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Buildings are often located near ambient air pollution sources such as wildfire or heavy traffic areas. While windows in buildings are intermittently open for free cooling or natural ventilation, increased leakage area can lead to elevated human exposure to air pollutants of outdoor origin. The objective of this study is to investigate the effectiveness of paper filter windows in reducing exposure to outdoor air pollution and improving indoor air quality. The physical properties of paper windows as filtration media were experimentally determined, based on which multi-zone indoor air quality and ventilation analysis (CONTAM) simulations were performed for a full-scale building. The results show that the outdoor-indoor air exchange rate of a building can increase about 100% when conventional windows are replaced with paper filter windows. Even with the increased air exchange rate, the infiltration of outdoor particles into the building was reduced about 57–77% for the particle size range of 7–300 nm. These findings imply that paper windows have potential benefits for controlling both outdoor originated pollutants and indoor-generated pollutants with minimal energy inputs, especially in cities and communities impacted by urban air pollution and wildfires. [Display omitted] • We studied removal of outdoor particles due to paper filter windows in buildings. • Paper windows reduced indoor concentrations of outdoor particles up to 77%. • Due to paper windows, air exchange rate of the building increased about 100%. • Paper windows reduced indoor-generated pollutants (e.g., CO 2) by 35–45%. Synopsis: This study investigates filtration and ventilation performances of paper filter windows. [ABSTRACT FROM AUTHOR]

• White crusts on the oil paintings created by Edward poynter in 1897 shows a wide variety of deterioration products including sulphates, oxalates, carboxylates and carbonates. • Metal ions in the ground layers have migrated via amorphous metal carboxylates in the paint layer, the metal carboxylates with a broader spectral band of ν as (COO−) leading to white crusts. • Crystallinity and quantity of CaCO 3 as a degradation product in the white crusts by evaluating the peak shift and calculating the integrated absorbance in the ATR-FTIR spectra. The formation of white crusts on the surface of oil paintings can affect both the physical and aesthetic values of the artwork. Characterisation of these white crusts enables better identification of methodologies and solutions when performing conservation treatments of oil paintings. In this paper, micro Attenuated Total Reflection-Fourier Transform Infrared (ATR-FTIR) spectroscopic imaging was used to investigate the white crusts and metal carboxylates and its relationship to the migration of lead elements, with analysis focused on two cartoons by Edward Poynter from the Heritage Collections at UK Parliament. The white crusts may be linked to the metal ion migration via metal carboxylates to the surface of the painting, resulting in the interaction between the metal ions and air pollutants. The results show that the white crusts on the Poynter cartoons are heterogenous, containing three different components (sulphates, oxalates and carbonates). This information has offered conservator guidance for cleaning treatments of the white crusts. Furthermore, metal carboxylates with different crystallinity (crystallised and amorphous) and with different metal ions (lead and zinc) were successfully characterised and their distribution were revealed in ATR-FTIR chemical images. These chemical images shed light on possible degradation process associated with relocation of lead and calcium from ground layers, and invasion of calcium from the air, to form the white crusts. In the ATR-FTIR spectra, qualitative analysis of one of the degradation products (CaCO 3) was combined with quantitative analysis, conducted by evaluating the peak shifts calculating the integrated absorbance. This enabled the authors to evaluate the crystallinity and formation directions of CaCO 3 in the white crusts. It is hoped that these results of the case study and advancement of knowledge in this paper can aid the development of scientifically supported and improved conservation strategies. These results can be used as a trigger for further investigation into the degradation mechanisms related to white crusts and pigments/binding media used in the paintings. [Display omitted] [ABSTRACT FROM AUTHOR]

Childhood and adolescence are critical periods for the development of the brain. However, a limited number of studies have explored how air pollution may associate with affective symptoms in youth. We performed a comprehensive review of the existing research on the associations between outdoor air pollution and affective disorders, suicidality, and the evidence for brain changes in youth. PRISMA guidelines were followed and PubMed, Embase, Web of Science, Cochrane Library, and PsychINFO databases were searched from their inception to June 2022. From 2123 search records, 28 papers were identified as being relevant for studying the association between air pollution and affective disorders (n = 14), suicide (n = 5), and neuroimaging-based evidence of brain alterations (n = 9). The exposure levels and neuropsychological performance measures were highly heterogeneous and confounders including traffic-related noise, indoor air pollution, and social stressors were not consistently considered. Notwithstanding, 10 out of the 14 papers provide evidence that air pollution is associated with increased risk of depression symptoms, and 4 out of 5 papers provide evidence that air pollution might trigger suicidal attempts and behaviors. Besides, 5 neuroimaging studies revealed decreased gray-matter volume in the Cortico-Striato-Thalamo-Cortical neurocircuitry, and two found white matter hyperintensities in the prefrontal lobe. Outdoor air pollution is associated with increased risks of affective disorders and suicide in youth, and there is evidence for associated structural and functional brain abnormalities. Future studies should determine the specific effects of each air pollutant, the critical exposure levels, and population susceptibility. • Air pollutants can induce neuroinflammation and oxidative stress in the CNS. • Air pollution (AP) increased the risks of depression and suicide in the juveniles. • Neuroimaging studies found AP-related brain structural and functional alterations. [ABSTRACT FROM AUTHOR]

For the very first time, on 22-March-2020 the Indian government forced the only known method at that time to prevent the outburst of the COVID-19 pandemic which was restricting the social movements, and this led to imposing lockdown for a few days which was further extended for a few months. As the impact of lockdown, the major causes of air pollution were ceased which resulted in cleaner blue skies and hence improving the air quality standards. This paper presents an analysis of air quality particulate matter (PM)2.5, PM10, Nitrogen Dioxide (NO2), and Air quality index (AQI). The analysis indicates that the PM10 AQI value drops impulsively from (40–45%), compared before the lockdown period, followed by NO2 (27–35%), Sulphur Dioxide (SO2) (2–10%), PM2.5 (35-40%), but the Ozone (O3) rises (12–25%). To regulate air quality, many steps were taken at national and regional levels, but no effective outcome was received yet. Such short-duration lockdowns are against economic growth but led to some curative effects on AQI. So, this paper concludes that even a short period lockdown can result in significant improvement in Air quality. [ABSTRACT FROM AUTHOR]

Sulfur trioxide (SO 3) is one of the major air pollutants in the flue gas of Fluid catalytic cracking (FCC) units during the regeneration process in refineries. However, it is difficult to remove SO 3 with the existing desulfurization systems. Nowadays, alkaline adsorbent spraying technology has been a promising method to remove SO 3. However, there is no relevant application research in the field of FCC regeneration flue gas. Therefore, in this paper, The SO 3 removal characteristics from FCC flue gas by activated carbon modified red mud adsorbent were studied, including modification method, reaction temperature and flue gas components. Combined with characterization analysis of adsorbents, the mechanism of SO 3 removal was studied. It was found that when the AC mass ratio is 10% and modification temperature is 400 ℃, the best LBRMAC adsorbent was achieved. When particle size is 0.096–0.125 mm and reaction temperature is 400 ℃, the highest SO 3 adsorption efficiency is up to 83.4%. The addition of NO has little influence on the adsorption efficiency of SO 3. The addition of SO 2 leads to competitive adsorption between SO 3 and SO 2 , resulting in a rapid decline in the removal efficiency of SO 3. Meanwhile, the characterization analysis proves that the SO 3 adsorption process mainly include chemical kinetics control stage and product layer diffusion stage. • Best modification of LBRMAC was achieved when the AC mass ratio is 10%. • Reaction temperature of 400 ℃ is optimal for SO 3 adsorption. • Particle size and SO 3 concentration have effects on SO 3 adsorption except NO. • There exists competitive adsorption between SO 2 and SO 3. • Chemical kinetics control stage and product layer diffusion stage mainly dominate SO 3 adsorption. [ABSTRACT FROM AUTHOR]

