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In cooperation with the world’s second largest manufacturer of beverage cartons (SIG Combibloc®) for liquid foodstuffs an innovative off-take for neutralisation of reactive gas in a paper laminating machine was constructed. A great challenge during engineering work was ensuring a high concentration of the reactive gas where needed and at the same time minimising work place impact in a machine basically without housing. Preliminary 2D-models of the machine geometry proved to be insufficient in describing all the governing flow phenomena. A simplified 3D-geometry containing all important parts of the complex machinery was necessary for accurate predictions. It was found that the driving force of air movement and transport of reactive gas (which acts as an adhesive agent) from the reaction zone in the interior of the laminating machine to the outside is a boundary flow caused by the rapid movement of carton material and rotating cylinders. A physically correct simulation result of the boundary flow is a premise for correct prediction of air flow in and around the machinery. Lacking experimental data (due to an inaccessible geometry) a worst case scenario was constructed by generating a grid and using turbulence models that maximised mass transport in the boundary layer region and thus emission of (tracer)gas from the machine. CFD simulations were done using the geometry preprocessor Gambit, and the finite volume solver Fluent. The results of the analysis of the emission paths from the machine were surprising and led to the construction of an effective off-take relatively far away from the emission source. The chosen position ensures low disturbance of highly sensitive flow patterns inside the machine and diffusive mixing, dilution and contamination of the surroundings. The effect of the new off-take is an immediate and significant rise in air quality in the vicinity of the laminating machine and ensures maximum allowed concentration in the plant area. The product quality furthermore is uncompromised by the working off-take which was another important goal of this work. [Copyright &y& Elsevier]

Governments have implemented many regulations to solve traffic congestion and pollutant emissions. Traffic restriction (TR) policy is the most common and widely used traffic regulation. However, it is difficult to alleviate traffic pollution through TR policies. Only a few studies focus on TR policies in multiple cities. In this paper, we classify traffic restrictions by geographical factors and evaluate the impact of different policies based on PSM-DID first. Then, we classify the mixed restrictions and test their effects on air pollution. The results show that TR policy improves air quality regardless of the season. Moreover, the effect of TR policy is not affected by the superposition of the type of traffic restriction. For cities implementing the same TR policy, geographic factors can lead to different outcomes. This finding provides a reference for governments in reducing air pollution. • Traffic restrictions significantly affected air quality in different regions of China. • Cities implemented traffic restrictions have improved air quality in adjacent cities. • Effect of traffic restrictions does not depend on the number of mixed restrictions. [ABSTRACT FROM AUTHOR]

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

Abstract: Large-scale open burning of joss paper is an important ritual practice for deity worshipping during Buddhist and Taoist festivals. Since Buddhism and Taoism are two of the most popular religions in Chinese societies and some Asian countries, the impact of joss paper burning on the air quality needs further investigation. This study explores the concentrations of polycyclic aromatic hydrocarbons (PAHs) in ambient air during one of the most important festivals, in which large-scale burning of joss paper occurs in temples and in people''s houses. The PAH concentrations were measured simultaneously at a temple site and a background site during both the festival and non-festive (ordinary) periods. Each ambient sample was extracted by the Soxhlet analytical method (for both particle-bound and gas-phase) and analyzed with gas chromatography. Experimental results indicate that the total PAH concentration during the festival period is approximately 4.2 times higher than that during the ordinary period (5384ngm−3 vs. 1275ngm−3). This study also employed statistical methods including diagnostic ratios and principal component analysis (PCA) to identify the possible PAH emission sources. Joss paper burning and vehicular emissions are identified as the principal sources of airborne PAHs during the large-scale open-burning event. The results of this work provide useful information for public awareness concerning PAH emission from the open burning of joss paper. [Copyright &y& Elsevier]

