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The impact of air pollution on public health is substantial, and accurate long-term predictions of air quality are crucial for early warning systems to address this issue. Air quality prediction has drawn significant attention, bridging environmental science, statistics, and computer science. This paper presents a comprehensive review of the current research status and advances in air quality prediction methods. Deep learning, a novel machine learning approach, has demonstrated remarkable proficiency in identifying complex, nonlinear patterns in air quality data, yet its application in air quality prediction is still relatively nascent. This paper also conducts a systematic analysis and summarizes how cutting-edge deep learning models are applied in air quality prediction. Initially, the historical evolution of air quality prediction methods and datasets is presented. This is followed by an examination of conventional air quality prediction techniques. A thorough comparative analysis of progress made with both traditional and deep learning-based prediction methods is provided. This review particularly focuses on three aspects: temporal modeling, spatiotemporal modeling, and attention mechanisms. Finally, emerging trends in the field of air quality prediction are identified and discussed. [ABSTRACT FROM AUTHOR]

In today's world, as ecological challenges continue to rise, the importance of environmental monitoring and conservation cannot be overstated. These efforts play a vital role in countering the adverse impacts of human activities on our planet. The development of predictive models that can accurately predict the results of different conservation measures is essential for the success of these conservation efforts. This paper's objective is to create a Machine Learning (ML) model that predicts how environmental changes can unfold. This prediction helps in proactive monitoring and conservation planning, considering factors like deforestation, air pollution, sea level rise, temperature shifts, and forest preservation. The model relies on machine learning techniques, including AdaBoost, ETS, Prophet, SARIMA, LightGBM, and TL-BLSTM models, to analyze data, uncover trends, and inform decision-making. By offering a thorough assessment of the effects of conservation approaches, this model aids in shaping well-informed decisions regarding the distribution of resources. As a result, this process strengthens initiatives designed to protect biodiversity and the Earth's natural resources. Predicting changes in environmental variables is the main goal of this research, which will improve effective environmental management. In order to draw conclusions that are meaningful, this study compares several models for each of the four crucial factors. [ABSTRACT FROM AUTHOR]

The Gothenburg Protocol (Protocol to Abate Acidification, Eutrophication and Ground-level Ozone) was first established in 1999 to support the enactment of the 1979 Convention on Long-range Transboundary Air Pollution. The Executive Body launched a review in December 2019 which was concluded in December 2022. In order to support the review and contribute to the assessment of the remaining risks for health, ecosystems and crops, model calculations have been performed on 2015, 2030 and 2050 emission scenarios which include both baseline and maximum technical feasible reduction (MFR) scenarios. The uEMEP/EMEP MSC-W modelling system has been applied for these calculations. uEMEP is a downscaling module for the EMEP MSC-W model that allows high resolution calculations, at 250 m for exposure, to be made. In this paper we document the input data, the model setup and present the calculated concentrations, exposure and source contributions based on emission scenario input from CIAM (Center for Integrated Assessment Modelling). The focus of this paper is on annual mean PM 2.5 and NO 2 at both existing monitoring sites and for population exposure in general. A comparison between three regions, namely the EU27+EFTA+UK, the Western Balkan, and EECCA countries is made. Comparison of current and future exposure to the WHO Guideline and Interim Target levels is also made. These 3 regions showed different exposure levels and different future trends. By 2030 the Baseline scenario indicates that 75% of the EU population will still be exposed to PM 2.5 levels above 5 μ g m−3. However, this number is reduced to 40% in the 2050 Baseline calculation. The additional implementation of MFR in 2050 will reduce this further to just 14% of the population with exposure in excess of 5 μ g m−3. In this scenario, there is less than 1% of the population exposed to PM 2.5 levels above 10 μ g m−3. For the Western Balkan and EECCA countries, the baseline scenario shows much less improvement in the PM 2.5 levels. It is only with the implementation of MFR that significant improvement in these countries can be attained. The EECCA countries are limited in achieving lower concentration levels by high source contributions from wind blown dust. For NO 2 , all scenarios in 2050 showed that less than 2% of the EU population are still exposed above the WHO Guideline of 10 μ g m−3, while this share is 21% for the Western Balkan. For the 2050 baseline, the EECCA countries showed an increase in NO 2 concentrations compared to 2015, with about 50% of the population being exposed to above 10 μ g m−3 and still with 13% of the population (33 million inhabitants) above 40 μ g m−3. It is only with the implementation of MFR that NO 2 concentrations approach, but do not achieve, the WHO Guideline levels. • Emission scenarios have been developed for the Gothenburg protocol review. • The EU, Western Balkans and EECCA countries have been modelled. • Scenarios have been modelled at a resolution of 250 m for all three regions. • By 2050 14% of the EU are exposed above the PM 2.5 WHO Guideline of 5 μ g/m3. • Western Balkan and EECCA countries show much less improvement in PM 2.5 levels. • By 2050 2% of the EU are exposed above the NO 2 WHO guideline of 10 μ g/m3. [ABSTRACT FROM AUTHOR]

To enhance regional collaborative management and effectively address air pollution, this paper constructs the industrial air pollution network of 168 cities in China between 2010 and 2021. Social network analysis (SNA) and temporal exponential random graph model (TERGM) are used to investigate the structural characteristics and driving mechanisms of industrial air pollution spatial correlation networks. The results indicate the following: (1) Industrial air pollution levels in key Chinese cities showed a slight fluctuation between 2010 and 2015, but decreased after 2015. (2) The industrial air pollution linkage network exhibits small-world characteristics and path dependence, with different cities playing distinct roles in the network. (3) The evolution of the network is influenced by both exogenous and endogenous mechanisms. Local network structures such as reciprocity, connectivity, and transitivity make the network exhibit path-dependent characteristics. Cities' attributes such as economic development level, green innovation capacity, and population density significantly influenced their status in the network and linkages between cities. Pollution linkages are more likely to occur in cities with similar levels of economic development, green technological innovation capacity, and institutional environments. Therefore, effective regional division for scientific collaborative governance should be considered from multiple perspectives. Based on these results, the paper provides several policy recommendations. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has reinforced an interest in the relationship between air pollution and respiratory viral infections, indicating that their burden can be increased under poor air quality. This paper reviews the pathways through which air pollutants can enhance susceptibility to such infections and aggravate their clinical course and outcome. It also summarizes the research exploring the links between various viral infections and exposure to solid and gaseous pollution in Poland, a region characterized by poor air quality, especially during a heating season. The majority of studies focused on concentrations of particulate matter (PM; 86.7%); the other pollutants, i.e., BaP, benzene, CO, NO x , O 3 , and SO 2, were studied less often and sometimes only in the context of a particular infection type. Most research concerned COVID-19, showing that elevated levels of PM and NO 2 correlated with higher morbidity and mortality, while increased PM 2.5 and benzo[a]pyrene levels were related to worse clinical course and outcome in hospitalized, regardless of age and dominant SARS-CoV-2 variant. PM 10 and PM 2.5 levels were also associated with the incidence of influenza-like illness and, along with NO 2 concentrations, with a higher rate of children's hospitalizations due to lower respiratory tract RSV infections. Higher levels of air pollutants also increased hospitalization due to bronchitis (PM, NO x , and O 3) and emergency department admission due to viral croup (PM 10 , PM 2.5 , NO x , CO, and benzene). Although the conducted studies imply only correlations and have other limitations, as discussed in the present paper, it appears that improving air quality through reducing combustion processes in energy production in Poland should be perceived as a part of multilayered protection measures against respiratory viral infections, decreasing the healthcare costs of COVID-19, lower tract RSV infections, influenza, and other respiratory viral diseases prevalent between autumn and early spring, in addition to other health and climate benefits. [Display omitted] • Polluted air may increase the severity of respiratory viral infections. • Poland is a PM and BaP pollution hot spot in Europe. • Air pollution is associated with a higher burden of respiratory viral infections. • Improving the air quality should be a protection measure against infections. • Further studies are encouraged to explore the relationships with other pathogens. [ABSTRACT FROM AUTHOR]

This paper describes the DAnish Lagrangian Model (DALM), which is a new high-resolution air pollution model based on the concept of Lagrangian particles. The new model is developed from the simpler Gaussian plume-in-grid Urban Background Model (UBM) and is to be integrated with the DEHM/UBM/AirGIS modeling system, developed at Aarhus University. In the first part of the paper, the theoretical foundation of the model is presented, and the implementation of a large set of physical and numerical parameterizations is discussed. The second part describes the validation of DALM against measurements, applying different combinations of the implemented parameterizations. This validation demonstrates that DALM can accurately reproduce spatiotemporal patterns in the measured data and that its performance is more sensitive to parameterizations of vertical compared to horizontal transport. Conclusively, the combination of parameterizations yielding the best model performance is determined based on a ranking system, and future improvements to DALM are outlined. [Display omitted] • A new comprehensive local-scale Lagrangian air pollution model is in development. • The new model is validated against measurements from Danish monitoring stations. • Some sets of planetary boundary layer parameterizations show a better performance. [ABSTRACT FROM AUTHOR]

