natural surveillance and reduce the frequency of walking and driving accidents. This study will address how walkable communities can improve welfare and pedestrian safety by promoting walking behavior that perceive walkability and its relation to safety, as well as the importance of public awareness policies that promote walkability [ABSTRACT FROM AUTHOR]

Atmosphere pollution is a burning issue in Pakistan and therefore getting substantial attention at both the national and international levels. It is not only confined to the cities, but the small towns in Pakistan also suffer from this problem. However, very little information exists regarding public awareness and willingness to pay (WTP) for atmosphere pollution, particularly among the inhabitants of towns. This study aims to investigate the role of public awareness and WTP in the elimination of atmosphere pollution via a well-structured interview schedule. The interval regression model is used to analyze the data. The analysis shows that radio, television, and newspapers are the prime sources of information regarding atmosphere pollution in the study area. The attitude of the respondents concerning the severity of atmosphere pollution, only 46.0 percent declares pollution in towns as 'moderately severe.' Regarding the sources of pollution, 48 percent of the respondents attribute vehicular emissions besides industrial emissions to atmosphere pollution. The environment-friendly measures that respondents favored include the use of public transportation (37.7%) and solar equipment (27.7%) followed by the carbon tax and personal car sharing. There exists a significant difference between the WTP for self-protective measures before and after the dissemination of information. The analysis further demonstrates that the WTP to support the control and prevention of air pollution after the dissemination of information is significantly higher than before the dissemination of such information. However, respondents tend to pay more for self-protective measures than to support the control and prevention measures. Alternatively, this may indicate that inhabitants of the study area want the government to play a dominant role in controlling atmosphere pollution and preventive actions. Among the various socio-economic factors, the level of education and income significantly increase the WTP to eliminate atmosphere pollution. This study is important to better understand public awareness and WTP to prevent atmosphere pollution in Pakistan. It could also contribute to activating positive public participation to control atmosphere pollution and its management to move toward sustainable development. [ABSTRACT FROM AUTHOR]

Forecasting air quality plays a crucial role in preventing and controlling air pollution. It is particularly significant for improving preparedness for heavily polluted weather conditions and ensuring the health and safety of the population. In this study, a novel deep learning model for predicting air quality spatio-temporal variations is introduced. The model, named graph long short-term memory with multi-head attention (GLSTMMA), is designed to capture the temporal patterns and spatial relationships within multivariate time series data related to air quality. The GLSTMMA model utilizes a hybrid neural network architecture to effectively learn the complex dependencies and correlations present in the data. The extraction of spatial features related to air quality involves the utilization of a graph convolutional network (GCN) to collect air quality data based on the geographical distribution of monitoring sites. The resulting graph structure is imported into a long short-term memory (LSTM) network to establish a Graph LSTM unit, facilitating the extraction of temporal dependencies in air quality. Leveraging a Graph LSTM unit, an encoder-multiple-attention decoder framework is formulated to enable a more profound and efficient exploration of spatio-temporal correlation features within air quality time series data. The research utilizes the 2019–2021 multi-source air quality dataset of Qinghai Province for experimental assessment. The results indicate that the model effectively leverages the impact of multi-source data, resulting in optimal accuracy in predicting six air pollutants. [ABSTRACT FROM AUTHOR]