[Display omitted] • Latrine odor release followed "rapid increase, continuous decrease, stable" pattern. • Temperature changes mainly drove the variation of fecal microbes and odor emission. • The composited microbial deodorant significantly suppressed odor emission. • Abundance of odor-producing bacteria were obviously decreased after deodorization. As an important source of malodor, the odor gases emitted from public toilet significantly interfered the air quality of living surroundings, resulting in environmental problem which received little attention before. Thus, this paper explored the odor release pattern of latrine feces and deodorization effect with composited microbial agent in Chengdu, China. The odor release rules were investigated in sealed installations with a working volume of 9 L for 20 days. The odor units (OU), ammonia (NH 3), hydrogen sulfide (H 2 S) and total volatile organic compounds (TVOC) were selected to assess the release of malodorous gases under different temperature and humidity, while the highest malodor release was observed under 45℃, with OU and TVOC concentration was 643.91 ± 2.49 and 7767.33 ± 33.50 mg/m3, respectively. Microbes with deodorization ability were screened and mixed into an agent, which composited of Bacillus amyloliquefaciens , Lactobacillus plantarum , Enterococcus faecalis and Pichia fermentans. The addition of microbial deodorant could significantly suppress the release of malodor gas during a 20-day trial, and the removal efficiency of NH 3 , H 2 S, TVOC and OU was 81.50 %, 38.31 %, 64.38 %, and 76.86 %, respectively. The analysis of microbial community structure showed that temperature was the main environmental factor driving the microbial variations in latrine feces, while Firmicutes, Actinobacteria, Proteobacteria and Bacteroidetes were the main bacteria phyla involved in the formation and emission of malodorous gases. However, after adding the deodorant, the abundance of Bacteroidetes, Proteobacteria and Actinobacteria were decreased, while the abundance of Firmicutes was increased. Furthermore, P. fermentans successfully colonized in fecal substrates and became the dominant fungus after deodorization. These results expanded the understanding of the odor release from latrine feces, and the composited microbial deodorant provided a valuable basis to the management of odor pollution. [ABSTRACT FROM AUTHOR]

In this paper we critically review the work "Assessing nitrogen dioxide (NO 2) levels as a contributing factor to coronavirus (COVID-19) fatality" (Ogen, 2020), stressing the fact that we think there are flaws in the published methodology. We do this as we think it is important, given the current deluge of 'COVID-19 related' publications, to clearly define what can be stated and what on the contrary, cannot be stated, due to limitations in terms of data quality and/or methodology. Development of countries' mortality rate during March and April 2020. Unlabelled Image • Main limitations of the Ogen, 2020 paper (on COVID and NO2) are stressed. • NO2 from satellite has limitations to track ground level pollution in cities. • The onset of the COVID outbreak is different for different countries. • Correlation of NO2 and COVID does not mean causality. • Interdisciplinary involvement (e.g. of epidemiologists) in COVID-related analysis is desirable. [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]

• 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]

In view of upcoming more stringent air quality limits and the ambition to align with the WHO guidelines, nitrogen dioxide (NO 2) pollution from traffic and other sources will remain a problem in the EU. To assess the impact of traffic measures and emission reductions in other sectors on NO 2 -concentrations, an EU-wide high-resolution NO 2 source apportionment web-application was developed. The application allows users to define scenarios in a user-friendly way and quickly visualize the NO 2 -concentrations at measurement stations and in cities. The user can configure a new Euro 7/VII emission standard and additionally define urban access regulations scenarios in cities. To capture the spatial scales of NO 2 pollution, the SHERPA source-receptor model was used in combination with the QUARK kernel dispersion model. The first model considers long-distance impacts, the latter considers the strong concentration gradients close to roads. This paper focuses on the methodology, a follow-up paper describes the web-application. • We present a method for fast sectoral and spatial NO 2 source apportionment. • The road transport sector is considered in high detail and at 100-m resolution. • The impact of a new vehicle emission standard for NO x can be simulated. • The effect of urban access regulations can be simulated in 948 European cities. • NO 2 concentration effects are visualized at measurement stations and over cities. [ABSTRACT FROM AUTHOR]

Based on the panel data of various daily air pollution indicators of 288 prefecture-level cities in China from January 1, 2015 to December 31,2018, this paper takes the first round of central environmental protection inspection (CEPI) as a quasi-natural experiment, and empirically analyzes its impact on urban air pollution by using multi-period difference-in-difference (DID) method. The results show that: (1) the first round of five batches of environmental inspection and two batches of "looking back" have significantly reduced the concentration of seven pollutants; (2) The first five batches of environmental inspection have a stronger impact on reducing air pollution than "looking back"; Moreover, in the short tel·in after the inspection, it still has a significant reduction effect on air pollution; (3) The impact of central environmental protection inspection on air pollution shows strong heterogeneity in different batches of inspections, urban scale and economic development level, as well as the air quality level of cities before the inspection. The research results of this paper provide corresponding policy recommendations and decision-making basis for the Chinese central government to further deepen the reform of environmental protection supervision mechanism and promote the emissions of multiple pollutants mitigation. [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]