To meet the requirements of the update for the supersonic landing and take-off (LTO) cycle standard, a methodology for studying the LTO supersonic standard based on pollutant emission characteristics is proposed in this paper to analyze the thrust and time of the LTO. In this study, three distinct supersonic engine models were developed based on subsonic engine cores. Subsequently, the pollutant emission calculation models were developed to calculate the pollutant emission index (I e) of supersonic engines and analyze the pollutant emission characteristics. Findings confirmed the presence of a correlation between standard formulations and pollutant emissions. Finally, considering that the current LTO standard leads to excessive noise during the climb phase and inefficient combustion during the taxi/ground idle phase, the LTO supersonic standard was analyzed for the trade-off, which resulted in the more applicable and fairer standard being formulated. The results indicate that the LTO supersonic standard formulated in this paper proves to be more reasonable than the current LTO standard and accurately represents the pollutant emission characteristics of distinct supersonic engines during both the climb and taxi/ground idle phases. Consequently, this study's results have guiding significance for formulating the future LTO supersonic standard and predicting pollutant emissions from supersonic engines. • Outdated supersonic landing and take-off cycle standards are updated. • A trade-off analysis of the supersonic standard is developed. • More comprehensive supersonic engine models and supersonic emission calculation methods are developed. • The updated supersonic climb standard more closely approximates the emissions from the noise-reducing climb trajectory. • The updated supersonic taxi/ground idle standard is better able to meet the combustion efficiency requirements. [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]

In the last few years, subways have rapidly spread in many countries and have replaced different modes of commuting in some important areas. Despite the fact that passengers only spend a short time in the subway, pollution in the subway is devastating to human health and can cause various diseases, including respiratory diseases. With the development of artificial intelligence (AI), more and more scholars are keen to use this technology to predict and monitor pollution focuses, which in turn can screen the air quality in subways. This paper reviews the application of AI in prediction and control of indoor air quality (IAQ) in subways during 2010–2022. The results show that most of the prediction studies analyzed were conducted for PM 10 and PM 2.5 , and most of the control studies were conducted to optimize the subway ventilation system. Fewer studies have been conducted on the prediction of other air pollutants and on IAQ control facilities. This study attempts to provide guidelines for future AI to manage IAQ in subways. [ABSTRACT FROM AUTHOR]

Particulate matter (PM), one of the major air pollutants, is generated by variety of natural or man-made sources, leading to acute and chronic diseases in humans since the last few decades. Employing satellite-derived aerosol optical depth (AOD) data, a wide technique that allows the retrieval of the PM concentration to support diverse on-field applications, including eliminating regular monitor stations and enhancing quality of breathing air. In this current review, the collection of data like ground-level particulate matter concentration, AOD, and metrological data, along with data screening, model grid and grid alignment, have been discussed. This paper endeavours the various development of models like single-stage, multi-stage, hierarchical, and ensemble models for PM prediction. Furthermore, the multicollinearity problem in developing the optimization model has been addressed. In order to evaluate the model efficiency, different performance evaluation techniques have been explored. Finally, the paper presents future directions for PM retrieval with the help of AOD data, such as the need for increased spatial and temporal resolution satellite data, incorporating more sophisticated machine learning models, and developing a global PM product from satellite AOD. The present review also accentuated the determination of PM in the air, which might be advantageous for the environment and human beings. • Satellite-derived aerosol optical depth (AOD) data is used to retrieve PM concentration for on-field applications. • Data collection, screening, model development, and performance evaluation for PM prediction were discussed. • Parameter selection considerations. • Delineates PM retrieval from AOD and summarize the various models used for retrieving PM. • Future directions for PM retrieval using AOD data with machine learning models. [ABSTRACT FROM AUTHOR]

Docked vessels in ports can be one of the dirtiest emitters in terms of local air pollutants. This paper evaluates whether the regulation of Emission Control Areas in China reduces air pollutants emissions in port cities. The transboundary spillovers of air pollution, induced by meteorological conditions, results in air pollution concentrations at specific locations encompassing both locally generated pollutants and those originating from elsewhere. Therefore, it is challenging to discern the emission reduction effects of environmental regulatory policies through air pollutant monitoring concentrations. This paper develops an approach to isolate transboundary spillovers induced by the wind, estimate locally produced pollution, and utilize the concentrations of locally produced pollution to identify the treatment effects of the Chinese Emission Control Areas policy. The results find that the policy has significantly reduced the locally produced sulfur dioxide concentrations by 3.41% in port cities. However, the omission of transboundary spillovers could yield contrasting results, suggesting that ignoring the pollution transport could lead to erroneous conclusions. The research findings of this study hold significant policy implications, highlighting the importance of accounting for transboundary transport of air pollution in evaluating the local government's environmental efforts and implementing supra-city environmental policies to prevent air pollution. • We investigate the efficacy of marine fuel restrictions policy. • We develop a new approach to account for the transboundary air pollution spillovers. • The policy significantly reduced local sulfur dioxide concentrations in ports. • Ignoring pollutant spillovers may bias the estimate of environmental policy effects. [ABSTRACT FROM AUTHOR]

• Vertical atmospheric conditions were extracted from the ECMWF 2019. • Eliminating the inputs using multi-collinearity test. • Linear, nonlinear, and hybrid models were developed. • The results of hybrid models showed the best function in predicting surface-air pollution concentrations. Most metropolitan areas experience increasing health and environmental pressure from air pollutants. Air pollutant concentrations are mostly influenced by meteorological factors, pollutant sources, and topography. The vertical atmospheric conditions can affect the surface air pollution concentration. In this paper, to evaluate the relationship between the vertical meteorological characteristics and air pollution concentrations (CO, O 3 , PM 2.5 , PM 10 and SO 2) four different models including multi-linear regression (MLR), multi-linear perceptron (MLP), generalized regression neural network (GRNN), and adaptive Neuro-Fuzzy inference system (ANFIS) were developed. The vertical temperature difference, relative humidity, U and V wind components in different altitudes were extracted from the operational archive of the European Center for Medium Range Weather Forecast (ECMWF 2019) from March 2011 to December 2017. The variance inflation factor (VIF) and tolerance tests were utilized to remove the independent variables, which made multi-collinearity. Results showed that among the studied air pollutant variables, variation of PM 10 concentration was more influenced by vertical meteorological characteristics obtained using the GRNN model with R2 = 0.960, root Mean square error (RMSE) = 0.019 and mean absolute error (MAE) = 0.005 in tearing phase and R2 = 0.940, RMSE = 0.054 and MAE = 0.019 in testing phase. The results of ANFIS model for PM 2.5 concentration were acquired highest among the other air pollutant variables in tearing phase (R2 = 0.819, RMSE = 0.036 and MAE = 0.025) and in testing phase (R2 = 0.960, RMSE = 0.001 and MAE = 0.001). The results illustrated that GRNN and ANFIS models outperform other models for air pollutants prediction using vertical atmospheric conditions in the Sanandaj, Iran. [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]

[Display omitted] • The influence of co-firing ratio of ammonia in coal-fired process on the formation of CPM was explored. • The total FPM decreased when the ammonia co-firing ratio increased from 0 to 50%. • The conversion of sulfur from coal to CPM was promoted when the ammonia was mixed in coal combustion process under the same total calorific value. In this paper, the influence of zero-carbon ammonia co-firing ratio during coal/ammonia combustion on the generation of condensable particulate matter(CPM) was investigated. 0%, 10%, 20%, 30% and 50% ammonia were mixed into the coal combustion according to the energy input ratio to collect the discharged CPM and FPM in laboratory and the composition of flue gas was tested. The results show that mixing ammonia into coal combustion had little impact on the total concentration of CPM. The main reason is that the ammonia co-firing into coal can promote the formation of inorganic matter of CPM, and the concentration of organic matter decreased. The mass of FPM decreased when the proportion of ammonia is increased. In addition, co-combustion of ammonia inhibited the formation of SO 2 that discharge to the atmosphere. The main reason is that the mass of coal decreased with the increasing of calorific value of ammonia. In addition, the co-firing of ammonia can promote the conversion of S to FPM and CPM. The reason is that H came from NH 3 burned to produce water vapor, then water vapor can combine with sulfur dioxide. Peak areas of some organic substances are drawn with the variety of ammonia co-firing ratio. Most of the substances in alkanes are N-alkanes. The conclusions in this paper are of great significance to the further understanding of migration and transformation of pollutants under the influence of ammonia co-combustion. In addition, the research in this paper provides a theoretical basis for achieving the goal of carbon neutrality. [ABSTRACT FROM AUTHOR]

