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Imported air pollution has a significant impact on urban air quality. Relevant studies have shown that many urban air pollution events are not resourced by local emissions but are imported by air pollution from surrounding areas transported across regions. The prevention and control of air pollution is very necessary. However, the existing supervision of urban air quality mostly relies on ground monitoring stations, which are extremely limited in time and space, and cannot satisfy continuous time-space air pollution research. Therefore, aiming at the problem of urban air pollution control, this paper used MERRA-2 reanalysis data and ground monitoring data to establish a "Time-Longitude-Latitude" three-dimensional pollution curve, and then a genetic algorithm was used to optimize its fitting. This study finally reconstructed the imported air pollution transmission route. This paper takes an air pollution event that occurred in Xuzhou City, China, on 12 January 2020, as an example. Through the analysis of aerosol optical depth (AOD), particulate matter (PM), wind speed, and other factors, we found the source, transmission route, and impact time of this pollution. We have verified the correctness and accuracy of the reconstructed contamination transport paths. It is proved that the method is universal and it can quickly and accurately restore the air pollution transmission route and identify the urban imported air pollution transmission entrance. This method will also provide strong data support for the division of responsibilities of environmental protection departments in various regions for severe air pollution transmission events and provide effective governance ideas for the prevention and control of imported air pollution in recipient cities. [ABSTRACT FROM AUTHOR]

The market-oriented reform of China's power market has gradually transformed power prices from government pricing to market regulation, which not only promotes the production efficiency of industrial enterprises, but also inhibits the excessive consumption and waste of power by residential power users. This paper uses the data from 2006–2018 combined with the precious industrial power price data and macroeconomic data of 100 cities in China, takes the marketization reform of the power market in 2015 as a quasi-natural experiment, and uses the difference-in-differences model to empirically study the causal relationship between power market reform and air pollution for the first time. The study found that power market reform can reduce air pollution, and this conclusion is also supported by a number of robustness tests. Mechanism analysis shows that power market reform can reduce air pollution by improving power market efficiency, promoting technological progress, and reducing power consumption. Heterogeneity analysis shows that power market reform can suppress air pollution more significantly in eastern regions, regions with severe air pollution, and regions with larger populations. This paper not only provides new research perspectives and research ideas for air pollution prevention and control, but also provides empirical evidence for the positive externalities of power market reform. [ABSTRACT FROM AUTHOR]

PM2.5 has a non-negligible impact on visibility and air quality as an important component of haze and can affect cloud formation and rainfall and thus change the climate, and it is an evaluation indicator of air pollution level. Achieving PM2.5 concentration prediction based on relevant historical data mining can effectively improve air pollution forecasting ability and guide air pollution prevention and control. The past methods neglected the impact caused by PM2.5 flow between cities when analyzing the impact of inter-city PM2.5 concentrations, making it difficult to further improve the prediction accuracy. However, factors including geographical information such as altitude and distance and meteorological information such as wind speed and wind direction affect the flow of PM2.5 between cities, leading to the change of PM2.5 concentration in cities. So a PM2.5 directed flow graph is constructed in this paper. Geographic and meteorological data is introduced into the graph structure to simulate the spatial PM2.5 flow transmission relationship between cities. The introduction of meteorological factors like wind direction depicts the unequal flow relationship of PM2.5 between cities. Based on this, a PM2.5 concentration prediction method integrating spatialtemporal factors is proposed in this paper. A spatial feature extraction method based on weight aggregation graph attention network (WGAT) is proposed to extract the spatial correlation features of PM2.5 in the flow graph, and a multi-step PM2.5 prediction method based on attention gate control loop unit (AGRU) is proposed. The PM2.5 concentration prediction model WGATAGRU with fused spatiotemporal features is constructed by combining the two methods to achieve multi-step PM2.5 concentration prediction. Finally, accuracy and validity experiments are conducted on the KnowAir dataset, and the results show that the WGAT-AGRU model proposed in the paper has good performance in terms of prediction accuracy and validates the effectiveness of the model. [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]

The application of new energy systems for industrial production to advance air pollution prevention and control has become an irreversible trend. This development includes hybrid systems consisting of reversible solid oxide cells (RSOC) and a Li-ion battery; however, at present the energy dispatching of such systems has an unstable factor in the form of poor heat/electricity/gas controllability. Therefore, the system studied in this paper uses the Li-ion battery as the energy supply/storage case, and uses the RSOC to supply power for the Li-ion battery charge or the Li-ion battery supply power to the RSOC for hydrogen production by water electrolysis. In this hybrid system, Li-ion battery thermoelectric safety and RSOC hydrogen production stability are extremely important. However, system operation involves the switching of multiple operating conditions, and the internal thermoelectric fluctuation mechanism is not yet clear. Therefore, in this paper we propose a separate control with a dual mode for hybrid systems. Active disturbance rejection control (ADRC) with a simple structure is used to achieve Li-ion battery module thermoelectric safety and control the hydrogen production/consumption of the RSOC module in the hybrid system. The results show that the required Li-ion battery thermoelectric safety and RSOC hydrogen consumption/production requirements can be met using the proposed controller, leading to a hybrid system with high stability control. [ABSTRACT FROM AUTHOR]