• This paper investigates the impact of air pollution on excess executives' perks. • To establish causality, we exploit an exogenous event (i.e., COVID-19), which restrained executives' perk consumption due to social distancing and lockdown policies. • We find that air pollution positively affects excess perks. • External monitors and managerial monetary compensation can curb excess perk consumption. This paper investigates the impact of air pollution on excess executives' perks. To establish causality, we exploit an exogenous event (i.e., COVID-19), which restrained executives' entertainment activities and travelling due to social distancing and lockdown policies. Using a difference-in-difference model and a sample of listed firms in China from 2016 to 2022, we find that excess perks of companies located in polluted cities reduce more due to COVID-19 than those of companies in cities with good air, indicating that air pollution positively affects excess perks. Further analyses demonstrate that external monitors and managerial monetary compensation can curb excess perk consumption. [ABSTRACT FROM AUTHOR]

Air pollution and labor mobility are two important social phenomena associated with China's pursuit of high-quality development. First, this paper matches China's urban characteristics data with China Migrants Dynamic Survey in 2017. Then, the conditional logit model is used to empirically investigate the effects and heterogeneity of air pollution on employment location choice. Finally, combined with the concept of risk perception in psychology, this paper explores the underlying mechanisms. The main results are as follows: (1) Air pollution has a significant negative effect on employment location choice of labor force. Specifically, a 1 μg/m3 increase in PM 2.5 concentration in a city can result in a significant decrease of 0.917 percentage points in the probability that the labor force will choose to work in that city. (2) There are heterogeneous characteristics of the negative impact of air pollution on employment location choice. Those with higher education level, outdoor work, relatively higher individual income, and 1–2 minor children are more sensitive to air pollution when choosing employment sites. (3) The mechanism analysis shows that perceived air pollution is an important channel influencing labor's employment location choice, and the stronger the stimulus of air pollution, the greater the negative impact of perceived air pollution on labor force. • This paper matches China's urban characteristics data with China Migrants Dynamic Survey. • The effects and heterogeneity of air pollution on employment location choice is explored. • Combined with the concept of risk perception in psychology, this paper explores the underlying mechanisms. [ABSTRACT FROM AUTHOR]

Photochemical air quality models (AQMs) are a vital tool for atmospheric pollution research and have been widely used in various applications, such as air quality prediction and evaluation of pollution control strategies. Before using these models for further studies, it is essential to thoroughly evaluate their reliability and accuracy. While previous guidelines and benchmarks have primarily focused on fine particulate matter (PM 2.5) and ozone (O 3), there is still a lack of benchmarks for evaluating the model performance on primary criteria pollutants such as sulfur dioxide (SO 2), nitrogen dioxide (NO 2), carbon monoxide (CO), and particulate matter with aerodynamic diameter less than or equal to 10 μm (PM 10). The use of air quality models in China has increased significantly in the past decades. However, there is still a lack of standardized benchmarks for the performance evaluation of these models. Building upon our previous work on PM 2.5 and its chemical species, we propose a set of benchmarks for evaluating the performance of the aforementioned four air pollutants. Initially, we identified a total of 475 papers published during 2007–2019 that utilized at least one of the five commonly used AQMs in China. From these papers, we selected 164 articles that provided model performance evaluation (MPE) results of the four primary air pollutants. The three most frequently used Model Performance Evaluation (MPE) metrics were selected to analyse the impact of different model configurations on the reported statistics, including modelling region, season, and emission inventory. Lastly, three commonly used statistical indicators, including normalized mean bias (NMB), normalized mean error (NME), and correlation coefficient (R), were proposed for the validation of simulated NO 2 , SO 2 , CO, and PM 10. Two sets of benchmarks are given, including the "goal" and "criteria". The "goal" represents the best range of performance that a model can be expected to achieve, and the "criteria" represents performance that the majority of studies have achieved. We recommend R values above 0.50, 0.35, 0.45, and 0.40 for NO 2 , SO 2 , PM 10 , and CO, respectively, in order to meet the "criteria" benchmark. If the "goal" benchmark is to be achieved, the corresponding R values are 0.60, 0.55, 0.60, and 0.60. The "goal" benchmarks of NMB for NO 2 , SO 2 , PM 10 , and CO are within ±20%, ±25%, ±20%, and ±25%, respectively; while the "goal" benchmarks of NME for the four pollutants are less than 40%, 45%, 45%, and 60%, respectively. These benchmarks supplement our previous benchmarks for PM 2.5 and its components and provide a more comprehensive guideline for the air quality modelling community in China. • A set of benchmarks for evaluation of the NO 2 , SO 2 , CO and PM 10 simulation over China are proposed. • Three commonly used statistical indicators (NMB, NME, and R) were proposed for the validation of the four pollutants. • The goal benchmarks of NMB for NO 2 , SO 2 , PM 10 , and CO are <20%, <25%, <20% and <25%, respectively. [ABSTRACT FROM AUTHOR]

A declining trend in Indian summer monsoon precipitation (ISMP) in the latter half of the 20th century is a scientifically challenging and societally relevant research issue. Heavy aerosol loading over India is one of the key factors in modulating the ISMP. Using the state-of-the-state-of-the-art chemistry-climate model, ECHAM6-HAMMOZ, the impacts of South Asian anthropogenic sulfate aerosols on the Indian summer monsoon precipitation were investigated against: (1) 2010 La Niña (excess monsoon), (2) 2015 El Niño (deficit monsoon) in comparison to (3) normal monsoon 2016. Sensitivity simulations were designed with 48% enhancement in South Asian SO 2 emissions based on a trend estimated from Ozone Monitoring Instrument (OMI) satellite observations during 2006–2017. The model simulations showed that sulfate aerosols reduce ISMP by 27.5%–43.3 %, while simulations without sulfate loading enhanced ISMP by 23% in 2010 La Niña and reduction by 35% in 2015 El Niño. This paper reports that sulfate aerosols loading over India reduce precipitation by aerosol-induced direct and indirect effects by inducing atmospheric cooling, weakening in the convection, and reduction in moisture transport to Indian landmass. This paper emphasizes the necessity of alternate use of energy to reduce sulfate aerosol emissions to solve water issues in South Asia. [Display omitted] • Sulfate emission reduces Indian summer monsoon precipitation through aerosol-radiation and aerosol-cloud interactions. • Over 27% of the summer monsoon rainfall deficit in a normal year are linked to Asian anthropogenic sulfate emissions. • Sulfate-induced rainfall reduction is the maximum for La Niña year. • SO 2 loading increases atmospheric stability and weakens moisture transport to India. [ABSTRACT FROM AUTHOR]

• Urban green spaces have direct and indirect effects on improving residents' health. • The health benefits of urban green spaces exhibit regional heterogeneity. • Economic development weakens the health benefits of urban green spaces. • Environmental regulations can enhance the health benefits of urban green spaces. Urban green spaces have long been cherished as the "lungs of the city", it can not only improve air quality and enhance residents' health. The impact of urban green spaces on air pollution and residents' health has attracted great attention. In this paper, we select 31 provinces in China as research samples and use ArcGIS software to draw spatial distribution maps of urban green spaces, air pollution, and residents' health in China, characterizing their spatiotemporal changes in order to explore whether there is a correlation among the three. On this basis, this paper utilizes panel data of Chinese provincial-level from 2010 to 2020 and conducts empirical tests through a two-way fixed effect model and a threshold regression model. The research findings are as follows: (1) Urban green spaces not only bring environmental benefits, but also health benefits, and the health benefits are greater than the environmental benefits. (2) There is a health risk of air pollution in northern China, and the health benefits of urban green spaces are not significant. Conversely, there is no health risk of air pollution in southern China, and the health benefits of urban green spaces are significant. (3) Although environmental regulations can enhance the health benefits of urban green spaces, the role of environmental regulations in enhancing the health benefits of urban green spaces may be further weakened as the economy develops. [ABSTRACT FROM AUTHOR]