• Red mud-based catalysts for the removal of NO x , CO and VOCs have been reviewed. • The relationship between the structure of red mud and its performance was discussed. • Future prospects and directions for the development of such catalysts are proposed. Red mud, as a solid waste produced during the alumina production, can cause severe eco-environmental pollution and health risks to human. Therefore, the resourcing of this type of solid waste is an effective way for the sustainable development. This paper reviews the recent progress on red mud-based catalysts for the removal of typical air pollutants, such as the catalytic reduction of nitrogen oxides (NO x) by NH 3 (NH 3 -SCR) and the catalytic oxidation of CO and volatile organic compounds (VOCs). The factors influencing the catalytic performance and the structure–activity relationship have been discussed. Future prospects and directions for the development of such catalysts are also proposed. This review would benefit for the high value-added utilizations of red mud in mitigating atmospheric pollutions. [Display omitted] [ABSTRACT FROM AUTHOR]

• Thirteen cohort studies obtained from eleven articles were included in this systematic review. • PM 2.5 was the most reported pollutants in included studies. • PM 2.5 was associated with liver cancer mortality. Air pollution has previously been linked to several adverse health outcomes, but the potential association between air pollution and liver cancer remains unclear. We searched PubMed, EMBASE, and Web of Science from inception to 10 October 2021, and manually reviewed the references of relevant papers to further identify any related literature investigating possible associations between air pollution and liver cancer. Risk estimates values were represented by statistical associations based on quantitative analyses. A total of 13 cohort studies obtained from 11 articles were included, with 10,961,717 participants. PM 2.5 was the most frequently examined pollutant (included in 11 studies), followed by NO 2 and NO x (included in 6 studies), and fewer studies focused on other pollutants (PM 2.5 absorbance , PM 10 , PM 2.5–10 , O 3 , and BC). In all the 16 associations for liver cancer mortality, 14 associations reported the effect of PM 2.5 on liver cancer mortality. Eight associations on PM 2.5 were significant, showing a suggestive association between PM 2.5 and liver cancer mortality. Among 24 associations shown by risk estimates for liver cancer incidence, most associations were not statistically significant. For other air pollutants, no positive associations were presented in these studies. PM 2.5 was the most frequently examined pollutant, followed by NO 2 and NO x , and fewer studies focused on other pollutants. PM 2.5 was associated with liver cancer mortality, but there was no association for other air pollutants. Future research should use advanced statistical methods to further assess the impact of multiple air pollutants on liver cancer in the changing socio-environmental context. [Display omitted] [ABSTRACT FROM AUTHOR]

The present study evaluates ground-based downward surface shortwave radiation (R s) over the coastal region of Alicante (Southeastern Spain). Hourly measurements collected over the eleven-year period 2010-2020 are used. Two weather stations located over the region capital, Alicante, have been selected as representative of urban and suburban typologies. Two additional weather stations far from the city have been selected representing rural typologies. R s is significantly reduced over the urban station during the morning hours within the winter season compared to the observations recorded over the suburban and rural stations, with a global mean difference of -81 and -120 W/m2 at 10 LT, respectively. However, no significant differences are obtained during the midday sun, with a global mean difference of -20 W/m2 between the urban and rural stations. With the aim of explaining these differences, the current paper investigates the relationship between R s and different air pollutants: NO x , SO 2 , and fine particulate matter (PM 2.5 and PM 10) as well as the wind field measured at the urban and suburban stations. The results found in this work point towards a close relationship between R s and NO x concentrations annual cycles, which are also influenced by the prevailing wind circulations observed over the study area. A global mean NO x concentration of 107 µg/m3 is observed over the urban station at 10 LT during the winter season. In contrast, these high concentrations are significantly reduced over the suburban station, with global mean value of 40 µg/m3 at 10 LT, for this period of the year. [Display omitted] [ABSTRACT FROM AUTHOR]

Air pollution control policies in China have been experiencing profound changes, highlighting a strategic transformation from total pollutant emission control to air quality improvement, along with the shifting targets starting from acid rain and NO x emissions to PM 2.5 pollution, and then the emerging O 3 challenges. The marvelous achievements have been made with the dramatic decrease of SO 2 emission and fundamental improvement of PM 2.5 concentration. Despite these achievements, China has proposed Beautiful China target through 2035 and the goal of 2030 carbon peak and 2060 carbon neutrality, which impose stricter requirements on air quality and synergistic mitigation with Greenhouse Gas (GHG) emissions. Against this background, an integrated multi-objective and multi-benefit roadmap is required to provide decision support for China's long-term air quality improvement strategy. This paper systematically reviews the technical system for developing the air quality improvement roadmap, which was integrated from the research output of China's National Key R&D Program for Research on Atmospheric Pollution Factors and Control Technologies (hereafter Special NKP), covering mid- and long-term air quality target setting techniques, quantitative analysis techniques for emission reduction targets corresponding to air quality targets, and pathway optimization techniques for realizing reduction targets. The experience and lessons derived from the reviews have implications for the reformation of China's air quality improvement roadmap in facing challenges of synergistic mitigation of PM 2.5 and O 3 , and the coupling with climate change mitigation. [Display omitted] [ABSTRACT FROM AUTHOR]

Abrupt air pollution accidents can endanger people's health and destroy the local ecological environment. The appropriate emergency response can minimize the harmful effects of accidents and protect people's lives and property. This paper provides an overview of the key emergency response technologies for abrupt air pollution accidents around the globe with emphasis on the major achievements that China has obtained in recent years. With decades of effort, China has made significant progress in emergency monitoring technologies and equipment, source estimation technologies, pollutant dispersion simulation technologies and others. Many effective domestic emergency monitoring instruments (e.g., portable DOAS/FT-IR systems, portable FID/PID systems, portable GC-MS systems, scanning imaging remote sensing systems, and emergency monitoring vehicles) had been developed which can meet the demands for routine emergency response activities. A monitoring layout technique combining air dispersion simulation, fuzzy comprehensive evaluation, and a post-optimality analysis was proposed to identify the optimal monitoring layout scheme under the constraints of limited monitoring resources. Multiple source estimation technologies, including the forward method and the inversion method, have been established and evaluated under various scenarios. Multi-scale dynamic pollution dispersion simulation systems with high temporal and spatial resolution were further developed. A comprehensive emergency response platform integrating database support, source estimation, monitoring schemes, fast monitoring of pollutants, pollution predictions and risk assessment was developed based on the technical idea of "source identification - model simulation - environmental monitoring" dynamic interactive feedback. It is expected that the emergency response capability for abrupt air pollution accidents will gradually improve in China. [Display omitted] [ABSTRACT FROM AUTHOR]