Timely short-term spatial air quality forecasting is essential for monitoring and prevention in urban agglomerations, providing a new perspective on joint air pollution prevention. However, a single model on air pollution forecasting or spatial correlation analysis is insufficient to meet the strong demand. Thus, this paper proposed a complex real-time monitoring and decision-making assistance system, using a hybrid forecasting module and social network analysis. Firstly, before an accurate forecasting module was constructed, text sentiment analysis and a strategy based on multiple feature selection methods and result fusion were introduced to data preprocessing. Subsequently, CNN-D-LSTM was proposed to improve the feature capture ability to make forecasting more accurate. Then, social network analysis was utilized to explore the spatial transporting characteristics, which could provide solutions to joint prevention and control in urban agglomerations. For experiment simulation, two comparative experiments were constructed for individual models and city cluster forecasting, in which the mean absolute error decreases to 7.8692 and the Pearson correlation coefficient is 0.9816. For overall spatial cluster forecasting, related experiments demonstrated that with appropriate cluster division, the Pearson correlation coefficient could be improved to nearly 0.99. [ABSTRACT FROM AUTHOR]

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]

The PM2.5 index is an important basis for measuring the degree of air pollution. The accurate prediction of PM2.5 concentration has an important guiding role in air pollution prevention and control. The Pearson Correlation Coefficient (PCC) is a common index used to mine the correlation between meteorological factors and other air pollutants. However, this index cannot be used to mine non-linear correlations, nor can it quantitatively analyze the weight of each related attribute. In order to accurately explore the correlation between meteorological factors and other air pollutants and to achieve an accurate prediction of PM2.5 concentration, this paper proposes a short- and long-time memory (LSTM) network prediction model based on Copula entropy (CE) and the adaptive genetic algorithm (AGA). By calculating CE, the correlation between multiple meteorological factors and various atmospheric pollutants and PM2.5 was analyzed. The correlation of influencing factors was sorted according to the size of the correlation coefficients. The contribution rate of meteorological factors and atmospheric pollutants to PM2.5 concentration was determined, used as the weight of each influencing factor and predicted as the input data of the prediction model. In this paper, a long- and short-term memory network (LSTM) suitable for time series data was selected as the prediction model, while the selection of model parameters was taken into account, and the relevant parameters were sought by an adaptive genetic algorithm (AGA). The air pollutant data and meteorological data of Beijing from 1 January 2016 to 31 December 2016 were selected, and MAE and RMSE were used as evaluation indexes. By comparing the experimental results of the CE-AGA-LSTM with those of other eight prediction models (LR, SVM, RF, ARMA, ST-LSTM, LSTM, CE-LSTM and CE-RNN), we found that among the models, the CE-AGA-LSTM model provided the lowest MAE and RMSE values, i.e., 14.5 and 21.88, respectively. At the same time, the loss rate and accuracy of the CE-AGA-LSTM model were evaluated, and the experimental results verified the validity of the model. [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]

The article discusses the Copenhagen Consensus, an international group of economists led by Danish environmentalist Bjorn Lomborg, which deliberates on how to address major challenges facing the world. In 2004, this consensus compiled a list of ten challenges facing the world, which included climate changes, communicable diseases, and conflicts and arms proliferation. Climate changes was placed at the bottom of the list, due to the perception that expenditures would be greater than benefits. The Copenhagen Consensus met again in 2008 and fashioned ten new challenges facing the world, including air pollution, global warming, and malnutrition. The process is analyzed and discussed.

The criteria-based sand and dust weather determination method has the problem ofbeing a cumbersome and time-consuming process when processing a large amount of raw data, and cannot avoid the problems of repeatability and reproducibility. On the basis of statistical analysis of the air automatic monitoring data in the cities affected by sand and dust, this paper proposes a k-means optimization algorithm (MDPD-k-means) based on maximum density and percentage distance, which can quickly filter the characteristic data of sand and dust in a short time, and identify the days affected by sand and dust. This method effectively improves the data processing efficiency, solves the problems of poor reproducibility and large artificial error of traditional methods, and can support the business application of sand and dust data elimination. This paper uses the method to identify the sand and dust data of 10 cities in Shaanxi Province from 2016 to 2022, determines a total of 1107 sand and dust days, and points out that the number of days affected by sand and dust is increasing year by year. After excluding the effect of sand and dust, the urban PM10 concentration decreases by 18.42~1.41% respectively, which provides important data information for accurately evaluating the effectiveness of air pollution prevention and control. [ABSTRACT FROM AUTHOR]