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]

• 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]

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]

Atmospheric oxidizing capacity (AOC) is an essential driving force of troposphere chemistry and self-cleaning, but the definition of AOC and its quantitative representation remain uncertain. Driven by national demand for air pollution control in recent years, Chinese scholars have carried out studies on theories of atmospheric chemistry and have made considerable progress in AOC research. This paper will give a brief review of these developments. First, AOC indexes were established that represent apparent atmospheric oxidizing ability (AOIe) and potential atmospheric oxidizing ability (AOIp) based on aspects of macrothermodynamics and microdynamics, respectively. A closed study refined the quantitative contributions of heterogeneous chemistry to AOC in Beijing, and these AOC methods were further applied in Beijing-Tianjin-Hebei and key areas across the country. In addition, the detection of ground or vertical profiles for atmospheric OH·, HO 2 ·, NO 3 · radicals and reservoir molecules can now be obtained with domestic instruments in diverse environments. Moreover, laboratory smoke chamber simulations revealed heterogeneous processes involving reactions of O 3 and NO 2 , which are typical oxidants in the surface/interface atmosphere, and the evolutionary and budgetary implications of atmospheric oxidants reacting under multispecies, multiphase and multi-interface conditions were obtained. Finally, based on the GRAPES-CUACE adjoint model improved by Chinese scholars, simulations of key substances affecting atmospheric oxidation and secondary organic and inorganic aerosol formation have been optimized. Normalized numerical simulations of AOIe and AOIp were performed, and regional coordination of AOC was adjusted. An optimized plan for controlling O 3 and PM 2.5 was analyzed by scenario simulation. [Display omitted] [ABSTRACT FROM AUTHOR]

• A novel spatial general equilibrium model was constructed and the impact of air pollution on urban residents' welfare in China was studied. • Empirical study shows air pollution significantly reduces urban residents' welfare and migration cost reduces the negative impact of air pollution on the welfare of urban residents. • Policy suggestions suggested that air pollution should be balanced against the welfare of residents, and should not be cleaned up by "one size fits all" in the early stages of economic development. Air pollution and its effects are important issues of human society. However, few literatures on the effect of air pollution on urban residents' welfare considered the cost of migration. This paper constructs a novel spatial general equilibrium model and studies the impact of air pollution on urban residents' welfare in 274 cities at and above the prefectural level in China from 2007 to 2017, and investigates the role of migration costs. The theoretical analysis and empirical research show that air pollution has a significant negative impact on the welfare of residents. Further, the simulation results indicate that the reduction of migration cost and the improvement of air quality accounts for roughly 24.65% improvement of resident's welfare from 2007 to 2017 in China. Another conclusion is that there is an inverted U-shaped relationship between resident's welfare and air pollution. On another side, migration costs play a moderating role in the impact path of air pollution on residents' welfare. When the migration costs fall, it will help residents to move out of cities with poor air quality and improve the resident's welfare. The policy suggestions are as follows: (1) In the process of sustainable development, local governments need to coordinate the phased characteristics between air pollution and residents' welfare, effectively improve the livable degree of city and urban economic sustainability, and prevent the brain drain caused by excessive migration of residents. (2) Local governments should lower the migration costs between cities by deepening the reform of the household registration system, so as to promote the free flow of labor and ultimately improve the welfare of residents. [ABSTRACT FROM AUTHOR]