The toll design problem (TDP) provides a quantitative approach to the design of road pricing schemes. Its practical use, however, can be computationally challenging if the formulated TDP requires time-consuming computer models to evaluate candidate designs, especially if such designs must account for multiple objectives. For TDPs to be of practical relevance to the real-world planning of sustainable transportation networks, efficient TDP solution heuristics must be developed. To this end, two surrogate-based solution heuristics for multi-objective TDPs are proposed in this paper. Surrogate-based optimization uses simple approximations to computationally expensive models in order to accelerate the discovery of good solutions. The general search strategy of the proposed heuristics is as follows. In each iteration of the heuristics, a pool of candidate pricing schemes with unique sets of tolling locations and associated tolling levels is generated. From this pool of designs, the heuristics use the surrogate models to screen for solutions that are expected to be nondominated and that meet a specified selection criterion. Then, these promising designs are evaluated by the computationally expensive models, and the outputs obtained from these evaluations are used to update the surrogate models. Both heuristics repeat this general process until a maximum number of iterations are completed, at which point the best TDP solutions are returned. In addition to the solution heuristics, this paper presents a transportation network paradox that highlights how transportation network interventions intended to reduce traffic emissions could have unintended effects on a population's exposure to pollutants. The paradox also is used to illustrate the practical complexity of accounting for environmental inequality objectives, as well as the relevance of multi-objective analysis approaches to transportation planning. Formulations of multi-objective TDPs that consider both travel and pollutant exposure-related objectives are also presented, including the objectives of reducing human intake of vehicle-generated air pollutants and of minimizing environmental inequality. The Sioux Falls and Chicago Sketch networks were used in tests that examined the relative performance of the heuristics, as well as the characteristics of pricing configurations obtained under different budget constraints. Among other results, the tests show that a pricing configuration could decrease total pollutant intake and environmental inequality, while at the same time producing an increase in pollutant concentrations in a significant number of pollutant receptor points. [ABSTRACT FROM AUTHOR]

In the past few decades, many urban areas around the world have suffered from severe air pollution and the health hazards that come with it, making gathering real-time air quality and air quality forecasting very important to take preventive and corrective measures. This paper proposes a scalable architecture to monitor and gather real-time air pollutant concentration data from various places and to use this data to forecast future air pollutant concentrations. Two sources are used to collect air quality data. The first being a wireless sensor network that gathers and sends pollutant concentrations to a server, with its sensor nodes deployed in various locations in Bengaluru city in South India. The second source is the real-time air quality data gathered and made available by the Government of India as a part of its Open Data initiative. Both sources provide average concentrations of various air pollutants on an hourly basis. Due to its proven track record of success with time-series data, a Long Short-Term Memory (LSTM) Recurrent Neural Network (RNN) model was chosen to perform the task of air quality forecasting. This paper critically analyses the performance of the model in two regions that exhibit a significant difference in temporal variations in air quality. As these variations increase, the model suffers performance degradation necessitating adaptive modelling. [ABSTRACT FROM AUTHOR]

• Prefer longer route with shorter distance within the ECA; sailing speed within the ECA is lower. • The larger difference the fuel price within and outside the ECA, the more savings the model can achieve. • A set of Pareto optimal solutions is provided for the decision makers. • Provide suggestions for the redesign of ECA. Pollutants such as nitrogen oxides ( NO x ), sulfur dioxide ( SO 2 ), and particulate matters (PM) generated by shipping industry are increasing in recent years. In order to control the ship emission pollution, the International Maritime Organization (IMO) has established the Emission Control Areas (ECAs). In the fierce competition of the shipping market, liner shipping companies are looking for strategies to maintain their core competencies under the emission control policy. To achieve this goal, this paper first proposes a bi-objective mixed integer linear programming model, aiming to optimize sailing routes and speeds within and outside the ECA while minimizing the total fuel cost and SO 2 emissions. Then, a new algorithm is developed to solve the proposed model by combining the two-stage iterative algorithm and fuzzy logic method based on ∊ -constraint. Finally, this paper compares and analyzes the navigation plan of a real sailing route considering and not considering the effects of ECA. Some experiments are conducted to analyze the effects of fuel cost, decision makers, and ECA boundaries on the total fuel cost and SO 2 emissions. The results indicate that the proposed model and algorithm can contribute to save fuel cost and reduce SO 2 emissions under the ECA policy and provide different Pareto optimal solutions. Thus, the effectiveness of the model and the efficiency of the algorithm are validated. [ABSTRACT FROM AUTHOR]

With the rapid development of China's industrialization, the extensive development of industrial models has led to increasing environmental pressure. In this paper, we use China as an example, combined with the Tapio elastic analysis method (Tapio model), modified gravity model, social network analysis (SNA), quadratic assignment procedure (QAP) regression and logit model, to explore a network effect on the decoupling of industrial waste gas emissions and industrial added value (industrial GDP). We use the special network structure of industrial waste gas emissions and industrial GDP to capture the network effect. The results show the following: (1) For industrial sulfur dioxide (SO 2), nitrogen oxide (NO x) and soot (dust) (SD), most provinces in China show obvious strong decoupling, and provinces with faster economic growth, such as the eastern coastal areas, have a larger proportion of strong decoupling than provinces with poor economic development. (2) According to the network analysis of the industrial GDP, SO 2 , NO x and SD, economically developed provinces such as Shanghai, Jiangsu, Zhejiang, Beijing and Guangdong are generally located at the core of the network and have a strong spatial spillover effect on other provinces; (3) The QAP regression results show that the spillover effects of industrial SO 2 , NO x and SD are largely influenced by industry structural similarities, geographic proximity, and the development of economy. (4) The logit regression results show that the decoupling of industrial waste gas emissions will be greatly affected by the spatial network structure of industrial waste gas emissions and industrial GDP. The provinces at the core of the network are more likely to undergo strong decoupling. Finally, based on the research results, this paper provides some recommendations. • A network effect on decoupling of industrial emissions and industrial added value. • The modified gravity model is used to build the network. • Provinces with faster economic development are more prone to pollution decoupling. • Provinces at the core of the network are more prone to pollution decoupling. [ABSTRACT FROM AUTHOR]

Outdoor pollutants are well documented and monitored due to existing air quality regulations in many parts of the world. However, as information regarding indoor air quality is still limited, there are not enough information for indoor air pollutants. Indoor air quality pollutants are harmful to human health and are especially dangerous to vulnerable populations, such as elderly people and children. The effects of indoor environment and indoor air pollutants on children and the elderly are being studied worldwide. Ultimately, this paper highlights the prospect of remodeling wood interior, an indoor air pollutants reduction strategy that will help improve indoor air quality and public health. Wood is an environmentally friendly building material with low thermal conductivity and humidity control, and wood interior remodeling can protect people's health from indoor air pollutants. In this paper, twelve indoor air quality factors were measured: comfort factor (temperature and relative humidity), particulate matter, biological pollutants (total airborne bacteria), Airborne asbestos fibers, and chemical pollutants (carbon monoxide and dioxide, formaldehyde, volatile organic compounds, radon, ozone and nitrogen oxides). The average value of the indoor air quality factors for 12 welfare facilities is as follows. The average particulate matter was less than 12μg/㎥, the average of carbon dioxidewas 754.2 ppm, the formaldehyde was 46.6μg/㎥, the volatile organic compounds was 335.0μg/㎥, the total airborne bacteria was 37.8 CFU/㎥and the radon was 59.5Bq/㎥. As a result, almost all of the measurement locations were found to satisfy the international indoor air quality guidelines. It is thus judged that interior remodeling using wood can improve the indoor air quality of welfare facilities, and that it is necessary to constantly measure indoor air quality for accurate indoor air quality analysis. • Twelve welfare facilities were remodeled using wood materials. • Twelve indoor air quality factors include biological pollutants were measured. • The PM value can be lowered when the room interior is remodeled with wood. • The higher the quantity of wood used, the lower the concentration of HCHO. • This paper established a basis for the improvement of IAQ with wood remodeling. [ABSTRACT FROM AUTHOR]