In order to achieve prevention and control of air pollution through energy consumption adjustment in advance, the paper proposes an Fuzzy Cognitive Map (FCM) of various energy resources affecting air quality, an incremental prediction algorithm of FCM and gradient descending method used to learn the FCM based on the small sample data on various energy consumptions and concentration of air pollutants. The FCM as an interpretable prediction method not only can predict future air quality more accurately, but also can analyze and interpret the affecting of various energy types on the future air quality. As the time delay of various energy consumptions affecting concentration of air pollutants, the quantitative time sequence influencing relationships (causality) in the FCM is mined directly from these data, and the air quality affected by various types of energy consumptions is predicted based on the FCM. Accordingly, the energy types affecting air pollution can be obtained for prior decision of energy consumption structure adjustment. The experimental results in Beijing-Tianjin-Hebei show that the FCM modeling is better than Support Vector Regression (SVR), Linear Regression (LR), Principal Component Analysis (PCA)-based forecasting, Convolutional Neural Networks (CNN) and Long-Short Term Memory (LSTM) methods in predicting air quality affected by energy resources, meanwhile according to the interpretable prediction results of the FCM, we obtain some interesting results and suggestions on energy consumption types in Beijing-Tianjin-Hebei regions in advance. Implications: At present, China's air pollution control has entered the deep-water area, and the biggest challenge is how to adjust the energy (consumption) structure. Therefore, this study completed the two important tasks: (1) driven by small sample data of energy consumptions, the paper provides an interpretable prediction model and method with better performance to achieve prevention and control of air pollution through energy consumption adjustment in advance; (2) according to the interpretable prediction results, the paper obtains some interesting results used to guide energy consumption adjustment in Beijing-Tianjin-Hebei regions. This study will provide beneficial suggestions and strategies for air pollution prevention and control in Beijing-Tianjin-Hebei, will help improve the air quality and energy consumption structure in Beijing-Tianjin-Hebei, and also can be extended to other regions. [ABSTRACT FROM AUTHOR]

This paper reports an analysis using the computable general equilibrium (CGE) model to calculate the economic development and emissions under different air pollution prevention strategies, with a focus on the Beijing-Tianjin-Hebei rim surrounding the cities in the 'Capital Economic Circle'. It appeared that raising the emission tax effectively suppressed air pollution but also suppressed economic development. Raising the emission subsidy promoted economic development, but did not suppress air pollution. A stepwise tax collection mode can be used to formulate the emission tax. When using emission subsidies to encourage manufacturers to improve their exhaust treatment technology, the policy should be tilted towards small enterprises to reduce economic pressure on them. [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]

Schools are increasingly positioned as sites for intervening on the bodies and minds of children in the here and now in order to bring about health gain for the future. Public health interventions for schools bring together coalitions of commercial, statutory and philanthropic actors with children and their teachers and carers. Drawing on ethnographic case studies in London, UK, this paper explores two such interventions: one aiming to increase levels of physical activity and one to reduce exposure to air pollution. Both interventions not only evoke care for children's own current and future wellbeing but also fold in imaginaries of collective health futures, which orient and legitimise particular intervention logics and actions. As interventions unfold, children are recruited as monitors of health risks in the present. They are also positioned as risk ambassadors, who will leverage care about unhealthy environments and lifestyles across space, to risky domestic environments, and into imagined health futures. These 'futuring' school‐based interventions open up small alternative spaces in which imaginaries of collective and resistant public health practices emerge. However, in the here and now, children appear to be bearing a disproportionate burden of responsibility, as ambassadors for, and imagined saviours of, public and environmental health. [ABSTRACT FROM AUTHOR]

This paper comprehensively evaluates the dynamic effects on China's environment and economy during the COVID-19 pandemic. Results show that the COVID-19 lockdown resulted in a temporary improvement in air quality. Furthermore, nitrogen dioxide (NO2) levels in the atmosphere in China were 36% lower than in the week after last year's Lunar New Year holiday, but this also led to an economic downturn. Moreover, the aerosol optical depth (AOD) decreased significantly. During the back-to-work period, the economy recovered and there was an increase in energy consumption, and CO2, NO2 emissions sharply increased to pre-lockdown levels. In the post-lockdown period, the AOD was lower than that of the same period last year. This study can provide reference for environmental policy making, as it demonstrates to what extent the control of pollution sources can improve air quality. Precise emission reduction and regional joint prevention and control are important and effective means for the prevention and control of O3 pollution. The health and economic benefits of COVID-19 pandemic control measures are incalculable. And this can provide an effective scientific basis and theoretical support for the prevention and control of air pollution. [ABSTRACT FROM AUTHOR]

Rapid economic development has led to increasingly serious air quality problems. Accurate air quality prediction can provide technical support for air pollution prevention and treatment. In this paper, we proposed a novel encoder-decoder model named as Enhanced Autoformer (EnAutoformer) to improve the air quality index (AQI) prediction. In this model, (a) The enhanced cross-correlation (ECC) is proposed for extracting the temporal dependencies in AQI time series; (b) Combining the ECC with the cross-stage feature fusion mechanism of CSPDenseNet, the core module CSP_ECC is proposed for improving the computational efficiency of the EnAutoformer. (c) The time series decomposition and dilated causal convolution added in the decoder module are exploited to extract the finer-grained features from the original AQI data and improve the performance of the proposed model for long-term prediction. The real-world air quality datasets collected from Lanzhou are used to validate the performance of our prediction model. The experimental results show that our EnAutoformer model can greatly improve the prediction accuracy compared to the baselines and can be used as a promising alternative for complex air quality prediction. [ABSTRACT FROM AUTHOR]