• Empirical evidence for NEV market sales responses to incentive policies is provided. • Purchase restrictions are effective in cities with poor air quality or large sizes. • The policy mix accounts for 16.9–47.6 % variation in NEV sales in distinct contexts. • The NEV industry has a tendency to transition from policy-driven to market-driven. • The removal of local fiscal subsidy has a significant, negative effect on NEV sales. Incentive policies play an essential role in the rapid expansion of new energy vehicles (NEVs). Based on a comprehensive panel dataset, this paper investigates the effectiveness of various demand-side incentives in 88 NEV pilot cities in China. First, to explore where policies work effectively, we segment the pilots into several categories and conduct group regression. The results indicate that purchase restriction policies are effective in cities with poor air quality or large sizes. Use restrictions take effect only in cities with poor traffic infrastructure. Charge subsidies work in cities with good air quality or good traffic infrastructure. Second, to measure the contribution of policies, we conduct Shapley decomposition and reveal that the policy mix accounts for 16.9–47.6 % of the variation in NEV sales in distinct city categories. The empirical results of the decomposition also confirm the tendency of a transition from policy-driven to market-driven in the NEV industry. Third, to further validate the role of purchase subsidies, we consider the policy removal event in 2019 as a quasi-natural experiment and demonstrate that the removal of local purchase subsidy had a significant, negative effect on NEV sales. In light of the above findings, we suggest that policymakers avoid making one-size-fits-all policies and design precise and personalized policies, such as purchase restrictions (charge subsidies) for cities with poor (good) air quality. Moreover, a long-term, dynamic subsidy scheme remains necessary for the current stage. [ABSTRACT FROM AUTHOR]

Purpose: We investigated whether a tungsten functional paper (TFP) shield and/or organ-based tube current modulation (TCM) can reduce the dose to the eye lens.Materials and Methods: All scans were performed using our routine head examination protocol (spiral acquisition, 120 kVp, noise Index 3.5) with an anthropomorphic head phantom. The dose reduction rate was measured by the following methods with a scintillation fiber optic dosimeter: (a) without any dose reduction techniques (Original scan), (b) TFP shield, (c) TCM, and (d) TFP shield plus TCM. Image noise and CT number were obtained and compared between the three groups. In addition, image noise in method (d) was measured with varying distances between the TFP shield and eye lens.Results: The reduction rates using TFP shield, TCM, and TFP shield plus TCM compared with those for the Original scan were 17.8 %, 13.6 %, and 27.7 %, respectively. Image noise (mean ± standard deviation) in the anterior region for the Original scan, TFP shield, TCM, and TFP shield plus TCM were 4.1 ± 0.2, 4.6 ± 0.2, 4.4 ± 0.3, and 5.0 ± 0.2, while the CT numbers were 19.3 ± 0.8, 23.8 ± 0.8, 19.6 ± 0.8, and 24.1 ± 0.8, respectively. Increasing the distance between the TFP and the eye significantly decreased the CT number when using TFP shield plus TCM (p < .05).Conclusion: TFP shield plus TCM reduced the dose to the eye lens in head CT while maintaining image quality with an air gap between the TFP and skin surface. [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]

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]

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]

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]

Classical functional linear regression models the relationship between a scalar response and a functional covariate, where the coefficient function is assumed to be identical for all subjects. In this paper, the classical model is extended to allow heterogeneous coefficient functions across different subgroups of subjects. The greatest challenge is that the subgroup structure is usually unknown to us. To this end, we develop a penalization-based approach which innovatively applies the penalized fusion technique to simultaneously determine the number and structure of subgroups and coefficient functions within each subgroup. An effective computational algorithm is derived. We also establish the oracle properties and estimation consistency. Extensive numerical simulations demonstrate its superiority compared to several competing methods. The analysis of an air quality dataset leads to interesting findings and improved predictions. • Pairwise-penalization-based approach to determine the coefficient functions. • Oracle properties and estimation consistency of the proposed approach are built. • Great performance in numerical simulation and real data analysis. [ABSTRACT FROM AUTHOR]

Air quality prediction has garnered significant attention from both governmental bodies and the general public due to its close connection with people's daily lives. Learning spatio-temporal dependencies within air quality data are crucial for accurate air quality forecasting. Graph Convolutional Network (GCN) has been widely utilized in spatiotemporal data prediction due to its remarkable ability of capturing non-Euclidean spatial dependencies among nodes. However, previous research on Spatiotemporal Graph Neural Networks (ST-GNNs) has either utilized graph adjacency matrices predefined by heuristic rules or learned through trainable parameters. These approaches fail to accurately represent the true spatial relationships between stations and adaptively capture the required multi-level spatial dependencies for prediction. To address these limitations, this paper proposes an Adaptive Hierarchical Graph Convolutional Neural Network (AHGCNN) for P M 2. 5 prediction. More specially, an adaptive hierarchical graph convolutional structure is initially developed for dynamically extracting multi-level spatial dependencies. Then, a hierarchical aggregation scheme is designed to consolidate the unique topological structures of graph neural networks across different levels. Finally, a unified Adaptive Hierarchical Graph Convolution Gated Recurrent Unit (AHGC-GRU) model is proposed by integrating Gated Recurrent Unit (GRU) with hierarchical graph convolution in an effort to capture spatiotemporal dependencies of air quality data. Extensive experiments are conducted on real-world datasets, and the results demonstrate that the proposed model outperforms existing models. [ABSTRACT FROM AUTHOR]