As the most abundant natural aromatic polymer, tens of million of tons of lignin produced in paper-making or biorefinery industry are used as fuel annually, which is a low-value utilization. Moreover, burning lignin results in large amounts of carbon dioxide and pollutants in the air. The potential of lignin is far from being fully exploited and the search for high value-added application of lignin is highly pursued. Because of the high carbon content of lignin, converting lignin into advanced carbon-based structural or functional materials is regarded as one of the most promising solutions for both environmental protection and utilization of renewable resources. Significant progresses in lignin-based carbon materials (LCMs) including porous carbon, activated carbon, carbon fiber, carbon aerogel, nanostructured carbon, etc., for various valued applications have been witnessed in recent years. Here, this review summarized the recent advances in LCMs from the perspectives of preparation, structure, and applications. In particular, this review attempts to figure out the intrinsic relationship between the structure and functionalities of LCMs from their recent applications. Hopefully, some thoughts and discussions on the structure-property relationship of LCMs can inspire researchers to stride over the present barriers in the preparation and applications of LCMs. [ABSTRACT FROM AUTHOR]

Pollution havens create environmental inequality issues. China's recent policy of directing high-pollution firms to migrate out of its capital, Beijing, offers a case of a pollution haven that was mandated, rather than resulting from firms' responses to environmental regulation. More importantly, it leads to a question that has been less discussed in previous literature: does a pollution haven strike back? After the highly polluting enterprises moved out of Beijing to neighboring Hebei province, Hebei has been blamed for the continuing poor air quality in Beijing, based on a perception that the pollutants emitted in Hebei are being blown back to Beijing. This paper investigates the existence of a pollution haven in Hebei and quantifies the contribution of Hebei's emissions to Beijing's air quality. We find that the proportion of output values and the relative concentration of high-pollution industries in Hebei increased when the large-scale out-migration began, while the indicators in Beijing decreased. We also find higher emissions of enterprises in Hebei than their counterparts in Beijing. Together, these findings indicate the existence of a "manmade" pollution haven. Further, based on high-frequency spatial data, we combine an Autoregressive Distributed Lag model and a Spatial Autoregressive Model and decompose the factors that affect Beijing's air quality into local and regional transport of air-borne pollutants. We find a significant effect of the neighboring cities' air pollution on Beijing's air quality. This indicates that the pollution haven indeed struck back. These findings imply the importance of regional policies to control air pollution. [ABSTRACT FROM AUTHOR]

The nitrogen-containing species and SO 2 generated during coal pyrolysis lead to environmental problems. This paper investigated the evolution behaviors of four identified nitrogen-containing species including hydrogen cyanide (HCN), ammonia (NH 3), nitric oxide (NO), nitrogen dioxide (NO 2), and sulfur dioxide (SO 2) from fast pyrolysis of four different types of coal in a fixed bed reactor in the 600–1200 °C temperature range. Results show that HCN yield increases as the temperature increasing while the NH 3 maximum yield exists a transition temperature ranging from 800 to 1000 °C that depends on coal category. The impact of pyrolysis temperature on the SO 2 yield is not linear. To further quantify the NH 3 formation source, a mathematical method was proposed to divide the NH 3 evolution profile into gas-gas reaction and gas-solid reaction two categories. The gas-solid reaction predominates the NH 3 formation with a ratio up to 75% in 1200 °C high temperature condition. In low temperature condition, which path is predominant depends on the coal category. In addition, the oxynitride including both NO and NO 2 were observed in pyrolysis process. Moreover, enhancing temperature can boost NO 2 yield and restrain NO yield. The NO and NO 2 come from the combination of nitrogen and oxygen functional group which are both directly split off the parent coal. In addition, the elements liberation sequence from fast to slow is carbon > sulfur > nitrogen. To be further, the liberation sequence of pyrolysis species from fast to slow is CO > SO 2 >NO > NO 2 >HCN > NH 3 , which can supply guides to remove pollutants in industry. • Multiple nitrogen species evolution behaviors during coal fast pyrolysis were well investigated. • A mathematic method was proposed to decouple the NH 3 formation paths. • The liberation sequence of pyrolysis species from fast to slow is CO > SO 2 >NO > NO 2 >HCN > NH 3. [ABSTRACT FROM AUTHOR]

In this paper, an improved mathematical model is proposed to analyze a special internal relationship between rainfall intensity and gas pollutants in urban atmospheric system, where the nonlinear relationship between precipitation and the concentration of pollutants in the air is considered, as well as the influence of random disturbances from the outside world. After the approximate solution of the system is obtained, the stability and bifurcation characteristics of the system with and without random disturbance are both analyzed. Theoretical analysis and numerical simulation results show that there is a close covariant relationship between rainfall and gaseous pollutants; the system has abundant bifurcation characteristics, which, in the physical sense, represents sudden changes of the gaseous pollutants in the urban atmospheric system; the urban atmospheric system can always be kept at a lower pollution level by applying slight disturbances to offset external disturbances. [ABSTRACT FROM AUTHOR]

There is a general consensus that private car ownership is a significant barrier to transport system change, specifically in regard to injuries, space, air pollutants, or greenhouse gas emissions. Observed changes in automobile characteristics also suggest that the system is becoming less sustainable, given trends towards larger cars with greater mass and horsepower. It is thus relevant to understand how the automobile system progresses. National statistics provide data on the technical side of car ownership, such as changes in vehicle specifics or national fleet size. This paper complements this view with a socio-psychological perspective on aspirational car ownership, i.e. the type of car people preferred to drive if given a free choice. Data is derived from an online panel (n = 1,211) representative of the German population, and also contains information on current car ownership, use, driving style, traffic behavior, attitudes towards traffic risks and safety measures, as well as political orientation. This allows for a discussion of driver segments in relation to the characteristics of cars, and hence to better understand the socio-psychological drivers of the development of the automobile system. [ABSTRACT FROM AUTHOR]

The flue gas pollutants emitted from industrial flue gas and automobile exhaust have serious harm to atmospheric environment and human health. Therefore, it is important to study the control and purification of flue gas pollutants. At present, adsorption and catalytic methods are extensively employed for the elimination of flue gas pollutants. Clay minerals, as a kind of substances widely existing in nature, have unique physical and chemical properties that make them great application in many fields, especially in the field of flue gas pollutants removal. This review presents the types and structures of common clay minerals and their modification methods. It depicts recent advances in the use of modified clay materials to remove flue gas pollutants, highlighting the adsorption performance and adsorption mechanism for CO 2 , SO 2 and VOCs, as well as the catalytic performance and catalytic mechanism for NO x and VOCs of modified clay materials. In addition, this paper also focuses on the recycling of polluted gases and their conversion into value-added products. Finally, the current challenges and future prospects of clay mineral materials in flue gas pollutant removal are introduced. This review provides new insights into using clay materials as adsorption and catalytic materials for flue gas pollutants removal. [Display omitted] • Clay-based adsorbents and catalysts for removal of flue gas pollutants are reviewed. • The types and modification methods for clay-based materials are discussed. • Various air pollutants removal performance and mechanism are briefed. • Challenges and future development of clay-based materials are proposed. [ABSTRACT FROM AUTHOR]

In the past few years, there has been significant progress in developing sensors for indoor air quality (IAQ) monitoring. Thanks to their miniaturization, low cost, and growing concerns for people's well-being, these sensors have become increasingly important. This paper reviews the latest metal oxide semiconductor (MOS)-based gas sensors published since 2019 for the WHO-priority gas pollutants. While MOS-based gas sensors initially had limitations, including low selectivity, high operating temperatures leading to high power consumption, and interference from humidity, these challenges have been overcome. Currently, MOS-based sensors can detect some of the WHO-priority target gases at room temperature, achieving guideline values established for human safety. This progress in MOS-based gas sensors instills optimism for their future role in IAQ monitoring despite the challenges that still need to be addressed. • Past 5 years MOS-based gas sensors for indoor air evaluation have been revised. • Sensors operating at room temperature with low limits of detection are now accessible. • Challenges, including long-term stability and humidity, still remained. • Sensors for SO 2 , naphthalene, tri-and tetrachloroethylene are foreseen. [ABSTRACT FROM AUTHOR]