Copyright of Environmental Science & Technology (10036504) is the property of Editorial Board of Environmental Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of China Population Resources & Environment is the property of Shandong Normal University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

As air pollution worsens, the fast prediction of air pollutant concentration becomes increasingly important for public health. This paper proposes a new cross-domain prediction model of air pollutant concentration based on federated learning (FL), differential privacy laplace mechanism (DPLA) and long and short-term memory network optimized by sparrow search algorithm (SSA-LSTM), named FL-DPLA-SSA-LSTM. Firstly, with FL, SSA-LSTM is used as local training model for each city and predicts air pollutant concentration. Secondly, DPLA is used to add noise to the local model parameters, which can protect local data security. Then, the global model is updated by using the federated averaging algorithm (FedAvg). Lastly, FL is used to share global model for all cities, which can safely and quickly cross-domain predict air pollutant concentration. For data set, it is taken from hourly air pollutants and meteorological data from 12 cities in the Fenhe River and Weihe River Plains in China in 2020. The experimental results show that the prediction performance of the proposed model is significantly better than all comparison models. FedAvg updating with local model parameters with DPLA noise has little effect on the performance of the global model and even exceeds that of the global model. The calculation time of FL-DPLA-SSA-LSTM model is reduced by 99.95% compared with that of not using FL-DPLA machine learning model. It is proved that the model is high sharing and high safety, which greatly improves the training efficiency and has better generalization ability. It is significant for joint air pollution prevention and control and environmental protection. [ABSTRACT FROM AUTHOR]

Air pollution has become an urgent issue affecting sustainable urban development. The Chinese government has implemented a series of air pollution control policies since 2012. Exploring the effectiveness of pollution control policies is important for future policy-making and improvements in air quality. Mean and variance tests were used for evaluation on the effectiveness of pollution control policies implemented in major cities and estimates of the heterogeneity among cities based on the distribution fitting and testing of daily PM2.5 data from January 2015 to January 2020. The nonparametric kernel density estimation adopted in this paper can effectively describe the data characteristics, and this is very important for air quality monitoring and control. Our findings demonstrate that air pollution prevention and control policies have significantly improved the levels and distribution of urban air quality in China. [ABSTRACT FROM AUTHOR]

In this paper, we present an optimal control model of a stochastic production–inventory with deteriorating items, emission tax and pollution abatement investment. In our model, the emission tax is levied on the firm’s environmental obsolescence rate of technology rather than the total amount of the environmental externality. Our objective is to apply Hamilton–Jacobi–Bellman (HJB) equation to solve the stochastic production–inventory system with deteriorating items, emission tax and pollution abatement investment; and derive the optimal production rate and pollution abatement investment rate that maximise the objective function value. The results are discussed with some illustrative examples for different demand rate functions, and sensitivity analysis is conducted to study the effect of changing the parameters and coefficients on the objective function value. [ABSTRACT FROM PUBLISHER]

INTRODUCTION The study aims to understand the facilitators and barriers associated with enforcing and complying with Türkiye's smoke-free policy from the perspective of hospitality venue owners and employees. METHODS A qualitative open-ended survey was conducted in Istanbul and Ankara in 2021 with 58 respondents from 3 different districts in each city from four types of venues: restaurants, traditional coffee and waterpipe houses, and European-style cafés. The open-ended survey included questions to understand the knowledge, beliefs, and attitudes of respondents about Türkiye's smoke-free policy and their perceptions of the facilitators and/or barriers to smoke-free policy implementation and changes after COVID-19. The data were analyzed using an inductive approach to identify patterns and categorize the data into themes. RESULTS The respondents expressed that the smoke-free policy aimed to protect employees and customers from secondhand smoke (SHS), respect human health, and improve air quality. Findings suggest that the positive attitude of venue owners and staff toward the smoke-free policy serves as a facilitator. However, fear of financial impact, customers' negative attitudes, difficulties in meeting physical requirements, and insufficient enforcement were found to be barriers to implementing the smoke-free policy. The effects of the COVID-19 pandemic were reported as an initial increase in compliance and awareness among customers and staff, but some respondents noted negative changes due to the emotional and financial effects of prolonged restrictions. These challenges have led to decreased attention on the smoke-free policy among venue owners, staff and customers. Respondents' suggested improvements were related to building infrastructure, such as the ventilation systems and educating the public on the harmful health effects of smoking. CONCLUSIONS Despite the general understanding of the dangers of secondhand smoke and the smoke-free policy, this study highlights the challenges in implementing smoke-free policy measures and the continued need to raise awareness about the importance of a 100% smoke-free venue. A comprehensive approach to addressing the tobacco epidemic as a multifaceted public health issue is essential. [ABSTRACT FROM AUTHOR]