As a rapidly developing country, Indonesia faces a challenge in improving air quality that is made more difficult by frequent forest fires linked to deforestation and volcanic eruptions. This paper analyses the link between development and air pollution in 30 Indonesian cities from 2002 to 2019. Air quality is measured using two novel regional air quality indicators (AQIs) for PM 2.5 and PM 10 estimated from weather observations and NASA satellite data on Aerosol Optical Depth (AOD). Results from an Environmental Kuznets Curve (EKC) analysis, based on a dynamic panel data model, show that forest fires and volcanic activity have significantly worsened regional air quality in Indonesia. The model also shows that the EKC hypothesis is rejected for Indonesia as cities have followed a monotonically increasing trend in air pollution with respect to economic growth. An additional 1% of economic growth increased long run pollution as measured by AQI PM 2.5 and PM 10 by 0.35% and 0.46%, and forest fire events by 0.11% and 0.06%. A partial Benefit-Cost Analysis (BCA) of forest fires shows that particulate matter pollution is excessive. Decreasing the frequency of forest fires by 1% would generate a net benefit of between US$17 million to 145 million. The lower estimate of the health benefits exceeds the agricultural benefits of forest fire without accounting for other non-market costs due to forest fires. Our results imply that, as Indonesia grows economically, air quality is likely to continue deteriorating. Further, regions affected by regular volcanic activity and wildfires should apply tighter emission standards to controllable point source emissions from industry and non-point source emissions such as agricultural forest fires. [ABSTRACT FROM AUTHOR]

COVID–19 has disrupted human life considerably since the beginning of 2020. The transport sector is one of the most affected sector due to restrictions imposed to curb COVID–19 pandemic. This paper evaluates the environmental impacts of COVID–19 response scenarios using life cycle assessment approach. Six regions selected for the study are Australia, China, European Union (EU), India, United Kingdom (UK), and United States of America (USA). Data for vehicle population, vehicle kilometers travelled, fleet type, and fuel efficiency were used for 2015–2020. The environmental impacts were calculated for five scenarios; Baseline–2019, Business As Usual (BAU) and COVID–19 Response Scenario (CRS) for 2020 and 2030 namely, 2020–BAU, 2020–CRS, 2030–BAU, and 2030–CRS. The results indicate that COVID–19 occurrence and related response measures did not contribute to significant environment benefits in terms of Global Warming Potential (GWP) and other pollutants in short term and long term. This implies that although immediate reductions in emissions were observed in many studies, the long term effects were minimal due to irregular policies and unmanaged demand. Further, to achieve environment benefits sustained measures should be implemented in long term with proper planning and supporting infrastructure. Although electric vehicle strategies will provide benefits; emphasis on vehicle production and fuel production technologies is also needed to achieve the emission reduction targets. The findings are relevant for policy makers while devising policies to achieve the targets. • Vehicle stock will grow significantly in 2030 for developing countries • China's 2030 transport sector GWP will be highest, 1772 MT CO 2 eq, surpassing USA • Only USA showed reductions for all pollutants in 2030 COVID-19 response scenario • All countries had similar trends for most pollutants at different life cycle stages • COVID–19 occurrence didn't contribute to significant benefits in short & long term [ABSTRACT FROM AUTHOR]