Polycyclic aromatic hydrocarbons (PAHs), which are widely present in incompletely combusted air particulate matter <2.5 μm (PM2.5), tobacco and other organic materials, can enter the human body through various routes and are a class of environmental pollutants with neurotoxic effects. PAHs exposure can lead to abnormal development of the nervous system and neurobehavioral abnormalities in animals, including adverse effects on the nervous system of children and adults, such as a reduced learning ability, intellectual decline, and neural tube defects. After PAHs enter cells of the nervous system, they eventually lead to nervous system damage through mechanisms such as oxidative stress, DNA methylation and demethylation, and mitochondrial autophagy, potentially leading to a series of nervous system diseases, such as Alzheimer's disease. Therefore, preventing and treating neurological diseases caused by PAHs exposure are particularly important. From the perspective of the in vitro and in vivo effects of PAHs exposure, as well as its effects on human neurodevelopment, this paper reviews the toxic mechanisms of action of PAHs and the corresponding prevention and treatment methods to provide a relevant theoretical basis for preventing the neurotoxicity caused by PAHs, thereby reducing the incidence of diseases related to the nervous system and protecting human health. Sources of polycyclic aromatic hydrocarbons, neurotoxic effects and their mechanisms of action. [Display omitted] • PAHs are a class of environmental pollutants, which have a negative impact on nervous system. • PAHs can harm the nervous system by causing oxidative stress, altering DNA methylation, and impacting mitochondrial autophagy. • PAHs have been found to induce aberrant neurological development and neurobehavioral abnormalities in animal models. • Exposure to PAHs adversely affects the nervous system in children and adults. [ABSTRACT FROM AUTHOR]

Ships present a significant source of air pollution, contributing to environmental degradation and posing health risks. Boilers are a significant part of the vessels in which the water is heated to evaporate and generate steam. The boilers emit pollutants such as hazardous air pollutants (HAPs), particle pollution, and volatile organic compounds (VOC). This paper conducts an extended risk analysis for air pollution due to boiler operation on ships. An improved Z -numbers theory and Fault Tree Analysis (FTA) are adopted to predict risk. Whilst improved Z-number theory is capable of handling uncertainties inherent in risk assessment, the FTA presents systematically the causal relationships among various factors contributing to the risk of air pollution on ships. The findings show that the failure probability of air pollution during ship boiler operation is 2.08E-05 and BE-12 is the most significant event. Results provide valuable data to maritime stakeholders in fostering environmentally sustainable practices. • A risk analysis of air pollution due to boiler operation on ships is presented. • Risk analysis is carried out through improved Z-number and fault tree analysis. • The findings provide information to understand and mitigate air pollution risk. [ABSTRACT FROM AUTHOR]

Formaldehyde (HCHO) poses a significant threat as a common indoor air pollutant, leading to various health issues. However, effectively addressing HCHO removal at room temperature remains a considerable challenge. This paper presents the preparation of a robust, eco-friendly, and biodegradable composite cellulose nanofiber film, incorporating CeO 2 -Ag@MnO 2 catalysts and TEMPO-oxidized cellulose nanofiber (TOCNF), for high-efficiency HCHO removal at room temperature. A CeO 2 -Ag@MnO 2 ternary catalyst with a core–shell structure was constructed to enhance the catalytic oxidation activity and stability. This structure increased the number of active sites on the catalyst surface and enhanced the interfacial synergistic effect of Ce-Ag-Mn. The TOCNF physically adsorbed HCHO in the composite film, while the catalyst oxidized it to CO 2 and water. The composite films, particularly those with 20 wt% CeO 2 -Ag@MnO 2 catalyst, exhibited high HCHO removal rates of 91.2 % at 20 °C and 99.6 % at 60 °C. Furthermore, the TOCNF/20 CAM composite films demonstrated excellent mechanical properties and degradability. This composite film offers an efficient and eco-friendly solution for the catalytic oxidation of HCHO at room temperature. [Display omitted] • We propose a novel cellulose nanofiber composite film for high-efficiency HCHO removal at room temperature. • The composite films with 20 wt.% CeO 2 -Ag@MnO 2 catalyst, exhibit high HCHO removal rates of 91.2% at 20 °C and 99.6% at 60 °C. • The ternary catalysts with core–shell structure enhance the interfacial synergistic effect of Ce-Ag-Mn. • The TOCNF/20 CAM composite films demonstrated excellent mechanical properties and degradability. [ABSTRACT FROM AUTHOR]

• Assessment of ship emission performance under diverse decarbonization measures. • Technical options reduce more carbon and air pollutant emissions than operational. • Trade-offs between air pollutant reduction and decarbonization strategies. • Contribution in policies for reducing both carbon emissions and air pollutants. Shipping emits Greenhouse Gases (GHG), contributing to climate change and various air pollutants, posing risks on human health. To address GHG, maritime has responded with increasingly stringent legislations, including recent commitments to achieve net-zero emissions by 2050. Substantial investments have been made in cutting-edge technologies, alternative fuels and operational strategies ensuring emissions regulations compliance. This paper aims to quantitatively evaluate the emission performance of diverse decarbonization options, focusing on assessing their potential to mitigate GHG and addressing air pollutants. Our analysis utilizes a new set of load-dependent Emission Factors and employs Automatic Identification System data for 5 ship classes. While operational measures moderately reduce emissions, fuel shift to alternatives and on-board carbon capture and storage offer a more promising pathway towards decarbonization and air pollutants reduction. The outcomes intend to provide insights into the effectiveness of measures in reducing external costs, while serving as a valuable resource for regulatory authorities. [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]

Modernising Australia's old truck fleet and adopting a more stringent standard to reduce emissions and air pollutants is a primary objective for the Australian truck sector. Various strategies worldwide have been introduced to cut emissions and pollutants in the truck sector, such as a low-emission strategy supported by strict diesel standards and a zero-emission strategy to shift to battery-electric or hydrogen trucks. The paper focuses on emissions and local air pollutants of trucks under various transition scenarios at both the tailpipe and the wider supply chain including domestic power generation and hydrogen production. In contrast, for diesel, we focus on tailpipe outputs following fuel standards in Australia, given diesel is imported other than in some limited refineries. We compare and recommend actions that government and truck operators may take in the near to longer term in transitioning to cleaner energy. We tested a number of scenarios using a decision support system incorporating all the latest information on costs and emissions for all truck classes using diesel, electric or hydrogen. A key finding from our scenario tests is that the current electricity mix has high carbon emissions and air pollutants due to fossil fuel-fired sources for power generation. Without improvement in using renewable energy sources in the future, transitioning to electric trucks implies more carbon emissions and air pollutants in the atmosphere from power plants, even though electric trucks generate zero tailpipe emissions. The main motivation for switching to zero-emission trucks is energy cost savings. We urge the government to decide on a clear roadmap for the truck sector before the sector is in a position to take action to shift to low or zero-emission trucks without totally relying on the likely reduction of emission intensity in electricity and renewable energy production. [ABSTRACT FROM AUTHOR]

Volatile organic compounds (VOCs) are crucial air pollutants in indoor environments, emitted from building materials, furniture, consumer products, cleaning products, smoking, fuel combustion, cooking, and other sources. VOCs are also emitted from human beings via breath and whole-body skin. Some VOCs cause dermal/ocular irritation as well as gastrointestinal, neurological, cardiovascular, and/or carcinogenic damage to human health. Because people spend most of their time indoors, active control of indoor VOCs has garnered attention. Phytoremediation and microbial remediation, based on plant and microorganism activities, are deemed sustainable, cost-effective, and public-friendly technologies for mitigating indoor VOCs. This study presents the major sources of VOCs in indoor environments and their compositions. Various herbaceous and woody plants used to mitigate indoor VOCs are summarized and their VOCs removal performance is compared. Moreover, this paper reviews the current state of active phytoremediation and microbial remediation for the control of indoor VOCs, and discusses future directions. [Display omitted] • With well-known sources, humans are notable indoor VOCs sources. • Various herbaceous/woody plants are employed to mitigate indoor VOCs. • Most studies on phyto- & microbial remediation for indoor VOCs remains at Lab-scales. • Synergistic interaction of plant-microbe can enhance indoor VOCs removal. • Long-term field tests are needed to innovate biotechnology for indoor VOCs control. [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]