This paper examined the effect of air pollution joint prevention and control on pollution emissions in China. Specifically, based on the panel data of 290 cities from 2007 to 2021, taking the implementation of the "Air Pollution Prevention and Control Action Plan" as a natural experiment, the difference-in-difference-in-difference (DDD) model was used to explore the effect of air pollution joint prevention and control on haze pollution. Results show that air pollution joint prevention has a significant impact on pollutant emissions either as a whole or as a single pollutant. In terms of individual urban agglomeration, whether the Yangtze River Delta or the Pearl River Delta urban agglomerations, the air pollution joint prevention and control policy has a significant impact not only on the overall reduction of pollutant emissions but also on the reduction of single PM2.5 or industrial sulfur dioxide emissions alone. Environmental regulations have also achieved the effect of haze control in general and have a significant impact on the reduction of PM2.5 or industrial sulfur dioxide emissions. Environmental regulations also significantly reduced PM2.5 emissions in these three urban agglomerations. These findings provide a scientific basis and essential reference for understanding the implementation effect of regional joint prevention and control policies comprehensively and objectively. [ABSTRACT FROM AUTHOR]

The spread of microorganisms in the air, especially pathogenic microorganisms, seriously affects people's normal life. Therefore, the analysis and detection of airborne microorganisms is of great importance in environmental detection, disease prevention and biosafety. As an emerging technology with the advantages of integration, miniaturization and high efficiency, microfluidic chips are widely used in the detection of microorganisms in the environment, bringing development vitality to the detection of airborne microorganisms, and they have become a research highlight in the prevention and control of infectious diseases. Microfluidic chips can be used for the detection and analysis of bacteria, viruses and fungi in the air, mainly for the detection of Escherichia coli, Staphylococcus aureus, H1N1 virus, SARS-CoV-2 virus, Aspergillus niger, etc. The high sensitivity has great potential in practical detection. Here, we summarize the advances in the collection and detection of airborne microorganisms by microfluidic chips. The challenges and trends for the detection of airborne microorganisms by microfluidic chips was also discussed. These will support the role of microfluidic chips in the prevention and control of air pollution and major outbreaks. [ABSTRACT FROM AUTHOR]

The article deals with methods of chemistry teaching and disseminating chemical information and focuses on the use of art. In particular, the paper describes the method of linking theatre and chemistry and widespread dissemination of chemical information. It cites an example of one specific chemical theatre show "Whirlwind of Elements", staged by Ilia State University in 2018 and 2019, during an organized scientific picnic. The article specifies the key accents of the play, related to environmental chemistry problematics. The topics discussed are the following: quality of drinking water and water pollution with phthalates and heavy metals, air pollution and smog, acid rain. Very often, people are misinformed about chemical pollution of the environment. Some do not even know and realize that it is their action that causes the pollution. In this respect, the play highlights the human mistakes quite efficiently. It also outlines the semi-structural interview outcomes conducted with teachers, students, and representatives of other professions (non-chemists), randomly selected. The interview confirmed that the chemical emphasis in the play is placed on correct issues, and the connection between chemistry and theater ensures the right result in disseminating the information. [ABSTRACT FROM AUTHOR]

In recent years, the Chinese government has proposed a policy to replace coal use with natural gas and electricity in the northern region to reduce the air pollution caused by the large consumption of coal. In order to assess the air pollution reduction effect of the coal substitution policy in Liaoning Province, this paper proposes a data grouping grey model with a fractional order accumulation (FDGGM (1,1)). The empirical analysis results show that the new grey model can predict the monthly coal consumption more accurately than the traditional DGGM (1,1) model. The MAPEs of the training set in the FDGGM(1,1) and DGGM(1,1) models are 4.58% and 5.48%, and the MAPEs of the test set are 23.89% and 33.78%, respectively. And the policy achieves a great success based on the FDGGM(1,1) model. During the policy implementation period (from January 2015 to December 2018), the coal consumption in Liaoning Province decreased by 27.2501 million tons, while the emissions of CO2, SO2 and NOx fell by 0.714, 0.2316 and 0.2017 million tons, respectively. The results also provide a necessary support to the further implementation of the coal substitution policy. [ABSTRACT FROM AUTHOR]

In most countries, the problems of energy and environment are becoming worse. To deal with the environmental impacts and the dependence on fossil energy, many solutions were proposed. Plug-in electric vehicles (PEVs) is one of the best technique among these solutions. However, the large number of PEVs connected to the power grid simultaneously might increase power fluctuation or even cause the electricity shortage and thus affecting the typical use of the basic load. To cope with this issue and inspire PEV users coordinating with scheduling results, an algorithm was proposed to ensure the power transmission safety of branches and maximize the economic benefits. Considering the cost of both PEV owners and the power grid, a two-phase model of optimizing PEVs charging and discharging behaviors was built. According to the traveling purpose of PEV owners and the current electricity price, in the first phase, a novel model which defines each PEV’s charging or discharging status was established. The number of PEVs’ charging and discharging in each charging station can be obtained. Considering the constraints on the power transportation of branch, in the second phase, we built a mathematical model to maximize the benefit of both power grid and PEV owners. The genetic algorithm was used to optimize the charging and discharging power of PEVs. Simulation results show that the optimization method proposed in this paper has a better performance on the daily power curve compared with the uncoordinated PEVs charging. [ABSTRACT FROM AUTHOR]