In this paper, we applied the Dispersion Normalised Positive Matrix Factorisation (DN-PMF) approach recently proposed in the literature to provide a more realistic picture of the relative importance of emission strength vs. atmospheric dispersion conditions. The disentanglement of such effects is of great concern in pollution hot spots like the Po Valley (Italy), where particulate matter limit values are exceeded despite the existing abatement measures. To explore the potentiality of the DN-PMF approach – still scarcely applied in the literature – a well-chemically characterised PM 1 (atmospheric particles with aerodynamic diameter <1 μm) dataset comprising samples collected at different time resolutions at an urban background site (Bologna) in the southern Po Valley was used. Indeed, it is well known that shallow mixing layers promote pollutant accumulation but this observation is not enough to exclude an enhancement of emission strength which could be tackled by appropriate abatement strategies. The source apportionment of sub-micron sized aerosols having a quite long atmospheric residence time in a complex environment like the Po Valley - which is also strongly impacted by secondary aerosol formation on a basin-scale - is generally quite challenging when using receptor models. Due to the availability of a huge dataset with variables having multiple time resolutions, in this work the DN-PMF was implemented in a multi-time resolution approach (MT) to achieve a better source identification and to gain knowledge about the relative importance of atmospheric dilution vs. emissions. A comparison between results obtained by the application of the regular multi time resolution (REG-MT) vs. the DN-MT approach is presented here for the five factors identified (nitrate-dominated, sulphate-dominated, biomass burning, mineral dust, and urban aerosol). The first interesting outcome is that REG-MT and DN-MT results do not point at significant differences in temporal patterns for aerosol components and sources impacting at the basin-scale (i.e. sulphate- and nitrate-dominated aerosol, biomass burning) thus suggesting that the diel modulation of these PM 1 emissions is somehow masked by the stronger variability of the mixing layer. Conversely, contributions from local sources with more pronounced diel variation like traffic are quite well reproduced by DN-MT and the ambient concentrations are enhanced compared to REG-MT. This is an important piece of information highlighting that PM 1 concentrations from local sources have been likely underestimated by REG-MT assessments. To our knowledge, this is one of the very few applications of DN-MT and the first one at a European site where the huge effort made to implement air pollution containment measures is still not very much effective in reducing PM levels; moreover, in this paper a detailed discussion about the possible interpretation of the output of DN-MT in terms of temporal patterns is reported. [Display omitted] • High PM levels are due to atmospheric stability and emission strength; DN-PMF points out the source emission role. • Secondary aerosol-dominated factors showed a regional nature. • Urban aerosol factor was highly enhanced by dispersion-normalisation. [ABSTRACT FROM AUTHOR]

• Examining the problem of capital allocation efficiency in China from the perspective of air pollution. • Constructing a new indicator of capital allocation efficiency, measured using actual capital productivity over potential capital productivity. • Constructing a mechanism for air pollution to affect capital allocation efficiency in terms of innovation activities and human capital accumulation channels. • The effect of air pollution on capital allocation efficiency and its heterogeneity are empirically analyzed. As China seeks to transition to a high-quality economic development model, improving its capital allocation efficiency serves as a means to revitalize the capital stock and facilitate a shift from an investment-driven growth approach to an innovation-driven one. By exploring the relationship between air pollution and capital allocation efficiency, new avenues for promoting high-quality economic development can be identified. This paper develops a theoretical analysis framework that posits the mechanisms by which air pollution affect capital allocation efficiency through innovation activities and human capital channels. Based on the data from prefecture-level cities in China from 2003 to 2019, this paper introduces an innovative method to evaluate capital allocation efficiency. Further, using a panel two-way fixed-effects model to examine the impact of air pollution on capital allocation efficiency and its mechanisms. The findings show that ① the intensification of air pollution reduces capital allocation efficiency and the above findings pass the robustness tests. ② The results of heterogeneity analysis show that there is significant heterogeneity in the effect of air pollution on capital allocation efficiency. The intensification of the air pollution significantly inhibits capital allocation efficiency in inland areas, small and medium-scale cities, after 2013, and in areas with high foreign economic dependence. ③ The results of mechanism analysis shows that the intensification of the air pollution will inhibit capital allocation efficiency by suppressing innovation activities and human capital accumulation channels. [ABSTRACT FROM AUTHOR]

Energy conservation is a fundamental requirement in achieving sustainable development, enhancing environmental protection and improving economic and social wellbeing by reducing energy consumption. Carbon emissions are a significant manifestation of energy consumption. Real-time monitoring and forecasting of air quality plays a crucial role in effectively controlling carbon emissions and improving energy conservation measures. Complex Event Processing (CEP) is a technical framework for streaming data processing based on predefined rules and is widely used for real-time detection. Yet as the diversity of data increases, it becomes exceptionally difficult for experts to formulate the CEP rules. Rule-based algorithms can address this issue to a certain extent. However, the CEP rules extracted through these methods are obtained from static data and are often simple and independent of each other. Here, we regard such rules as direct rules, which ignore possible connections between events that usually imply deeper rules. In contrast, we refer to rules that reflect interconnections between events and reveal higher-level principles as indirect rules. In this paper, we propose a methodology fusing interpretable machine learning and decision mining to extract direct and indirect multi-level CEP rules from air pollution datasets for real-time detection, prediction, and early warning. First, we utilize the decision tree algorithm to extract direct rules to detect air quality in real-time. Next, we utilize the process mining algorithm to model the changes in air states and extract the Petri net model from it that reflects the changes in air quality. Then, we utilize our proposed decision mining algorithm to extract indirect CEP rules. Indirect rules can help to understand how pollutants in the air change over time. In addition, they can reveal underlying trends and patterns of air quality change for prediction and early warning. Finally, we validate the accuracy and effectiveness of our approach using a real air pollution dataset. This work contributes to the field of air pollution detection and can help promote the optimization of energy conservation, while it can also support the development of regulatory strategies. • A multi-level CEP rules extraction method is proposed for air quality detection. • A novel method combining machine learning and process mining to extract CEP rules. • A novel way to analyze decision points in Petri nets to monitor air states changes. [ABSTRACT FROM AUTHOR]

In the realm of new energy batteries, metal-air batteries and fuel cells exhibit promising prospects for broad market applications due to their high energy density, cost-effective manufacturing processes, and negligible atmospheric pollution. The oxygen reduction reaction (ORR) serves as a crucial cathode reaction in these battery systems. However, its relatively sluggish kinetics hinders the widespread adoption of such batteries. Therefore, efficient oxygen reduction catalysts are required to enhance the rate of ORR. Although traditional platinum (Pt) catalyst exhibits good electrocatalytic performance for ORR, it is difficult to be widely used because of its high cost and limited availability of resources. Alloy catalysts are anticipated to serve as substitutes for Pt catalysts by effectively enhancing catalytic activity while reducing production costs. Currently reported alloy catalysts in literature primarily augment their oxygen reduction activity through carbon carriers, heterojunction effects, amorphous structures or metal-organic framework-derived structures with heteroatom-doped composite alloys. This paper provides a comprehensive review on the research progress pertaining to Pt-based alloy catalysts, Pd-based alloy catalysts, and Fe-based alloy catalysts while summarizing the advantages and challenges associated with existing alloy catalyst systems. [ABSTRACT FROM AUTHOR]

Air pollution control has emerged as one of the key concerns of recent times and business firms are one of the significant stakeholders in deteriorating the quality of the natural environment. The purpose of this paper is to analyse the factors affecting the adoption of air pollution control measures across firms and contribute to the existing knowledge on pollution control among firms in the context of an emerging market. The analysis shows a significant association between enterprise characteristics and the adoption of air pollution control measures and about 44.3 percent firms have reported the adoption of air pollution control measures. Further, the marginal effect analysis indicates that firms older in age, larger in size and having shareholders' equity are more likely to adopt air pollution control measures. Similarly, firms practising lean operations, investing in research & development, availing bank credit, having quality certification and having better capacity utilisation are more likely to adopt air pollution control measures. Finally, firms perceiving obstacles related to business licensing & permits, tax administration, political instability, and business closure due to COVID-19 outbreak are more likely to adopt air pollution control measures. Our study provides useful implications for managers, policymakers and researchers for encouraging air pollution control technologies across business firms. The study also motivates undertaking future research on pollution control measures across various sectors of the economy. [Display omitted] • Air pollution control is becoming a critical agenda across the globe. • About 44.3 percent Indian firms reported the adoption of air pollution control measures. • Firms' characteristics are significantly associated with adopting air pollution control measures. • Business operations, regulatory mechanisms and obstacles influence the adoption of air pollution control measures. • These insights help develop strategies for reducing air pollution by involving managers, policymakers and researchers. [ABSTRACT FROM AUTHOR]