The current compliance networks of automatic air-quality monitoring stations in large urban environments are not sufficient to provide spatial and temporal measurement resolution for realistic assessment of personal exposure to pollutants. Small low-cost sensor platforms with greater mobility and expected lower maintenance costs, are increasingly being used as a supplement to compliance monitoring stations. However, low-cost sensor platforms usually provide data with uncertain precision. To improve the precision, these sensor platforms require in-field calibration. Our paper aims to demonstrate that data from each individual sensor system can be corrected using that sensor system's own data to achieve much improved data quality compared to a reference. However, in this procedure, there are practical difficulties such as individual sensor outputs from the multi-sensor system not being sufficiently available due to malfunctions for instance. We explore how this can be dealt with. In our opinion, this is a novel approach, of practical importance both to users and manufacturers. We present a detailed comparative analysis of Linear Regression (univariate), Multivariate Linear Regression and Artificial Neural Networks used with a specific aim of calibrating field-deployed low-cost CO and O 3 sensors. For Artificial Neural Network models, the performance of three common training algorithms was compared (Levenberg-Marquardt, Resilient back-propagation and Conjugate Gradient Powell-Beale algorithm). Data for this study were obtained from two campaigns conducted with 25 multi-sensor AQMESH v.3.5 platforms used within the activities of the CITI-SENSE project. The platforms were co-located to reference gas monitors at the Automatic Monitoring Station Stari Grad, in Belgrade, Serbia. This paper demonstrates that Multivariate Linear Regression and Artificial Neural Network calibration models can improve the output signal. This improvement can be measured by changes in the median and interquartile ranges of statistical parameters used for model evaluation. Artificial Neural Networks showed the best results compared to Linear Regression and Multivariate Linear Regression models. The best predictors for CO, in addition to CO low-cost sensor data, were PM 2.5 and NO 2 , while for O 3 , in addition to O 3 low-cost sensor data, the most suitable input predictors were NO and aH. Based on residual error analysis, we have shown that for CO and O 3 , a certain range of concentrations exists in which calibrated values differ by less than 10% from the reference method results. In addition, it was noted that for all models, CO sensors consistently showed lower variability between platforms compared to O 3 sensors. Image 1 • Precision of measured concentrations can be enhanced by using other available sensors. • Calibration predictors for CO were CO lc , PM 2.5 and NO 2 , for O 3 were O 3, lc , NO and aH. • ANN models perform better compared to linear or multilinear regression models. • Within certain ranges of CO and O 3 the error of calibrated data is less than 10%. • CO sensors show less variability between the platforms than O 3 sensors. [ABSTRACT FROM AUTHOR]

Research activity in the field of air pollution forecasting using artificial neural networks (ANNs) has increased dramatically in recent years. However, the development of ANN models entails levels of uncertainty given the black-box nature of ANNs. In this paper, a protocol by Maier et al. (2010) for ANN model development is presented and applied to assess journal papers dealing with air pollution forecasting using ANN models. The majority of the reviewed works are aimed at the long-term forecasting of outdoor PM 10 , PM 2.5 , and oxides of nitrogen, and ozone. The vast majority of the identified works utilised meteorological and source emissions predictors almost exclusively. Furthermore, ad-hoc approaches are found to be predominantly used for determining optimal model predictors, appropriate data subsets and the optimal model structure. Multilayer perceptron and ensemble-type models are predominantly implemented. Overall, the findings highlight the need for developing systematic protocols for developing powerful ANN models. • Research activity in ambient air pollution forecasting with ANNs continues to grow. • Forecasting of outdoor PM10, PM2.5, nitrogen oxides and ozone levels was widely done. • Feedforward and hybrid ANN model types were predominantly used. • Most of the identified model building steps were done in an ad-hoc manner. [ABSTRACT FROM AUTHOR]

Sweden has a policy goal of solving major environmental problems in Sweden within a generation, without increasing environmental or health problems in other countries. Following up this goal requires indicators for domestic and external footprints of Swedish consumption. This paper presents such macro-level indicators for the years 2008–2014. The new indicators are consistent with Swedish statistics from the System of Environmental-Economic Accounts. They combine a multi-regional input-output (MRIO) database, to capture the external components of Sweden's consumption, with national input-output, trade and environmental statistics. The hybrid MRIO-Sweden model provides a comprehensive environmental account for follow-up of the Generational Goal. This paper presents impacts from household consumption, government consumption and capital formation, covering emissions of greenhouse gases, sulphur dioxide, nitrogen oxides, and particulate matter smaller than 2.5 μm (PM2.5), land use, materials consumption, and blue water consumption. Except for land use, the majority (60% or more) of the environmental pressures due to consumption occurred outside Sweden in 2014; more than 90% of sulphur emissions and more than 80% of the water use fell abroad. The environmental pressures from consumption decreased over this period for all indicators (except materials consumption). This suggests an absolute decoupling between environmental pressure due to consumption and economic growth, which rose over the period. It is, however, too early to determine whether this is a genuine trend or a temporary stabilisation. [ABSTRACT FROM AUTHOR]

Abstract China's capital city, Beijing, has been suffering from sand and dust weather with high frequency since 2000. Billions of dollars have been invested into overcoming this hazard, but research with regard to the economic benefit of mitigating this hazard. This paper employed the contingent valuation method research, attempts to investigate the Beijing households' willingness to pay for sand and dust weather mitigation and its determinants. The willingness to pay data were obtained from a double-bounded discrete choice format. Because of 149 respondents (18% of the total samples) refused to pay for the goods, we applied the spike model for processing the data. The annual mean willingness to pay is 404.35 Chinese Yuan. Beijing households are willing to pay a total amount of 2.18 billion Chinese Yuan annually for mitigating sand and dust weather. Furthermore, respondents' income, presence of children, and social norm are significantly correlated with the willingness to pay. This paper suggested enhancing pro-environment education and that government credibility and will be of great importance in promoting public participation in sand and dust weather management. Graphical abstract Image 102424 [ABSTRACT FROM AUTHOR]

In urbanized regions with expansive impervious surfaces and often low vegetation cover, air pollution due to motor vehicles and other combustion sources, is a problem. The poor air quality days in Montreal, Quebec are mainly due to fine particulate matter and ozone. Businesses using wood ovens are a source of particulates. Careful vegetation selection and increased green roof usage can improve air quality. This paper reviews different green roofs and the capability of plants in particulate matter (PM), ozone (O 3 ) as well as nitrogen dioxide (NO 2 ) level reductions. Both the recommended green roof category and plants to reduce these pollutants in Montreal's zone 5 hardiness region are provided. Green roofs with larger vegetation including shrubs and trees, or intensive green roofs, remove air pollutants to a greater extent and are advisable to implement on existing, retrofitted or new buildings. PM is most effectively captured by pines. The small Pinus strobus ‘Nana’, Pinus mugho var. pumilio , Pinus mugho ‘Slowmound’ and Pinus pumila ‘Dwarf Blue’ are good candidates for intensive green roofs. Drought tolerant, deciduous broadleaved trees with low biogenic volatile organic compound emissions including Japanese Maple or Acer palmatum ‘Shaina’ and ‘Mikawa-Yatsubusa’ are options to reduce O 3 levels. Magnolias are tolerant to NO 2 and it is important in their metabolic pathways. The small cold-tolerant Magnolia ‘Genie’ is a good option to remove NO 2 in urban settings and to indirectly reduce O 3 formation. Given the emissions by Montreal businesses' wood ovens, calculations performed based on their respective complex roof areas obtained via Google Earth Pro indicates 88% Pinus mugho var. pumilio roof coverage can annually remove 92.37 kg of PM 10 of which 35.10 kg is PM 2.5 . The removal rates are 4.00 g/m 2 and 1.52 g/m 2 for PM 10 and PM 2.5 , respectively. This paper provides insight to addressing air pollution through urban rooftop greening. [ABSTRACT FROM AUTHOR]