Since China's clean air action, PM 2.5 (particulate matter with an aerodynamic equivalent diameter of 2.5 µm or less) air quality has improved, while ozone (O 3) pollution has become more severe. Here we apply a coupled meteorology–chemistry model (WRF-Chem: Weather Research and Forecasting model coupled to Chemistry v3.7.1) to quantify the responses of aerosol–radiation interaction (ARI) to anthropogenic emission reductions from 2013 to 2017, including aerosol–photolysis interaction (API) related to photolysis rate change and aerosol–radiation feedback (ARF) related to meteorological field change and their contributions to O 3 increases over eastern China in summer and winter. Sensitivity experiments show that the decreased anthropogenic emissions play a more prominent role in the increased daily maximum 8 h average (MDA8) O 3 in both summer (+ 1.96 ppb vs. + 0.07 ppb) and winter (+ 3.56 ppb vs. - 1.08 ppb) than the impacts of changed meteorological conditions in urban areas. The decreased PM 2.5 caused by emission reductions can result in a weaker impact of ARI on O 3 concentrations, which superimposes its effect on the worsened O 3 air quality. The weakened ARI due to decreased anthropogenic emissions aggravates the summer (winter) O 3 pollution by + 0.81 ppb (+ 0.63 ppb), averaged over eastern China, with weakened API contributing 55.6 % (61.9 %) and ARF contributing 44.4 % (38.1 %), respectively. This superimposed effect is more significant for urban areas during summer (+ 1.77 ppb). Process analysis indicates that the enhanced chemical production is the dominant process for the increased O 3 concentrations caused by weakened ARI in both summer and winter. This study innovatively reveals the adverse effect of weakened aerosol–radiation interaction due to decreased anthropogenic emissions on O 3 air quality, indicating that more stringent coordinated air pollution control strategies should be implemented for significant improvements in future air quality. [ABSTRACT FROM AUTHOR]

Background: There is an increasing body of evidence associating short-term ambient nitrogen dioxide (NO2) exposure with asthma-related hospital admissions in children. However, most studies have relied on temporally resolved exposure information, potentially ignoring the spatial variability of NO2. We aimed to investigate how daily NO2 estimates from a highly resolved spatio-temporal model are associated with the risk of emergency hospital admission for asthma in children in England. Methods: We conducted a time-stratified case-crossover study including 111,766 emergency hospital admissions for asthma in children (aged 0– 14 years) between 1st January 2011 and 31st December 2015 in England. Daily NO2 levels were predicted at the patients' place of residence using spatio-temporal models by combining land use data and chemical transport model estimates. Conditional logistic regression models were used to obtain the odds ratios (OR) and confidence intervals (CI) after adjusting for temperature, relative humidity, bank holidays, and influenza rates. The effect modifications by age, sex, season, area-level income deprivation, and region were explored in stratified analyses. Results: For each 10 μg/m³ increase in NO2 exposure, we observed an 8% increase in asthma-related emergency admissions using a five-day moving NO2 average (mean lag 0– 4) (OR 1.08, 95% CI 1.06– 1.10). In the stratified analysis, we found larger effect sizes for male (OR 1.10, 95% CI 1.07– 1.12) and during the cold season (OR 1.10, 95% CI 1.08– 1.12). The effect estimates varied slightly by age group, area-level income deprivation, and region. Significance: Short-term exposure to NO2 was significantly associated with an increased risk of asthma emergency admissions among children in England. Future guidance and policies need to consider reflecting certain proven modifications, such as using season-specific countermeasures for air pollution control, to protect the at-risk population. [ABSTRACT FROM AUTHOR]

This "Policy Brief" explores the potential integration of Aerosol Optical Depth (AOD) into the United Kingdom's air quality and public health monitoring frameworks, highlighting its potential to enhance existing air pollution control strategies. Amid growing concerns over air pollution's impact on health, particularly the link between particulate matter and lung cancer, this brief presents a focused investigation into how AOD can be leveraged alongside traditional monitoring methods to provide a more nuanced understanding of air quality trends. By correlating AOD data with lung cancer incidence rates within the UK, the brief aims to uncover potential associations and inform public health decisions. Furthermore, it discusses the advantages and limitations of employing AOD in air pollution and respiratory disease monitoring, advocating for a strategic enhancement of the UK's air pollution monitoring efforts. This approach seeks to complement and refine current monitoring practices with advanced remote sensing techniques, aiming to inform policy innovations that prioritize environmental health and public welfare. Through a comprehensive review of existing data and policies, the brief underscores the urgency of adopting multidimensional air quality management strategies that respond to technological advancements and emerging public health needs. [ABSTRACT FROM AUTHOR]