Desulphurization of fossil fuels is a critical process in reducing the sulphur content from environment, which is a major contributor to atmospheric pollution. Traditional desulphurization techniques, while effective, often involve high energy consumption and the use of harsh chemicals. Recently, photocatalytic desulphurization has emerged as a promising, eco-friendly alternative, leveraging the potential of photocatalysts especially semiconductor heterojunctions to enhance photocatalytic efficiency. This review comprehensively discusses the significance and mechanism of photocatalytic desulphurization reactions, designing of various heterojunctions such as conventional, p-n, Z-scheme and S-scheme, their charge transfer mechanism and properties and their contribution to the photocatalytic desulphurization activity. Heterojunctions, formed by combining different semiconductor materials, facilitate efficient charge separation and broaden the light absorption range, thereby improving the photocatalytic performance under visible light. Furthermore, the recent advancements in the heterojunction systems in the field of photocatalytic desulphurization activity have been discussed in detail and summarized. The current limitations and challenges in this particular field are also explored. The paper concludes with an outlook on future research directions and the potential industrial applications of heterojunction-powered photocatalytic desulphurization, emphasizing its role in achieving cleaner energy production and environmental sustainability. [Display omitted] • Mechanisms elaboration from perspective of photocatalysts and desulphurization. • Systematic overview on photocatalytic heterojunctions: Issues and innovations. • Investigation on recent advances in photocatalytic desulphurization by heterojunctions. • Evaluation of effectiveness and stability. • Discussion on the development prospects and challenges. [ABSTRACT FROM AUTHOR]

Over the past decades, air pollution has caused severe environmental and public health problems. According to the World Health Organization (WHO), fine particulate matter (PM 2.5), a key component reflecting air quality, is the fourth leading cause of death worldwide after cardiovascular disease, smoking, and diet. Various research efforts have aimed to develop PM 2.5 forecasting models that can be integrated into a solution to mitigate the adverse effects of air pollution. However, PM 2.5 forecasting is challenging because air pollution data are non-stationary and influenced by multiple random effects. This paper proposes an effective multivariate multi-step ensemble machine learning model for predicting continuous 24-h PM 2.5 concentrations, considering meteorological conditions, the rolling mean of PM 2.5 time series, and temporal features. PM 2.5 is strongly correlated with space and time. Therefore, forecasting results from one location are insufficient to represent the level of air pollution for an entire city. In this study, we established six real-time air quality monitoring sites in different regions, including traffic, residential, and industrial areas in Ho Chi Minh City (HCMC), and generated forecasting results for each station. Various statistical methods are incorporated to evaluate the performance of the model. The experimental results confirm that the model performs well, substantially improving its forecasting accuracy compared to existing PM 2.5 forecasting models developed for HCMC. In addition, we analyze to determine the contribution of different feature groups to model performance. The model can serve as a reference for citizens scheduling local travel and for healthcare providers to provide early warnings. [Display omitted] • The proposed model accurately predicts urban PM 2.5 contamination for the next 24 h. • MI-based correlation analysis is valuable for understanding the influence of meteorological variables on PM 2.5 concentration. • Combining rolling mean features of PM 2.5 with meteorological features improves PM 2.5 prediction accuracy significantly. • The forecasting model's training protocol effectively addressed the data's non-stationarity. [ABSTRACT FROM AUTHOR]

Understanding the relationship between air pollution and corporate investment strategy has significant implications for both environmental sustainability and economic development. Utilizing data from China's A-share listed companies spanning from 2014 through 2022, we investigate the causal relationship between air pollution and corporate allocation of financial assets. Using an instrumental variable approach, we find that worse air quality has a positive effect on corporate financial asset allocation. Specifically, a 1% increase in the number of air pollution days tends to result in a 0.002% increase in the share of financial assets held by firms. This effect prevails through diverse robustness tests. Furthermore, as companies encounter more stringent financing constraints and environmental regulations, the trend of air pollution promoting the increased financialization of assets becomes more pronounced. Based on the findings, this paper suggests providing more support for enterprises to facilitate the upgrading of production and operation to meet environmental standards. [ABSTRACT FROM AUTHOR]

Sustainable development in cities requires advanced technologies for monitoring and estimating air pollution emissions, which directly affect the health of local inhabitants and residents in the neighborhoods. For this, low-cost sensors and information technologies are increasingly used to provide accurate air quality forecasts. They are, however, subject to data constraints. This paper presents new techniques for accurate, reliable monitoring and forecasting of air pollution at various scales using data from IoT-enabled sensors along with state-run air-quality monitoring stations. Here, we develop an extended deep-learning model based on neural networks and algorithms for optimization of the model hyperparameters and network dropout rates. These can yield a significant improvement of over 31 % in prediction accuracy while maintaining a prediction coverage of approximately 80 % for forecasting air-particle levels over a 24-h period. The advantages and effectiveness of our model are validated and verified in two real-world scenarios, a suburban construction site and a civil infrastructure development project. Comparison analysis is conducted to indicate the outperformance of the proposed method over two recent techniques for probabilistic time series forecasting used for air pollution estimation on regular days and extreme events. • Urban emissions monitored with low-cost wireless sensor networks and estimated from air quality monitoring stations. • Novel extended learning model with CNN-LSTM-BNN developed for multi-scale particle forecasts. • Improving forecast coverage with Bayesian optimization-based technique developed for model hyperparameter selection. • Performance validation and verification with data from two real-world urban sites. [ABSTRACT FROM AUTHOR]

Accurate prediction of the Particulate Matter 2.5 (PM 2.5) plays a crucial role in the accurate management of air pollution and prevention of respiratory diseases. However, PM 2.5 as a time series is extremely difficult to accurately predict. In this paper, a Hybrid Integration (HIG) algorithm that combines data pre-processing, time-series decomposition, signal decomposition, a prediction module, a matching strategy, and a hybrid integrated optimization algorithm is proposed. First, the optimal parameters for the four individual models were selected by integrating multiple evaluation perspectives. Additivity was then determined by Seasonal and Trend decomposition using LOWESS (STL), followed by refinement decomposition using signal decomposition. The four new sequences were reconstructed using Range Entropy (RangeEn) and mapped to the models. Additionally, Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM) methods were optimized using the HIG algorithm. The results show that the HIG-RNN and HIG-LSTM are more advantageous than the ordinary method in terms of reasonable weight assignment. Finally, an innovative confusion test method was developed to test the stability of the prediction direction. To ensure generalizability, validation was performed using PM 2.5 data from two regions of China. The results show that the method significantly improves the prediction performance and provides a powerful tool for policy formulation and management. [Display omitted] • A hybrid integration (HIG) algorithm is proposed. • Prediction results were evaluated using the original COI and improved ICOI. • Creative time-frequency analysis bypasses traditional time series constraints. • RangeEn can assess entropy series for regularity to avoid excessive decomposition. • A pioneer confusion test for model direction consistency check was developed. [ABSTRACT FROM AUTHOR]

The increase in air pollution has posed numerous new challenges for human society, making the exploration of an effective method for predicting air pollutant concentrations highly significant. The current research faces several primary challenges: the neglect of non-Euclidean characteristics of site distribution on data and the strong spatiotemporal dependencies in the dispersion process of pollutants. To address these issues, this paper constructs a spatiotemporal hybrid prediction model – the MGAtt-LSTM method – for predicting PM 2.5 concentrations, which employs the dynamic multi-graph attention module (MGAtt) to tackle spatial dependencies and Long Short-Term Memory networks (LSTM) to address temporal dependencies. Additionally, extensive experiments are conducted by using historical air pollutant monitoring data and meteorological data from the Beijing-Tianjin-Hebei region. The results demonstrate that the proposed MGAtt-LSTM model achieved superior performance in concentration prediction compared to existing benchmark models. • The neural network consists of multi-graph attention network and LSTM network. • Air pollution data in North China and meteorological data are used to forecast. • The uneven distribution of pollutant sites is considered to predict PM 2.5 concentrations precisely. • The use of multi-graph attention networks addresses the issue of traditional GCN methods relying on fixed graph structures. [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]

Air pollution in urban areas poses a significant and pressing challenge for modern society. Unfortunately, the existing network of pollution detectors in many cities is limited in scope and fails to adequately cover the entire geographical area. Consequently, the implementation of spatial prediction algorithms becomes essential to generate high-resolution data. In this paper, we introduce two significant contributions: 1) We formalize the air pollution prediction problem as a Maximum A Posteriori (MAP) estimate within the framework of a Markov Random Field and 2) we propose a message-passing algorithm, which stands out as an efficient solution that surpasses the current state of the art. The experimental procedure has been carried out using the case study of the city of Barcelona, based on a dataset extracted from the BCN Open Data portal. [Display omitted] • Spatial prediction of air quality is tackled with a novel formalization and method. • The use of a Message Passing style algorithm is proposed. • The model is trained with data specific to the city of Barcelona. • The presented method outperforms other existing methods for the given problem. [ABSTRACT FROM AUTHOR]