In order to improve the prediction accuracy and real-time of the air pollutant concentration prediction, this paper proposes self-adaptive neuro-fuzzy weighted extreme learning machine (ANFIS-WELM) based on the weighted extreme learning machine (WELM) and the adaptive neuro-fuzzy inference system (ANFIS) combined air pollutant concentration prediction method. Firstly, Gaussian membership function parameters are selected to fuzzify the input values and calculate the membership degree of each input variable. Secondly, corresponding fuzzy rules are activated, and the firing strength is normalized to calculate the output matrix of hidden nodes. Then, the optimal parameters (C, M), weights are assigned to weighted ELM by using locally weighted linear regression, and the regularized WELM target formula with equality constraint is optimized by the Karush–Kuhn–Tucker (KKT) conditions, the output weight matrix is calculated, and finally the prediction output matrix is calculated. Based on the air pollutant concentration data collected in Datong, Taiwan, the data on the pollutants containing carbon monoxide (CO), nitric oxide (NO), PM 2.5 (particulate matter) and PM 10 , are selected by different historical time series lengths, using genetic algorithm-backpropagation neural network (GA-BPNN), support vector regression (SVR), extreme learning machine (ELM), WELM, ANFIS, regularized extreme learning adaptive neuro-fuzzy inference system (R-ELANFIS) and ANFIS-WELM are built for predict the concentration of each pollutant collected by single monitoring point in single-step time series. The experimental results show that the ANFIS-WELM presented in this paper has better prediction accuracy and real-time performance, realizes the prediction of multi-step time series on the basis of the ANFIS-WELM, and realizes the engineering application of the ANFIS-WELM algorithm package on the self-developed mobile source emissions online monitoring data center software system. [ABSTRACT FROM AUTHOR]

There is a huge demand for sophisticated materials and innovative strategies to create cost-effective and real-life solutions in security printing field to combat the grave threat due to forgery or counterfeiting of documents and products. In this study, nano ruby is dispersed in an eco-friendly, water-based screen-printing formulation containing modified chitosan as both luminescent colourant and resin to achieve superior security features against counterfeiting. Biopolymer chitosan is modified using 4-bromo-3-hydroxy-1,8-naphthalic anhydride through a green imidation reaction to obtain N-(6-bromo-5-hydroxy-1 H -benzo[ de ]isoquinoline-1,3(2 H)-dione) chitosan (CSNA) that display yellow fluorescence, good thermal stability, and low cytotoxicity. CSNA is used as a fluorescent resinated dye to formulate CSNA ink, devoid of any hazardous air pollutants or volatile organic compounds. To improve the security feature, ruby nanoparticles are dispersed in the CSNA ink to achieve nanoruby-CSNA (CSNAR) composite ink, which is further screen-printed on paper substrates. The CSNAR ink film exhibited advanced level security features, wherein the forger can detect only a bright yellow fluorescence from the prints under 365 nm UV light. However, the user can validate the originality of the print by viewing red and yellowish-orange emissions under specific excitation wavelengths, which is unique to the CSNAR ink, and therefore difficult for the forger to replicate. The good adhesion, lightfastness and abrasion resistance of the print combined with the multi-level security features open a new avenue for potential applications of CSNAR security inks/films in document/product authentication and packaging/pharmaceutical anti-counterfeiting applications. [Display omitted] • A green synthetic imidation reaction approach to prepare biocompatible CSNA. • Thermally stable CSNA as a resinated fluorescent dye in water-based formulation. • CSNAR composite security ink screen printed on paper substrate. • Photostable and abrasion resistant prints with multi-level security features. • Authenticity check of prints using features known to user but masked for forger. [ABSTRACT FROM AUTHOR]

Although trade does not only boost the economy but also exacerbates environmental transfers but still the evidence for air pollution's unequal exchange embodied in trade among the Belt and Road Initiative (BRI) countries is rarely discussed in the extant literature. From the perspective of an integrated evaluation framework comprising a multi-regional input-output model, system balance theory, and complex network theory, this research paper uncovers the spatial- and temporal changes of trade-induced air pollution inequality among countries along the BRI route for the time period from 2011 to 2016. The study findings point out that there exists an obvious spatial heterogeneity in the unequal relations among BRI economies. This implies that a large number of country pairs demonstrate weak inequalities, whereas a relatively small number of country pairs represent strong inequalities. In addition, the countries' positions indicate a unique characteristic of "duality and inverse", which is majorly manifested as pure causers represented by Moldova and Belarus, net beneficiaries symbolized by China and Russia, and pure victims exemplified by Bhutan. Meanwhile, nearly half of the dual identities are represented by Singapore. Besides this, the proposal and implementation of the BRI exert a positive influence on both system stability and inequality mitigation by supporting the balance of triangles. Finally, this paper posits that the environmental inequality system of the Belt and Road is not as complex as imagined since the 3-node motif classification is relatively simple. Our findings are helpful for mitigating air pollution inequalities so as to support the sustainable development of green BRI. • Evolution of the environmental inequality in BRI countries is studied. • The initiative exerts a positive influence on stability and inequality mitigation. • Most countries illustrate a feature of "duality and inverse". • Five identified subgraphs indicate that the system is predictable. [ABSTRACT FROM AUTHOR]

• A novel hybrid system is proposed with deterministic prediction and interval prediction. • Outlier detection and correction method are used to reduce the complexity of the series. • Data decomposition strategy is applied to decompose the original series to improve the prediction accuracy. • A multi-objective optimization algorithm is selected to determine the original weights and biases of the forecasting model. • Applied interval prediction to quantify the range of concentration change of air pollutants caused by uncertainties. A novel system regarding deterministic and interval predictions of pollutant concentration is constructed in this study, which can not only obtain higher prediction accuracy in deterministic prediction and also provide effective interval prediction of air pollutant concentration. In the deterministic prediction stage, the improved extreme learning machine combines outlier detection and correction algorithm, data decomposition strategy, and a multi-objective optimization algorithm to form a hybrid model for predicting pollutant concentration. Moreover, the applicability of the optimization algorithm was verified from theoretical and experimental analysis. In the interval prediction stage, three distributions are compared to mine, the traits of deterministic prediction errors are analyzed, and interval prediction is designed to quantify the uncertainties associated with pollutant concentration. To investigate the prediction performance of the proposed system, comparison experiments have been executed using the PM 2.5 concentration series from three cities. The results indicate that the system proposed in this paper outperforms comparison models in forecasting accuracy and has advantages for pollution prediction. [ABSTRACT FROM AUTHOR]

Black carbon (BC) has a large specific surface area and abundant surface functional groups, so it is traditionally regarded as an adsorbent and a metal catalyst carrier. In addition to this, it has recently been found that a variety of BCs can also be used as a metal-free catalyst to mediate the transformation and degradation of specific organic pollutants at room temperature. This paper reviews the recent findings and current developments of the mediating roles of BC in both chemical and microbial degradation of organic pollutants, including azo dyes, nitroaromatic compounds and halogenated hydrocarbons. It starts with a brief overview of the source, properties and sorption characteristics of BC and focuses on the mediating effects and mechanisms of various BCs (such as activated carbon, chars, graphite), as well as types of surface-modified carbon materials, on the transformation and degradation of organic pollutants. Moreover, it also introduces the research on engineered carbon-based nanomaterials (i.e., carbon nanotubes and graphene), which have similar physical and chemical characteristics, for reference. Finally, the application prospects of using BC as a metal-free catalyst to mediate the transformation and degradation of organic pollutants are discussed to provide a reference and expand new ideas for further research in this field. [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]