Rapid increases in the world’s population, increased urban density and increased congestion have created upwards pressure that has seen traffic-related pollution growing at a rapid pace. As atmospheric pollution has a proven detrimental effect on human health and decreases the ambience and general liveability of the world’s cities. Developing, deciding and implementing effective atmospheric pollution and mitigation strategies has become of the utmost importance to policy-makers around the world. Alongside the increase in urban densification, there has been a rapid increase in Smart City infrastructure, made possible by harnessing data from low-cost sensors that can report information in a timely, dependable and accurate manner. This paper proposes a decision support system (DSS) that uses an underlying traffic model to inform an atmospheric dispersion model. Mitigation strategies can then be tested within the DSS through simulation of strategies in the underlying traffic model and analysing the effect on the forecasted atmospheric pollution levels. The proposed DSS is used to detect a critical level of atmospheric pollution and then may respond via the implementation of full road closures. While a partial road closure is not incorporated in this paper it is a trivial extension and diverting a subsection of the polluting traffic (e.g. heavy trucks) may be an easier policy to implement. The paper demonstrates the ability of the DSS to prevent atmospheric pollution from reaching hazardous levels and inform policy-makers as to when and where mitigation treatments should be implemented for the best outcome. [ABSTRACT FROM AUTHOR]

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Air pollution is a worldwide problem that needs to be address. Climate change and human health effects are considered the major concerns on this event. On this note, one of the policy tools that could help to resolve the issue is by adopting the contingent valuation method (CVM). CVM is a stated preference approach and can be done through conducting a survey which directly asks the respondent's willingness to pay (WTP) towards a service (i.e. contingent scenario). This paper identified the socio-demographic profiles of the residents in Cabanatuan City, assessed their knowledge and attitude about air pollution and its prevention, policies and programs currently implemented in each selected barangays, described the willingness to pay of the respondents in relation to contingent scenario, and assess the relationship between the profile and the respondents' WTP. Respondents have very high knowledge in the concepts of air pollution that leads to a very positive attitude regardless of their socio-demographic profiles. However, Logistic regression analysis revealed the insignificant relationship between the respondents' profile to their WTP. Respondents have very high knowledge and very positive attitude, which contributes to the positive WTP. Hence, a positive outcome in WTP. Therefore, residents are willing to pay and prevention of air pollution has a potential market value for the city. [ABSTRACT FROM AUTHOR]

Urbanisation has become a challenge as the urban population grows while cities' land areas, amenities, and green spaces have remained relatively unchanged or even declined. While urban areas are growing, the link between humans and nature is fading. Increasingly, cities are being affected by climate change impacts and so, the role of media in providing updated and correct knowledge to the public is becoming more valuable. Based on this theoretical ground, the research evaluated two printed Iranian newspapers' functionality in informing the public on Tehran climate based on two main themes of air pollution and greenery spaces, spanning seven years (2007–2014). The paper evaluated the tone, style, and outline of messages publicised by the press media to explore the following questions: Which types of news are dominantly conceptualised as the significant debates and concerns on Tehran's climatic issues? Who is mainly writing about Tehran's climatic issues? Is the public being informed effectively on the surrounding arguments and issues by reading newspapers? As such, five self-descriptive indicators were developed: 'Layout' (Title, Subject, Content), 'Message' (Public Awareness, Educating, Alarming), 'Contributor' (Columnist, Researcher, Authority), 'Spatiality' (Local, Provincial, National, International), and 'Allocated space' (10% to 100%). A text analysis of Persian newspapers using a Structured Query Language (SQL) was employed to extract data. It was found that the news articles mostly covered public awareness, followed by alarming messages on climate. The findings highlighted the critical role of researchers in generating scientific news while encouraging media for disseminating more educating messages on climate change in urban areas. [ABSTRACT FROM AUTHOR]

Air pollution has an important impact on both human health and sustainable economic development. The relationship of the current account, which is an important carrier of international economic activity, with air pollution has rarely been discussed by scholars. This paper aims to investigate how air pollution affects the current account and the mechanism of this effect. We conducted a theoretical analysis of the relationship between air pollution and the current account by adopting an extended form of the life-cycle model. Then, we used panel data (2000–2017) from 159 countries and the panel double fixed-effect method to empirically test the theoretical outcomes. We found that an increase in the degree of air pollution in a country leads to the deterioration of the domestic current account. In addition, air pollution changes the current account by affecting the demographic structure, following the "air pollution→demographic structure→current account" mechanism. The study also tested the robustness of the benchmark results by solving endogeneity problems, subsample regression and controlling measurement errors. Our findings are an important expansion and innovation for the research about the current account and have important implications for external economic equilibrium and sustainable economic development. [ABSTRACT FROM AUTHOR]