The present work is dedicated to the study of multiphase turbulent and three-dimensional rotational flow in dust cyclones, a contribution to air pollution control. Cyclones are widely used devices for the separation of constituents from solid-gas mixtures in industry. In order to improve the filtration efficiency of cyclones, and to reduce the pressure drop, parametric numerical simulation studies using the Fluent code have been conducted to characterise the effects of the parameters affecting the operation of these devices through their performance indicators. In this work, the effect of inlet velocity and the particle size on the turbulent flow air in the cyclone is presented. Numerical simulation of the flow by Fluent code using three numerical models: the first based on the dissipation of kinetic energy by viscosity (RNG) K-epsilon and standard K-epsilon as well as the last based on the solution of Reynolds stress equations (RSM), combined with the multiphase mixing model, gave interesting results in terms of the pressure and flow field in the separator, the variation of inlet velocity, and the variation of particle size. Validation with experimental and empirical results showed the advantage of the Reynolds stress turbulence model (RSM) over the standard K-epsilon and RNG K-epsilon. The RSM model better captures physical phenomena in an intense vortex flow in the presence of walls. But it is characterised by a very long calculation time and requires large machine resources. An alternative to this model is RNG K-epsilon model, which offers a reasonable calculation time with acceptable results (maximum deviation of 5 %) for speed values below 10 m/s. In the absence of numerical resources, certain empirical models such as those of First (for the evaluation of pressure drop) and Iozia and Leith (for evaluation of efficiency) may well be useful for the dimensioning of the cyclone. [ABSTRACT FROM AUTHOR]

Despite the long-term implementation of air pollution control policies in northeast China, severe haze pollution continues to occur frequently. With the adoption of a megacity (Changchun) in northeast China, we analysed the vertical characteristics of aerosols and the causes of aerosol pollution throughout the year using multisource data for providing recommendations for controlling pollution events (i.e., straw burning and fireworks). Based on a ground-based LiDAR, it was found that the extinction coefficient (EC) of aerosols at a height of 300 m in Changchun was highest in winter (0.44 km−1), followed by summer (0.28 km−1), with significant differences from those in warmer regions, such as the Yangtze River Delta. Therefore, it is recommended that air pollution control policies be differentiated between winter and summer. On Chinese New Year's Eve in Changchun, the ignition of firecrackers during the day and night caused increases in the EC at a height of 500 m to 0.37 and 0.88 km−1, respectively. It is suggested that the regulation of firecracker ignition should be reduced during the day and strengthened at night. Based on the CALIPSO and backward trajectory analysis results, two events of dust–biomass-burning composite pollution were observed in March and April. In March, the primary aerosol component was dust from western Changchun, whereas in April, the main aerosol component was biomass-burning aerosols originating from northern and eastern Changchun. Hence, reducing the intensity of spring biomass burning can mitigate the occurrence of dust–biomass-burning composite pollution. These findings can provide emission policy suggestions for areas facing similar issues regarding biomass-burning transmission pollution and firework emissions. [ABSTRACT FROM AUTHOR]

The pervasive issue significantly affects both the environment and public health. This shows that there is an urgent need for effective air pollution control measures within the community. In order to address the issue, this research has developed a groundbreaking prototype known as the Air Quality Monitoring Device. AQM Dev has represented an innovative approach to air quality monitoring with several distinct advantages, including power consumption through a solar panel, real-time data transmission through the internet, customizable sensor integration, portability, an extensive air quality database, and a web-based display of air quality data. The device has measured several crucial parameters, comprising PM10, CO, NO2, and O3. AQM Dev consisted of essential components, such as sensors PMS5003, FC22, mics6814, a sensor reader Arduino, a data transmitter Mikrotik RB951 router and Modem, and a power source Solar Panel and related equipment. The data collected by AQM Dev has been transmitted to a cloud server, where it has been subjected to reception and processing through software components, including Node-RED and MySQL. In summary, this research has yielded an effective prototype known as AQM Dev for air quality monitoring. The device has efficiently collected and transmitted air quality parameter data from its sensors to a cloud server, contributing to better air quality control. [ABSTRACT FROM AUTHOR]