Emissions from ships stand as one of the principal contributors to air pollution in coastal areas. China has implemented the Domestic Ship Emission Control Area (DECA) policy progressively since 2016 to alleviate sulfur oxide and particulate matter pollution in coastal cities by limiting the sulfur content in marine fuel oil. This study utilizes Synthetic Difference-in-Differences model to construct a counterfactual framework for evaluating the DECA policy effect on air quality of cities in the Yangtze River Delta, the Pearl River Delta, and the Bohai Rim region. Results revealed that DECA policy significantly reduced the urban sulfur dioxide (SO 2) concentrations of YRD, BR, and PRD regions by 9.93 %, 8.64 %, and 7.09 %, respectively. However, the policy's efficacy in restraining fine particulate matter (PM 2.5) concentrations was found to be non-significant. This study further estimated the dynamic changes in SO 2 concentration in coastal cities of different regions in the DECA coverage. The results showed that the DECA policy generally began to significantly reduce SO 2 concentration levels in the second year after implementation, and with the tightening of the DECA policy, the reduction effect followed a growing-and-then-falling trend. Finally, the paper offers policy recommendations to further reduce ship emissions, aligned with recent emission reduction advancements. • Assessing the impact of China's DECA on coastal urban air pollution using a synthetic difference-in-differences model. • DECA effectively reduces SO 2 concentrations in coastal cities, yet its control over PM 2.5 levels is limited. • The impact of DECA varies across regions, with the most significant effects in the YRD, followed by BR and the PRD. • Several policy recommendations are proposed to optimize the effectiveness of DECA. [ABSTRACT FROM AUTHOR]

[Display omitted] • In-train air quality in UK diesel trains tested for 13 different train classes. • Means were PM 10 : 8–52; PM 2.5 : 4–36; BC: 1–11; NO 2 : 28–201 (µg/m3); UFP: 225–9131 #/cm3. • Large in-train variability was observed and associated with multiple factors. • Trains under 10 years old had the lowest in-train PM concentrations. • UK diesel trains measured lower PM 2.5 and PNC than other transport modes. Concentrations of particulate matter (PM 10 , PM 2.5), ultrafine (UFP), particle number (PNC), black carbon (BC), nitrogen dioxide (NO 2) and nitrogen oxides (NO X) were measured in train carriages on diesel and bi-mode trains on inter-city and long-distance journeys in the United Kingdom (UK) using a high-quality mobile measurement system. Air quality on 15 different routes was measured using highly-time resolved data on a total of 119 journeys during three campaigns in winter 2020 and summer 2021; this included 13 different train classes. Each journey was sampled 4–10 times with approximatively 11,000 min of in-train concentrations in total. Mean-journey concentrations were 7.552 µg m−3 (PM 10); 3.936 µg m−3 (PM 2.5); 333–11,300 # cm−3 (PNC); 225–9,131 # cm−3 (UFP); 0.6–11 µg m−3 (BC); 28–201 µg m−3 (NO 2); and 130–3,456 µg m−3 (NO X). The impact of different factors on in-train concentrations was evaluated. The presence of tunnels was the factor with the largest impact on the in-train particle concentrations with enhancements by a factor of 40 greater than baseline for BC, and a factor 6 to 7 for PM and PNC. The engine fuel mode was the factor with the largest impact on NO 2 with enhancements of up to 14-times larger when the train run on diesel compared to the times running on electric on hybrid trains. Train classes with an age < 10 years observed the lowest in-train PM, BC and NO X concentrations reflecting improvements in aspects of rail technology in recent years. Air quality on UK diesel trains is higher than ambient concentrations but has lower PM 2.5 and PNC than most other transport modes, including subway systems, diesel and petrol cars. This paper adds significantly to the evidence on exposure to poor air quality in transport micro-environments and provides the industry and regulatory bodies with reference-grade measurements on which to establish in-train air quality guidelines. [ABSTRACT FROM AUTHOR]

Recently, there have been growing concerns about the negative impacts of rising automobile use and road congestion on personal mobility, safety, air quality and climate change. To address these issues, special attention has been given to improving and expanding transit services in order to attract new riders and retain existing ones. Despite these efforts, transit ridership across Canada has been levelling-off and, in many cases, declining over the past few years. The goal of this study is to provide an empirical investigation of variables that affect variations in transit ridership among transit systems and over time. To achieve this goal, the study used a comprehensive longitudinal dataset of different factors collected from 103 transit agencies in Canada between 2002 and 2016. Two-stage least square (2SLS) models were developed for the purpose of the study. The results suggest that transit ridership (in terms of number of linked trips) in Canada is associated with several factors that differ according to transit agency size. These factors include built environment and socioeconomic aspects in addition to transit supply attributes. Additionally, ride-sharing systems (i.e., Uber service) have a mixed impact on ridership that varies according to the size of transit system. The presence of Uber within large transit agencies service areas is shown to have a small positive impact on ridership, while it is negatively associated with ridership for small transit agencies. This paper provides policy makers and planners with useful policy-relevant information related to the association between different factors and transit ridership. • The paper investigates the variables affecting ridership in Canada. • It uses a longitudinal dataset of various factors collected from 103 transit agencies. • Two-stage least square (2SLS) models were developed for the study. • Ridership is related to several factors that differ according to transit agency size. • Ride-sharing systems (i.e., Uber service) have a mixed impact on ridership. [ABSTRACT FROM AUTHOR]