Air pollution in northern China is relatively serious during the winter heating period, which attracts the attention of the state and government, especially in Beijing–Tianjin–Hebei Region. To further explore the issue of air pollutant emission and energy utilization in Beijing–Tianjin–Hebei during the heating season, this paper establishes a panel data model which describes the long-term relationship between air pollutant emission, heating capacity, coal, and power consumption with the data from 2004 to 2017. Based on this, we draw the following conclusions: there is a positive relationship between winter heating capacity and air pollutant emissions, which indicates that the energy consumed by heating will produce atmospheric pollutants. However, the increase of electricity consumption does not necessarily reduce pollution, which means that replacing coal with electricity is not the best way to solve air pollution in heating season, but requires the coordination of multiple energy sources. In addition, there are obvious differences in the analysis results in Beijing, Tianjin, and Hebei. For example, the impact of coal and electricity consumption on pollutant emissions in Beijing is quite different from that in Tianjin and Hebei. The local economic development and energy conditions should be fully taken into account when formulating policies. [ABSTRACT FROM AUTHOR]

Due to the increasingly serious air pollution problem, air quality prediction has been an important approach for air pollution control and prevention. Many prediction methods have been proposed in recent years to improve the prediction accuracy. However, most of the existing methods either did not consider the spatial relationships between monitoring stations or overlooked the strength of the correlation. Excluding the spatial correlation or including too much weak spatial inputs could influence the modeling and reduce the prediction accuracy. To overcome the limitation, this paper proposes a correlation filtered spatial-temporal long short-term memory (CFST-LSTM) model for air quality prediction. The model is designed based on the original LSTM model and is equipped with a spatial-temporal filter (STF) layer. This layer not only takes into account the spatial influence between stations, but also can extract highly correlated sequential data and drop weaker ones. To evaluate the proposed CFST-LSTM model, hourly PM2.5 concentration data of California are collected and preprocessed. Several experiments are conducted. The experimental results show that the CFST-LSTM model can effectively improve the prediction accuracy and has great generalization. [ABSTRACT FROM AUTHOR]

Wireless Sensor Networks (WSN) consists of sensors used for sensing environmental conditions and many more applications in real world. Air pollution is a threat to the life of humans. To control the air pollution it is necessary to monitor the pollutant gases in periodically. Various air pollution monitoring systems using sensor network have been developed, deployed and tested in the literature. This paper presents a comparative study about the literature for air pollution monitoring systems based on the classification such as stationary air pollution monitoring systems, dynamic air pollution monitoring systems and pollution data analysis techniques. These pollution monitoring systems are compared based on the methodologies followed, microcontroller used, communication device used, pollutants analyzed using sensors, evaluation attributes, tested location and performance of the system. This paper also discusses the merits and demerits of the air pollution monitoring systems. [ABSTRACT FROM AUTHOR]

The current paper deals with the major causes of air pollution in the big cities of Azerbaijan. In this context, industrial gas wastes and transport systems are the most important sources of air pollution. Also, relevant factors following the intensity of air pollution have recently been recognized. The motor transport system and its detrimental effects on human health are tightly connected with air pollution. Therefore, the use of ecological and geographical data could be an efficient approach to preventing air pollution in urban regions. Also, the advantage of nanotechnology in removing air contaminants has been introduced as a promising solution. Nanomaterials can serve as nano adsorbents, nanocatalysts, nano filters/membranes, and nanosensors in air pollution remediation. Moreover, the green synthesis of nanomaterials from plant-based origins is promising in this context. Also, nanotechnology is a robust candidate for the production of green and sustainable energy resources. In a nutshell, recommendations on the prevention and alleviation of air pollution, as well as the methods of refining the urban environment and controlling polluting agents are represented. [ABSTRACT FROM AUTHOR]

Copyright of Polish Journal of Allergology / Alergologia Polska is the property of Termedia Publishing House and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

China's cross-border pollution problem has attracted a growing level of attention from the domestic and international community. The elimination of environmental pollution greatly depends on professional environmental protection companies. China's environmental protection industry has sustained a rapid growth with 26.9% annual growing rate of output value since 2011. To effectively discover the potential investment fields and regions, this study examines the spatial distribution of 53 A-Share Listed Environmental Companies (ASLEC) in China and their 927 subsidiaries. Methods of hot spot analysis, Pearson's correlation analysis and coarsened exact matching were employed in our paper to reveal the spatial distribution characteristics of environmental protection industry and their main influencing indicators. Results show that ASLEC invested over US$ 13 billion distributed in 210 cities in China in 2017. Treatment of wastewater and municipal solid waste related to traditional water supply, drainage and sanitation are the main businesses of the environmental protection industry in China. This is because these businesses belong to conventional urban municipal works with low technological requirement and high economic return. Therefore, the government should support those environmental protection businesses with fine technology, such as air pollution prevention and industrial waste control. Our study also reveals that there is a strong and positive correlation between municipal indicators and environmental protection investment. This indicates that the municipal works attract much more investment of environmental protection companies than heavy industries. The eastern region of China remains a hot spot for investment whereas the investment in the western region increased significantly in 2017. The potential of future development will be located in the central and western regions. For serious air pollution and large-scale industrial transfer from eastern regions to the central and western regions in China, there is lack of industrialization environmental protection capacity to fulfill the ambitious national pollution reduction target. This opportunity implies to attract more investments from international environmental protection companies. [ABSTRACT FROM AUTHOR]