Exposure to ambient air pollution is associated with a significant number of deaths. Much of the evidence associating air pollution with adverse effects is from North American and Europe, partially due to incomplete data in other regions limiting location specific examinations. The aim of the current paper is to leverage satellite derived air quality data to examine the relationship between ambient particulate matter and all-cause and cause-specific mortality in Asia. Six cohorts from the Asia Cohort Consortium provided residential information for participants, recruited between 1991 and 2008, across six countries (Bangladesh, India, Iran, Japan, South Korea, and Taiwan). Ambient particulate material (PM 2·5) levels for the year of enrolment (or 1998 if enrolled earlier) were assigned utilizing satellite and sensor-based maps. Cox proportional models were used to examine the association between ambient air pollution and all-cause and cause-specific mortality (all cancer, lung cancer, cardiovascular and lung disease). Models were additionally adjusted for urbanicity (representing urban and built characteristics) and stratified by smoking status in secondary analyses. Country-specific findings were pooled via random-effects meta -analysis. More than 300,000 participants across six cohorts were included, representing more than 4-million-person years. A positive relationship was observed between a 5 µg/m (Dockery et al., 1993) increase in PM 2·5 and cardiovascular mortality (HR: 1·06, 95 % CI: 0.99, 1·13). The additional adjustment for urbanicity resulted in increased associations between PM 2.5 and mortality outcomes, including all-cause mortality (1·04, 95 % CI: 0·97, 1·11). Results were generally similar regardless of whether one was a current, never, or ex-smoker. Using satellite and remote sensing technology we showed that associations between PM 2.5 and all-cause and cause-specific Hazard Ratios estimated are similar to those reported for U.S. and European cohorts. This project was supported by the Health Effects Institute. Grant number #4963-RFA/18–5. Specific funding support for individual cohorts is described in the Acknowledgements. [ABSTRACT FROM AUTHOR]

Air pollution corresponds to one of the considerable challenges and disastrous sides of the environment that causes severe damage to all its biodiversity, including humans. As a result, establishing efficient, reliable, and interpretable methods and techniques to predict and control air quality is a must to preserve the environment and consider the necessary precautions. Most traditional machine learning models often lack transparency, making it challenging to interpret their decisions, especially in vital domains like air pollution. This paper proposes a novel approach that leverages granular computing to extract interpretable rules for air quality classification. We demonstrate the effectiveness of our approach through experiments on a real-world air quality dataset, showcasing the interpretability of the extracted rules and their accuracy in classifying air quality levels. The output of the proposed GrC model is a tree-like structure minimizing the entropy, allowing an easier interpretation of the classification results. A comparison is conducted with some widely used machine learning algorithms, including decision tree classifier, random forest, and CatBoost. The results indicate that the proposed granular computing rule extraction approach shows a competitive performance according to traditional black-box models in terms of accuracy (79%), transparency and reliability. The developed GrC model and the findings of this study not only contribute to advancing the field of air quality classification but also bear broader implications for environmental research and management for relevant and informed decision-making. [ABSTRACT FROM AUTHOR]

Nowadays, air pollution has become one of the most serious environmental problems facing humanity and an inescapable obstacle limiting the sustainable development of cities and society. Although air quality sensing and management systems based on artificial intelligence and signal analysis are evolving as essential parts of intelligent cities, the mixture of local emission sources and regional transport of air pollutants still makes PM 2.5 long-term prediction challenging, especially under complex geographical and climatic conditions. In this paper, the complete ensemble empirical mode decomposition based multi-stream informer (CEEMD-MsI) is proposed to predict the hourly PM 2.5 concentration, and extensive testing and comparisons are carried out in four typical cities in Shandong, China. Firstly, CEEMD is used for signal pre-processing to construct the intrinsic mode functions (IMFs) based multi-channel representations. Then MsI is specifically designed to learn both temporal and spatial features, and complete the PM 2.5 concentration prediction. To the best of our knowledge, this is the first attempt to predict long-term PM 2.5 concentrations using a deep learning model driven by data collected from monitoring stations spanning long distances and diverse terrains. Finally, test results demonstrate that CEEMD-MsI achieves the best PM 2.5 prediction performance by comparing it with state-of-the-art methods. [ABSTRACT FROM AUTHOR]

• Air pollution suppresses bank risk-taking. • The effect of air pollution is mainly driven by the frequency of extreme pollution days. • The effect of air pollution is more pronounced on banks located in cities with higher government environmental concern. • The effect of air pollution is more pronounced on banks with smaller asset scale and lower risk resistance. • The transparent environmental supervision weakens the effect of air pollution. In the context of frequent environmental events that attract widespread attention, this paper investigates the impact of air pollution on bank risk-taking. Using the data of 105 city commercial banks in China from 2014 to 2021, our findings suggest that air pollution leads banks to reduce risk-taking, which is primarily driven by the frequency of extreme pollution days. Banks in cities with higher government environmental concern and those with smaller asset scale and lower risk resistance are relatively more sensitive to air pollution. The transparent environmental supervision weakens the effect of air pollution on bank risk-taking. [ABSTRACT FROM AUTHOR]

Although the management of emerging environmental risks has received more attention recently, planning and public policy scholars have not yet generated consensus on management approaches for environmental and occupational risks that remain poorly described, unquantified, and may be potentially 'unknowable'. Our case study focuses on exposure to respirable mineral dust (RMD), a specific category of particulate air pollution which includes examples such as respirable crystalline silica and asbestos fibres, as an example of a risk that is emerging and that has often 'escaped' regulations in many countries. This article (i) reviews prevailing risk management strategies and (ii) discusses their limitations when applied to the unique characteristics of RMD. The aim of the paper is to examine the suitability of existing risk management approaches for the evaluation of emerging environmental risks, which are bounded by significant uncertainties in quantifying the likelihood of the occurrence and/or severity of the hazard. We identify potential improvements to these strategies, highlighting the value of different protection levels, strategies, measures, and proactive actions which are enabled within an adaptive management framework that responds more nimbly to future changes. • Synthesised the challenges of managing respirable mineral dust. • Seven risk management approaches are most useful for different risk characteristics. • Identified improvements to these strategies for emerging environmental risks. • Potential improvements of strategies that respond more nimbly to future changes. [ABSTRACT FROM AUTHOR]

Urban greenery is identified as a potential tool in air pollution mitigation. However, the impact is still debated. This paper introduces the innovative VIDA (Vegetation Impact Dynamic Assessment) model, specifically designed to quantify air pollution removal through deposition on vegetation. The VIDA model offers an advanced representation of vegetation that could be integrated into urban air quality dispersion models in the future. Furthermore, the model serves as a valuable tool for exploring the intricate interactions among deposition, resuspension, and wash-off processes, as well as understanding how meteorological conditions and various leaf traits influence these processes. The current version of the model focuses on particulate matter (PM) and encompasses a range of processes, including deposition on vegetation surfaces, encapsulation within the waxy cuticle, wind-driven resuspension, and wash-off. Additionally, the model takes into consideration dynamic changes in PM concentrations on the leaf surface over time, incorporating factors such as PM size fractions, meteorological conditions, and leaf characteristics. This comprehensive approach allows for the evaluation of various species or species groups based on their distinct traits. The VIDA model effectively reproduces measured data, yet continued evaluation remains crucial as new data emerges. Notably, challenges were encountered due to data scarcity and the absence of standardized methods for characterizing vegetation traits. Addressing these challenges and refining the representation of wash-off process will enhance the VIDA model's utility in predicting the dynamic relationship between vegetation and air quality. The introduction of VIDA provides a significant advancement in modelling air pollution removal by deposition to vegetation at a relevant local scale and enables inclusion of urban greenery as tool in urban planning for air pollution mitigation. • The VIDA model quantifies air pollution removal through deposition on vegetation. • VIDA models particle deposition, cuticle encapsulation, resuspension, and wash-off. • VIDA enables inclusion of urban greenery in air pollution mitigation. [ABSTRACT FROM AUTHOR]