Airborne microorganisms in food processing environments pose a potential risk for food product contamination. Yet, the absence of established standards or guidelines setting quantitative limits on airborne microorganisms underscores a critical gap in current regulatory frameworks. This review seeks to explore the feasibility of establishing quantitative limits for airborne microorganisms in food processing facilities, aiming to provide evidence-based guidance to enhance food safety practices in the industry. The review begins by addressing the complexities of microbial air quality in the food industry through a general literature search covering sources of airborne microorganisms, factors affecting particle deposition, air sampling methods and preventive measures. Subsequently, it employs a structured approach to assess the significance of air quality and its impact on product quality. Utilizing the PRISMA method, relevant scientific literature from May 2002 to May 2022 was examined, resulting in 26 articles meeting inclusion criteria from a pool of 11,737 original research papers. Additionally, the review investigates existing probability models for assessing airborne contamination to enhance air quality risk assessment in food safety management systems. The literature reveals a lack of substantial evidence supporting a direct correlation between airborne microorganisms and food contamination. The absence of standardized air sampling methodologies in previous studies hinders the comparability and reliability of research findings. Additionally, the literature fails to establish a conclusive relationship between influencing factors such as total particle counts, temperature, relative humidity and airborne contamination. Contradictory probability models for quantifying airborne contamination, and the absence of tailored preventive measures, hinder effective control and undermine microbial contamination control in diverse food processing contexts. In conclusion, the development of numeric guidelines for airborne contamination necessitates a tailored approach, considering factors such as product characteristics and production context. By integrating risk assessment models into this process, a more thorough comprehension of contamination risks can be achieved, providing tailored guidance based on the identified risk levels for each product. Ongoing collaborative efforts are essential to develop evidence-based guidelines that effectively mitigate risks without incurring unnecessary costs. • The extent of airborne contamination in the food industry remains unknown. • Quantitative data on particle deposition factors, such as airflow, is lacking. • Progress is hampered by inconsistent air sampling methods. • Probability models overlook production context and product characteristics. • Numeric guidelines need customization, integrating risk assessment models. [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]

Transport policies for improving air quality are increasingly being implemented in city centres across the world. While the effectiveness of those policies is markedly affected by a strong social controversy, one missing element is the role that stakeholders engagement can play in identifying impacts and potential implementation strategies. This paper diverges from other commonly accepted approaches by allowing stakeholders evaluate a set of transport policies for improving air quality. The city of Madrid (Spain), where there is a clear intention to restrict traffic in its centre, was taken as case study. Local stakeholders were engaged in a series of semi-structure interviews and a final face-to-face workshop to evaluate the potential impacts, acceptability, and feasibility of eight transport policies. Results show two distinct viewpoints: stakeholders from public institutions and eco-mobility sectors; and stakeholders linked to retail, logistics, and car-sharing services. Consensus between those two groups was hard to achieve for three specific policies: (i) license-plate restrictions; (ii) charges to motor vehicles; and (iii) development of walking and cycling infrastructure. The paper outlines the methodological process, reflects on the usefulness of stakeholder engagement in the policy-making process, and concludes with a discussion on the acceptability of the evaluated transport policies in the local context of Madrid. • A total of eight transport policies for improving air quality are evaluated. • A collaborative approach is used based on semi-structure interviews and a face-to-face workshop. • A total of 28 key stakeholders are engaged during an ex-ante evaluation. [ABSTRACT FROM AUTHOR]