This article analyzes the early years of 20th-century air pollution control in Los Angeles. In both scholarship and public memory, mid-century efforts at the regional level were overshadowed by major federal developments, namely the Clean Air Act and creation of the US Environmental Protection Agency in 1970. Yet the mid-century local experience was highly consequential and presaged many subsequent challenges that persist today. The article begins with an exploration of the existential, on-the-ground misery of smog in Los Angeles during the 1940s and 1950s. The article examines the role that scientific evidence on smog did and did not play in regulation, the reasons smog control galvanized support across various constituencies in the region, and, finally, some of mid-century air pollution's limits. [ABSTRACT FROM AUTHOR]

Environmental concerns are increasingly being incorporated into regional trade agreements (RTAs) to promote environmental quality and ultimately ensure compatibility between trade and environmental policies. This occurs in a context where air pollution and its effects on human health are of major concern. This paper investigates whether the proliferation and depth of environmental provisions (EPs) in RTAs are associated with lower concentration levels of particulate matter. We present an index of EPs in RTAs that measures the breadth and depth of the provisions and use it to estimate the effect of ratifying RTAs with different levels of EPs on changes in PM2.5 concentration levels in a panel of OECD countries over the 1999-2011 period. Using an instrumental variables strategy, we find that countries that have ratified RTAs with EPs show lower levels of PM2.5 concentrations when we control for scale, composition and technique effects and for national environmental regulations. Moreover, the PM2.5 concentration levels in the pairs of countries that belong to an RTA with EPs tend to converge for the country sample. The results also hold for a longer period of time (1990-2011) and a broader sample of 173 countries as well as for other pollutants, namely CO2 and NO2. [ABSTRACT FROM AUTHOR]

This paper analyzes the theoretical mechanism and transmission channel for the impact of air pollution on firms' domestic value-added ratio (DVAR) in exports. Based on the matched Chinese Industrial Enterprises Database and China Customs Enterprise Database, the DVAR in exports is measured, and this mechanism is empirically tested with standard measurement methods. The study concludes that air pollution is not conducive to raising the DVAR during the sample period. This conclusion remains robust to many issues, such as endogeneity, measurement error, extension of the sample interval, substitution of the air pollution index, and policy changes. The impact of air pollution on the DVAR varies by the type of firm ownership, size, and age, as well as the size and location of the city. In addition, the negative impact of air pollution on the DVAR grows with increases in the PM2.5 concentration. The growing factor prices of domestic intermediate goods and the falling productivity due to air pollution are important transmission channels for the negative impact of air pollution on the DVAR. In order for firms to open up on all fronts through quality trade development, we recommend the prevention and control of air pollution, fueling internal driving forces for firms' independent innovation, and encouraging firms to target the medium- and high-end markets. [ABSTRACT FROM AUTHOR]

ABSTRACT Air quality is declining in urban areas, in part because of the rapid motorization of societies world-wide. To combat the problem, various pollution control strategies have been used or proposed for urban passenger transport. This paper develops a simple framework to analyse the impact of these strategies. The paper examines the point of impact of different policy levers and categorizes different instruments in a way that should help policy makers choose between them. The framework explicitly recognizes behavioural incentives, especially the fact that offsetting changes in consumer behaviour can often undermine the original intent of particular policies. The paper concludes that policies aimed at improving transport efficiency often improve air quality at the same time. However, supply side policies to relieve traffic congestion can conflict with the objective of controlling air pollution. It is hence vital that policy makers are aware of the incentives created by different interventions and weigh the impact of these incentives on subsidiary objectives before adoption of particular policies. [ABSTRACT FROM AUTHOR]

Wet scavenging is an efficient pathway for the removal of particulate matter (PM) from the atmosphere. High levels of PM have been a major cause of air pollution in Beijing but have decreased sharply under the Air Pollution Prevention and Control Action Plan launched in 2013. In this study, 4 years of observations of wet deposition have been conducted using a sequential sampling technique to investigate the detailed variation in chemical components through each rainfall event. We find that the major ions, SO 42- , Ca 2+ , NO 3- , and NH 4+ , show significant decreases over the 2013–2017 period (decreasing by 39 %, 35 %, 12 %, and 25 %, respectively), revealing the impacts of the Action Plan. An improved method of estimating the below-cloud scavenging proportion based on sequential sampling is developed and implemented to estimate the contribution of below-cloud and in-cloud wet deposition over the four-year period. Overall, below-cloud scavenging plays a dominant role to the wet deposition of four major ions at the beginning of the Action Plan. The contribution of below-cloud scavenging for Ca 2+ , SO 42- , and NH 4+ decreases from above 50 % in 2014 to below 40 % in 2017. This suggests that the Action Plan has mitigated PM pollution in the surface layer and hence decreased scavenging due to the washout process. In contrast, we find little change in the annual volume weighted average concentration for NO 3- where the contribution from below-cloud scavenging remains at ∼ 44 % over the 2015–2017 period. While highlighting the importance of different wet scavenging processes, this paper presents a unique new perspective on the effects of the Action Plan and clearly identifies oxidized nitrogen species as a major target for future air pollution controls. [ABSTRACT FROM AUTHOR]