Aerosol types have crucial and distinguished impacts on climate effects and cause the difference of air pollution. However, direct retrieval of aerosol type by satellite remote sensing is difficult, and the satellite products of aerosol type are seriously lacking. At present, the identification of aerosol type is mainly based on ground-based observation data, but the coverage area of ground-based stations is limited, and large-scale spatial continuous monitoring cannot be carried out. The existing aerosol type identification methods based on satellite data need to combine multiple satellites data (e.g. Moderate-resolution Imaging Spectroradiometer (MODIS), Ozone Monitoring Instrument (OMI), etc.) which leads to a low temporal resolution. The research about aerosol type identification only based on geostationary satellite data is lacking. Therefore, aerosol type identification based on the multi-parameter threshold method and the neural network method are proposed and compared in this paper. The neural network jointly using spectral and temporal information was trained and tested by the measurements from 26 sites of Aerosol Robotic Network (AERONET) and Sun-Sky Radiometer Observation Network (SONET). For the overall validation, the aerosol type identification based on the neural network is better than that based on the multi-parameter threshold method, and the accuracy reaches 71.44%. Atmospheric environment monitoring and quantitative remote sensing can be supported by this study. • Aerosol type, neural network, geostationary satellite [ABSTRACT FROM AUTHOR]

Air pollution causes extreme toxicological repercussions for human health and ecology. The management of airborne bacteria and viruses has become an essential goal of air quality control. Existing pathogens in the air, including bacteria, archaea, viruses, and fungi, can have severe effects on human health. The photocatalysis process is one of the favorable approaches for eliminating them. The oxidative nature of semiconductor-based photocatalysts can be used to fight viral activation as a green, sustainable, and promising approach with significant promise for environmental clean-up. The photocatalysts show wonderful performance under moderate conditions while generating negligible by-products. Airborne viruses can be inactivated by various photocatalytic processes, such as chemical oxidation, toxicity due to the metal ions released from photocatalysts composed of metals, and morphological damage to viruses. This review paper provides a thorough and evaluative analysis of current information on using photocatalytic oxidation to deactivate viruses. [Display omitted] • Ventilation importance about Covid-19 virus leads to growing popularity of photocatalytic process. • UV-assisted photocatalytic approaches are utilized for deactivating pathogenic microorganisms. • Air filters with antimicrobial features can be helpful to prevent the virus spread. • TiO 2 -based photocatalysts demonstrate antiviral impacts against SARS-CoV-2 viruses. [ABSTRACT FROM AUTHOR]

This paper investigates the causal impact of fetal exposure to PM2.5 on birth outcomes, including birth weight, the incidence of low birth weight (LBW), and small for gestational age (SGA), based on a nationally representative birth record dataset in a developing country setting. We employed thermal inversion as the instrument variable (IV) for PM2.5 and leveraged the distinctive characteristics of rural China in the 1990 s to address identification challenges. Our IV estimates indicate that higher fetal PM2.5 exposure leads to lower birth weight and elevated probabilities of LBW and SGA. Due to the mortality selection in utero , weak male fetuses were more likely to be screened out by PM2.5 exposure, resulting in a comparatively lower vulnerability among the surviving male infants. Furthermore, infants born to less educated mothers exhibited increased susceptibility, a phenomenon not entirely explained by the sorting behaviors associated with the preference for cleaner air based on socioeconomic status. • A nationally representative birth record dataset in China was used. • We leveraged the distinctive features of rural China to address identification challenges. • Higher fetal PM2.5 exposure led to lower birth weight and elevated probabilities of LBW and SGA. • Weak male fetuses were more likely to be screened out by PM2.5 exposure. • Infants born to less educated mothers exhibited increased susceptibility. [ABSTRACT FROM AUTHOR]

Diverse citizens need to be involved in net zero transitions to ensure policy interventions do not entrench inequalities for people from minoritised or disenfranchised groups and ensure public engagement with rapid social changes. This paper illustrates the importance of designing enjoyable engagement activities aimed at segmented citizen and community groups, to ensure a broad cross-section of society can participate in environmental debate and policymaking. We analysed an evaluation sample of 857 people, representing 10.3% of the 8302 people directly engaged with the ClairCity project from six European countries. Women were under-represented in our activities, and younger participants preferred non-traditional forms of engagement like online games. Across all activities, multiple regression analyses found statistically significant relationships between younger age groups and enjoyment, and a positive correlation between enjoyment and understanding of air quality. Behaviour change intention was also significantly positively correlated to understanding, with 74% of participants indicating they would make a behaviour change to improve air quality. While climate change and air pollution are serious issues, public engagement to raise awareness and improve participation in policymaking does not have to be. To fully realise citizen-led emissions reduction, policymakers need to co-create engagement activities which are enjoyable, inclusive, and attractive to diverse citizens representing regional demographics. Ultimately, the more enjoyable and relevant the engagement activities, the more understanding people gain about the issues, and are therefore more likely to make a change to individual or community behaviours to reduce air pollution and carbon emissions and improve public and environmental health. [Display omitted] • Diverse communities need representation in climate/air pollution policymaking. • One size does not fit all: different activities attracted different demographics. • Activity enjoyment was significantly positively correlated to understanding. • Understanding was significantly positively correlated to behaviour change intent. • Policymakers should plan enjoyable activities to enable social learning between groups. [ABSTRACT FROM AUTHOR]

Persistent PM 2.5 pollution poses a serious threat to human health. Developing an accurate urban regional PM 2.5 forecasting is of practical significance for environmental protection. However, previous studies have mostly focused on individual monitoring stations, neglecting the influence of neighboring stations, which limits forecasting accuracy. Additionally, the PM 2.5 of a single monitoring station cannot reflect the overall situation of a region. Therefore, this paper develops a novel PM 2.5 spatiotemporal forecasting framework that combines graph convolutional module, temporal convolutional module, linear module. It enables the forecasting of PM 2.5 concentrations at multiple stations and multiple time steps in the future. Concretely, we utilize a mixed graph convolutional network to extract the spatial features of PM 2.5. Then, an improved temporal convolutional network, the second-order residual temporal convolutional network, is developed to capture complex temporal features. Following the classical "linear and non-linear" modeling strategy, a linear module is added to the forecasting framework. Experiments on the real air pollution dataset from Beijing demonstrate that our framework outperforms the state-of-the-art baselines. • Extracting and fusing spatiotemporal features of PM2.5 is crucial for prediction. • A novel spatiotemporal forecasting framework is proposed. • A second-order residual TCN is developed to extract complex temporal features. • Experiments on real air pollution dataset show our framework's superiority. [ABSTRACT FROM AUTHOR]

[Display omitted] VOCs and CO are considered to be one of the main factors causing air pollution. They have received widespread attentions due to their toxicity and serious threats to the environment and human health. Catalytic oxidation has been proved to be a promising and effective technology. As an eco-friendly and cheap material, cobalt-based catalysts based on Co 3 O 4 has become the most competitive and potential catalyst material for eliminating VOCs and CO. However, most of the pollutants are complex in composition, such as water vapor, sulfur dioxide, and other substances, which puts higher requirements on the low-temperature activity and stability of the catalysts. This work systematically presents recent advances in treating VOCs and CO with cobalt-based materials to fill this gap. Specifically, this paper focuses on the optimization strategy of activity and stability of cobalt-based materials and discusses the improvement mechanism of catalyst performance in detail. Finally, the challenges and prospects of cobalt-based catalysts in VOCs and CO oxidation were discussed. This work may guide the reasonable construction of catalysts with excellent activity and anti-toxicity for VOCs and CO oxidation. [ABSTRACT FROM AUTHOR]

In this paper we investigate the relationship between participation in global value chains and the environment from a spatial perspective. By drawing on an original dataset on global value chain participation, emissions of nitrogen oxides and sulphur oxides, and green patents for European regions, we present novel evidence about the relationship between global value chains, green technologies and air pollution at the regional level. Our findings suggest that although participation in global value chains may lead to lower polluting emissions, this effect largely depends on the capacity of regions to exploit the green knowledge deriving from participation and on the specific form of participation. When European regions are integrated with backward linkages (i.e., importing inputs to produce exports) they record lower levels of air pollution; conversely, participation through forward linkages (i.e., exporting inputs for other places' exports) leads to an increase in air pollution. Backward participation also come out to support the development of green technologies that mediate the effects of global value chains on the environment posited by the "Pollution Haven" hypothesis. Overall, the relationship between global value chains participation and air pollution will depend on the type of participation and on the capacity of territories to profit it for the development of green technologies. • Our research provides new evidence on the relationship between Global Value Chains (GVCs) and the environment by using an original dataset on EU NUTS-2 regions. • GVC integration may lead to lower polluting emissions. This effect largely depends on the green knowledge deriving from GVC participation. • Regions integrated through backward linkages record lower levels of polluting emissions and higher capacities to develop green technologies. • Integration through forward linkages is associated with higher level of polluting emissions but not with green technologies. • We propose a Technological Heaven Hypothesis for EU regions that would contrast the Pollution Haven Hypothesis. [ABSTRACT FROM AUTHOR]