Condensable particulate matter (CPM) is different from filterable particulate matter (FPM), which could escape from air pollution control devices (APCDs) and pose a great threat to the environment and human health. Thus, modeling and experimental studies were conducted on CPM particle behavior and removal, in a cold electrode electrostatic precipitator (CE−ESP) coupled with a electric field, temperature field and concentration field. A multi−field force coupling model was then established that was based on the mechanical behavior of particles inside the CE−ESP. The results showed that temperature field was beneficial to depositing small size particles and that, the greater the temperature gradient, the higher CPM's removal efficiency. While the electric field tended to gather larger size particles, the greater the voltage provided, the higher the removal efficiency for CPM and FPM. In the multi−field, the augmented coagulation and the removal efficiencies of both CPM and FPM increased significantly, reaching 89% and 98%, respectively. Subsequently, experiments were conducted by a self−made CE−ESP device, which showed the removal efficiencies of CE−ESP of a CPM in a multi−field were 91% and 81% for a coal−fired power plant and a waste incineration plant, respectively. This research could make a great contribution to CPM condensation, aggregation and removal. [ABSTRACT FROM AUTHOR]

Despite significant improvements in air quality in recent years, the Sichuan Basin (SCB) is still facing frequent haze pollution in winter, and the causes of severe haze formation have not yet been fully investigated. In this study, the chemical components of PM 2.5 (i.e., particulate matter with an aerodynamic diameter of less than 2.5 µ m) in a typical pollution period at the beginning of 2023 in Chengdu, a megacity in the SCB, were characterized by bulk-chemical and single-particle analysis, and the PM 2.5 sources and formation mechanism of pollution were analyzed. The average mass concentration of PM 2.5 during the study period was 95.6 ± 28.7 µ g m -3. Organic matter (OM) was the most abundant component (35.3 %), followed by nitrate (22.0 %), sulfate (9.2 %) and ammonium (7.8 %). The individual aerosol particles were classified into five categories, i.e., mineral, OM, secondary inorganic aerosol (SIA), soot and fly ash/metal particles, and most of them were in the state of being internally mixed. The entire observation period could be divided into two non-pollution periods (NP-1 and NP-2) and two haze periods (Haze-1 and Haze-2). With the evolution of pollution, the bulk-chemical and single-particle analysis exhibited similar characteristics, suggesting that Haze-1 was mainly caused by pollutants related to fossil fuel combustion, especially local mobile sources, while Haze-2 was triggered by the rapidly increasing secondary pollutants, which mainly came from regional transmission. The PM 2.5 sources included dust (8.5 %), biomass burning (3.5 %), coal combustion (15.4 %), industrial processes (6.5 %), vehicular emissions (25.6 %) and secondary sources (40.5 %). Analysis of Weather Research and Forecasting model with Chemistry (WRF-Chem) model results showed that the average contributions of local sources and regional transmission to pollution in Chengdu were the same (50 % vs. 50 %). In addition, the source composition and WRF-Chem simulation results in different periods confirmed our analysis of the formation mechanisms of the two haze events. This study confirms that further significant reductions in PM 2.5 in Chengdu are still needed, and more effective policies for local emission reduction or joint prevention and control of regional air pollution will be necessary in the future. [ABSTRACT FROM AUTHOR]

Layered double hydroxides (LDHs) are often used as adsorbents and catalysts in pollutant treatment because of their unique layered structure and chemical properties. The control of gaseous pollutants is an important step in environmental pollution control. In this paper, the research status of LDHs in the capture and transformation of typical gaseous pollutants (SO 2 , NO x , VOCs, CO 2) is reviewed, especially the purification mechanism and application advantages of these materials are expounded, and the future development direction of this material is predicted and analyzed. [Display omitted] [ABSTRACT FROM AUTHOR]