While concentrations of most air pollutants have been decreasing in Europe over the last 20 years, O 3 is showing variable trends, with increasing average and decreasing peak concentrations. The complexity of O 3 chemistry adds to the difficulty of understanding both the trends observed and how concentrations can be mitigated. This paper tries to answer the following questions: which emission sectors should be targeted and what levels of reduction could be achieved? To address these reflections, an Atlas of O 3 chemical regimes has been constructed. For this Atlas, 22 European cities were selected and the surrogate model Air Control Toolbox (ACT) was used to evaluate the simulated changes in several ozone metrics as a result of reductions in road transport and industrial emissions. O 3 chemical regimes have been classified and put in perspective with meteorological and emission data at each city location and around. The O 3 sensitivity to road transport and industrial emissions differ from one city to another, but also for the same city when considering different ozone metrics and seasons (e.g., annual means versus SOMO35 or summer peaks). Counterproductive impacts yielding O 3 increase when emissions are reduced are mainly encountered in regions or periods where O 3 concentration are relatively low. In terms of meteorological factors, O 3 chemical regimes are mostly impacted by the amount of solar radiation received but wind speed also has a considerable impact. Most cases show a higher sensitivity to emission reductions from road transport or equal sensitivity to emission reductions from road transport and industry. Very few cases are more sensitive to emissions from the industrial sector. However, the response of annual or seasonal average O 3 metrics to industrial and road transport emissions can be considered relatively low with a maximum reduction of 33% for a 100% reduction of both industrial and road transport emissions. This is because anthropogenic emission can only mitigate ozone above a substantial natural tropospheric background. It is precisely this incremental anthropogenic ozone, which should be targeted by efficient policies as we also demonstrate that none of the cities studied would exceed the European target value with a 100% reduction in emissions. • An atlas of ozone chemical regimes has been constructed for 22 European cities. • O 3 sensitivity to road transport and industrial emissions differ when considering different ozone metrics and different periods of the year. • Counterproductive effects of precursors emission reductions on ozone are a concern where and when ozone concentrations are low. • On the contrary, significant reductions of precursor emissions are found to reduce the number of exceedances of the EU target limit value. • In most cities, mitigation of road transport yields larger benefit to ozone air pollution than mitigation of the industrial sector. [ABSTRACT FROM AUTHOR]

This paper examines the impact of health status, and of experienced disutility caused by air pollution, on individuals' preferences regarding the health risk reductions resulting from air quality improvement. We investigate if changes in the level of experienced disutility due to air pollution mediate the relationship between individuals' respiratory health status and their preferences regarding health risk reductions. For this purpose, we conducted a choice experiment study among inhabitants of four big Polish cities that have varying levels of particulate matter pollution. We used the modified Medical Research Council breathlessness scale, verified in clinical trials, to obtain information about respondents' health status, except for usually applied health status measures in stated preference surveys. We do not find any direct impact of health status (apart from smoking) on willingness to pay for air quality improvement. Our results indicate, however, that individuals who self-reported a respiratory diagnosis, as well as those who scored high on the breathlessness scale, are more likely to display a latent trait indicating experienced disutility due to air pollution, and that those who experience this disutility are, in general, more in favour of air quality improvement programmes. [ABSTRACT FROM AUTHOR]

Air quality estimation using low-cost sensors is a pressing issue, with meteorological factors often causing measurement discrepancies. Hygroscopicity, arising from humidity's interaction with particulates, leads to inaccurate PM concentration readings in laser-scattering low-cost PM sensors. Common remedies involve data removal during high relative humidity or reference station calibration, but these solutions are not always practical or accurate due to the localized nature of hygroscopicity. In this paper, the authors present an adaptive correction framework that dynamically models hygroscopicity effectively mitigating humidity's impact on particle measurements. The framework exploits historical sensors' data, providing real-time adaptability in any context without relying on reference data, thus improving air quality estimations from low-cost sensors. • Presentation of the MitH framework to model and mitigate hygroscopicity during low-cost PM sensor readings. • The main features are anomalies-tolerant, dynamic and adaptable, real-time and reference station-agnostic. • Inclusion of a corrective functions optimization module, minimizing RH and PM correlation. • The code is open source along with the low-cost and reference station-aligned datasets. • The proposed framework is exploitable for air pollution modelling, interpolation and visualization techniques. [ABSTRACT FROM AUTHOR]

Peroxyl radicals (RO 2) are important components of atmospheric radical cycling and generation, but their formation, distribution and evolution mechanisms in the atmospheric environment have not been investigated. In this paper, we propose a novel atmospheric RO 2 radical trapping membrane that can trap low carbon number (Rc ≤ 5) RO 2 radicals and identify their R-group structures by fluorescence spectroscopy and chromatography. We also analyzed the composition and evolution mechanism of RO 2 species under different meteorological conditions in the atmospheric environment of Lanzhou, China, to provide scientific support for the treatment and research of atmospheric chemical pollution. [Display omitted] • A novel furan-based fluorescent probe was prepared. • Developed an atmospheric RO 2 free radical capture membrane. • Measured the species distribution of RO 2 radicals in the urban atmosphere. • The distribution of peroxyl radicals under different weather was compared. [ABSTRACT FROM AUTHOR]

By implementing Principal Component Analysis (PCA) of multitemporal satellite data, this paper presents modeling solutions for air pollutant variation in three scenarios related to COVID-19 lockdown: pre, during, and after lockdown. Tropospheric NO 2 satellite data from Sentinel-5P was used. Two novel PCA-models were developed: Weighted Principal Component Analysis (WPCA) and Rescaled Principal Component Analysis (RPCA). Model results were tested for goodness-of-fit to empirical NO 2 data. The models were used to predict actual near-surface NO 2 concentrations. Model-predicted NO 2 concentrations were validated with NO 2 data acquired at ground monitoring stations. Besides, meteorological bias affecting NO 2 was assessed. It was found that the weather component had substantial impact on NO 2 built-ups, propitiating air pollutant decrease during lockdown and increase after. WPCA and RPCA models well fitted to observed NO 2. Both models accurately estimated near-surface NO 2 concentrations. Modeled NO 2 variation results evidenced the prolongated effect of the total lockdown (up to half a year). Model-predicted NO 2 concentrations were found to highly correlate with monitoring station NO 2 data collected on the ground. It is concluded that PCA is reliable in identifying and predicting air pollution variation patterns. The implementation of PCA is recommended when analyzing other pollutant gases. [Display omitted] • PCA is reliable for identifying and predicting air pollution variation. • WPCA and RPCA models well fitted observed NO 2. • Both models accurately estimated near-surface NO 2 concentrations. • Total lockdown had prolongated effect on NO 2 concentrations. • Weather conditions propitiated air pollutant decrease during lockdown and increase after. [ABSTRACT FROM AUTHOR]

Visualizing tools are now capable of synthesizing satellite imagery with Computational Fluid Dynamics (CFD) results. The new method is applied in Augsburg, Germany, and consists of two main phases: pre-processing and post-processing. The pre-processing phase involves creating geometry, mesh, and extracting results from simulations of traffic pollutant dispersion. In the post-processing phase, the results are combined with satellite images to produce visually optimized results. The demonstration of road traffic air pollution is based on real data from local air quality measurements and specific scenarios. The results indicate that using these visualization tools produce understandable and impressive images and videos. This enhances the public's comprehension of scientific results and raises awareness of environmental issues. An increased understanding of scientific results can reinforce the implementation of environmental policies by pressuring responsible authorities to take action. This paper provides a valuable tool for visualizing air pollution and facilitating public engagement with environmental issues. • A new visualization tool capable of combining earth-observation images with CFD results which represent air-pollution dispersion. • Numerical simulation of air pollution fed to scientific visualization with 3D computer graphics to create photo-realistic images. • Photo-realistic results can guide environmental policies down to local level. • Easily comprehensible results make the general audience understand environmental conditions and enhance action. [ABSTRACT FROM AUTHOR]

• The development of LUR approaches over the last decade was reviewed. • LCMs, mobile, and satellite data increase the spatiotemporal resolution of inputs. • Advanced statistics can better fit data with complex relationships and interactions. • LURs in the spatial form have been complemented by spatiotemporal models. • New developments to break through LUR methodologies were discussed. Land use regression (LUR) models are widely used in epidemiological and environmental studies to estimate humans' exposure to air pollution within urban areas. However, the early models, developed using linear regressions and data from fixed monitoring stations and passive sampling, were primarily designed to model traditional and criteria air pollutants and had limitations in capturing high-resolution spatiotemporal variations of air pollution. Over the past decade, there has been a notable development of multi-source observations from low-cost monitors, mobile monitoring, and satellites, in conjunction with the integration of advanced statistical methods and spatially and temporally dynamic predictors, which have facilitated significant expansion and advancement of LUR approaches. This paper reviews and synthesizes the recent advances in LUR approaches from the perspectives of the changes in air quality data acquisition, novel predictor variables, advances in model-developing approaches, improvements in validation methods, model transferability, and modeling software as reported in 155 LUR studies published between 2011 and 2023. We demonstrate that these developments have enabled LUR models to be developed for larger study areas and encompass a wider range of criteria and unregulated air pollutants. LUR models in the conventional spatial structure have been complemented by more complex spatiotemporal structures. Compared with linear models, advanced statistical methods yield better predictions when handling data with complex relationships and interactions. Finally, this study explores new developments, identifies potential pathways for further breakthroughs in LUR methodologies, and proposes future research directions. In this context, LUR approaches have the potential to make a significant contribution to future efforts to model the patterns of long- and short-term exposure of urban populations to air pollution. [ABSTRACT FROM AUTHOR]

Air pollution has a significant impact on human health and influences housing choices. Existing studies on determinant factors affecting property prices, including air pollution, mainly employed hedonic and spatial regression models that have limitations in capturing non-linear relationships and local interactions. Neglecting the non-linear relationships can lead to incomplete estimation impacts between explainable features and prices. To fill these gaps, this study used machine learning algorithms and SHAP (SHapley Additive exPlanations) to identify the relative importance of air pollutant variables on land values and their non-linear mechanism. The results showed that the Extreme Gradient Boosting (XGBoost) outperformed Ordinary Least Squares (OLS), Gradient Boosting Decision Tree (GBDT), Random Forest (RF), K-Nearest Neighbors (K-NN), and Extra Tree Regression (ETR) models in price prediction and capturing the non-linear relationship between air pollution and land values. Notably, Ozone (O 3) and Nitric Oxide (NO) concentrations contribute 1.64% and 1.47% to land value variation, respectively. The results confirmed the non-linear relationship and identified that the threshold effect between air pollution and land values is at mean concentration values. Furthermore, we observed that the negative influence of O 3 and NO on land values appears as their concentration level is higher than 29.3 and 13 ppb, respectively. This paper contributes valuable insights by advancing our understanding of the non-linear mechanism impact of air pollution on house and land values, informing environmental policymaking, and shedding light on housing decisions. [ABSTRACT FROM AUTHOR]

Agriculture has a historical significance in human society and its present-day role as a vital sector of the global economy is irrefutable. However, the increase in agricultural production prompted by the rising demands has resulted in negative environmental consequences associated waste generation. Agricultural waste (agro-waste) constitutes a significant portion of the waste generated worldwide, causing environmental contamination and posing health risks when not managed properly. It is therefore crucial to formulate pioneering approaches that effectively mitigate the adverse impacts of agricultural waste, necessitating a deeper understanding of agro-waste. This review provides an overview of agro-waste, offering insights into its generation, categorization, composition and associated environmental implications. The sustainable utilization of the agro-waste is explored addressing pressing waste management and resource optimization issues. The paper engages in a comprehensive discussion regarding how agricultural practices contribute to soil degradation, air and water pollution, emphasising their interconnection and the need for integrated solutions. Promising pathways for utilizing agricultural waste, encompassing composting, production of animal feed, bioenergy generation, fiber extractions and biochar production are explored. Additionally, the review examines the complexities of efficiently managing and utilizing agricultural waste, addressing challenges such as the diverse nature of waste sources, compositional variations, contaminants, and practical feasibility considerations. It also explores potential solutions, including the need for further research, infrastructure development, and the establishment of regulatory frameworks and policies to improve agricultural waste management. [Display omitted] • Concept of agricultural waste: Explores sources, composition, and classifications. • Examines the environmental consequences of improper disposal and mismanagement of agricultural waste. • Discussion on agricultural practices on soil degradation, air pollution and water pollution. • Discusses innovative and sustainable methods for utilizing agricultural waste. • Addresses the various challenges and obstacles in effectively managing and utilizing agricultural waste. [ABSTRACT FROM AUTHOR]

• Eight methods to obtain near-surface temperature to monitor CLUHI. • Two kinds of human thermal indices for CLUHI research. • Urban-rural contrast and regression analysis methods to calculate CLUHI intensities. • Spatiotemporal variation and six types of associated factors of CLUHI. • Five potential research directions. The urban thermal environment is closely related to the well-being of many city dwellers. Rich achievements have been obtained for canopy layer urban heat island (CLUHI) studies. Nevertheless, the monitoring and associated factors of CLUHI have not been systematically and timely reviewed. Therefore, this paper aimed to solve this issue to some extent by reviewing the fruitful research progress of CLUHI from the above-mentioned two aspects. The main findings were as follows. (1) Eight methods were adopted to obtain near-surface temperature data for CLUHI research, including four observation, two numerical modeling, remote sensing and data assimilation methods. (2) Air temperature was usually used rather than apparent temperature indices. Obvious differences existed between them, especially under humid hot or cold windy conditions. (3) The CLUHI intensity was generally defined as the differences in temperature between urban and suburban or rural stations or regions by population, land cover/land use, etc, or derived using regression analysis between temperature and impervious surface percentage. (4) The diurnal, monthly, seasonal or interannual variation of CLUHI has been generally analyzed in various global regions. (5) Six types of factors were analyzed, including meteorological conditions, air pollution, socioeconomic factors, urban underlying surface condition, inland or coastal type and landform conditions, and combined effects of some factors. (6) Five potential research directions were proposed, including improvement of data acquisition, sharing and use, considering apparent thermal indices, focus on the rarely studied regions, cities and scales, improving the calculation of CLUHI intensity, and attention to more associated factors and driving force analysis methods. This review can provide references for future CLUHI research and the construction of climate-resilient, livable, low-carbon cities. [ABSTRACT FROM AUTHOR]

• Air pollution will reduce the quality of enterprise environmental information disclosure. • The state of enterprises regulates the relation between air pollution and enterprise environmental information disclosure. • Air pollution reduces the level of non-monetary and monetary environmental information disclosure. This paper studies the relationship between air pollution and the quality of environmental information disclosure taking the A-share listed companies in Shanghai and Shenzhen from 2014 to 2020 as sample. The study found that: firstly, air pollution will reduce the quality of enterprise environmental information disclosure. The main reason is that senior executives living in air pollution areas are more likely to have negative emotions, and the negative emotions of senior executives will make them have a negative attitude towards environmental information disclosure, thus reducing the quality of environmental information disclosure of enterprises. Second, the nature of state-owned enterprises positively regulates the negative correlation between air pollution and enterprise environmental information disclosure, while senior executive shareholding weakens the relationship between air pollution and environmental information disclosure. Further analysis found that: first, air pollution reduces the level of non-monetary and monetary environmental information disclosure. The research provides empirical evidence that air quality changes affect micro-business behavior. [ABSTRACT FROM AUTHOR]

[Display omitted] • The Int-CF&Z/C filters feature a three-stage pore structure for multi-stage filtration. • The Int-CF&Z/C filters demonstrates excellent filtration performance in removing PM. • The Int-CF&Z/C filters have long-lasting antimicrobial properties. • The Int-CF&Z/C is suitable for use in a variety of challenging environments. • The Int-CF&Z/C filters naturally degrade within two months. The increasing air pollution has drawn attention to the importance of using protective masks. The non-biodegradable nature of conventional disposable masks can have a significant ecological impact. Cellulose materials have great potential in degradable filters. In this study, a three-layer cellulose pore structure was designed for an antimicrobial integrated cellulose filter (Int-CF&Z/C) based on the generalized Murray's law. Int-CF&Z/C was constructed using cellulose nanofibers to create cellulose filter layers with three different pore sizes. The three-layer pore-size gradient of Int-CF&Z/C not only enables Multiple filtration of particulate matter (PM) but also enhances dust capacity and reduces pressure drop. Then, the binary composite of ZIF-8 and CuO was added to the cellulose filter layer with the smallest pore size. This addition aims to improve the filtration performance, moisture resistance, and antimicrobial properties of the filter. The Int-CF&Z/C had a filtration efficiency of 97.7 % for non-oily PM 0.3 and 98.5 % for oily PM 0.3 , with a pressure drop of only 74 Pa. Furthermore, the filtration performance of the filter exhibited a significantly lower decline compared to that of an N95 filter when utilized in prolonged periods in humid environments. Importantly, the filter could completely degrade in compost soil within approximately two months. [ABSTRACT FROM AUTHOR]