• A 16-year update on the first UK environmental justice air quality study is given. • Emissions for private vehicles are attributed to census area of registered keeper. • Social inequalities in traffic-related pollution exposure are clearer and stronger. • Young children, adults, and households in poverty have highest levels of exposure. • A strong inverse relationship was found between poverty and emissions generation. This paper presents unique spatial analyses identifying substantial discrepancies in traffic-related emissions generation and exposure by socioeconomic and demographic groups. It demonstrates a compelling environmental and social injustice narrative with strong policy implications for the UK and beyond. In the first instance, this research presents a decadal update for England and Wales to Mitchell and Dorling's 2003 analysis of environmental justice in the UK. Using 2011 UK Government pollution and emissions data with 2011 UK Census socioeconomic and demographic data based on small area census geographies, this paper demonstrates a stronger relationship between age, poverty, road NO x emissions and exposure to NO 2 concentrations. Areas with the highest proportions of under-fives and young adults, and poorer households, have the highest concentrations of traffic-related pollution. In addition, exclusive access to UK annual vehicle safety inspection records ('MOT' tests) allowed annual private vehicle NO x emissions to be spatially attributed to registered keepers. Areal analysis against Census-based socioeconomic characteristics identified that households in the poorest areas emit the least NO x and PM, whilst the least poor areas emitted the highest, per km, vehicle emissions per household through having higher vehicle ownership, owning more diesel vehicles and driving further. In conclusion, the analysis indicates that, despite more than a decade of air quality policy, environmental injustice of air pollution exposure has worsened. New evidence regarding the responsibility for generation of road traffic emissions provides a clear focus for policy development and targeted implementation. [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]

Worldwide, one of the most important causes of mortality is air pollution. To solve this problem, governments have implemented policies to reduce on-road and industrial emissions. In this regard, the Barcelona city council and Transports Metropolitans de Barcelona (TMB) started implementing the Nova Xarxa de Bus (NXB) to redefine the bus network following the criteria of connectivity, efficiency, and rationality. This policy was implemented in seven phases from 2012 to 2018. In this context, this paper analyses the impact of this policy on Barcelona's air quality using a dataset from 2008 to 2016. Using a difference-in-difference approach, we show that implementing these new routes increased air quality in Barcelona. Additionally, we show that pollution decreased in each phase analysed, especially in the air quality stations near the main roads. From our results, we can infer that an optimal bus route design can improve air quality in urban areas. [ABSTRACT FROM AUTHOR]

Grasping how individual choices and exogenous factors shape agents' environmental preferences is essential to comprehending societal trends in pro-environmental behaviour. To this end, this paper proposes the concept of "individual environmental capital". It investigates how an individual's past pro-environmental decisions relate to their future ones, particularly in terms of willingness to pay for improvements in air quality. A randomised information treatment choice experiment was conducted in Poland to gauge individual environmental capital, accounting for physical assets and expressed policy preferences. The investigation found that willingness to pay varied depending on levels of prior investment in environmental assets and declared support for environmental regulations. Results demonstrate that highlighting one's past pro-climate endeavours may fail to differentiate the valuation of environmental protection among those who have already invested in environmental capital. However, it does seem to relate to a higher valuation for those yet to transition their energy systems. [ABSTRACT FROM AUTHOR]

In recent years, environmental problems caused by air pollutants have received increasing attention. Effective prediction of air pollutant concentrations is an important way to protect the public from harm. Recently, due to extreme climate and social development, the forest fire frequency has increased. During the biomass combustion process caused by forest fires, the content of particulate matter (PM) in the atmosphere increases significantly. However, most existing air pollutant concentration prediction methods do not consider the considerable impact of forest fires, and effective long-term prediction models have not been established to provide early warnings for harmful gases. Therefore, in this paper, we collected a daily air quality data set (aerodynamic diameter smaller than 2.5 µm, PM 2.5) for Heilongjiang Province, China, from 2017 to 2023 and A novel Long Short-Term Memory (LSTM) model was proposed to effectively predict the situation of air pollutants. The model could automatically extract information of the effective time step from the historical data set and combine forest fire disturbance and climate data as auxiliary data to improve the model prediction ability. Moreover, we created artificial neural network (ANN) and permissive regression (support vector machine, SVR) models for comparative experiments. The results showed that the precision accuracy of the developed LSTM model is higher. Unlike the other models, the LSTM neural network model could effectively predict the concentration of air pollutants in long-term series. Regarding long-term observation missions (7 days), the proposed model performed well and stably, with R2 reaching over 88%. [Display omitted] • This study proposes a method for predicting air pollutants over a long time series. • This study breaks the bottleneck of air pollutant prediction only for cities. • We added interference of forest fires to pollutant predictions to improve accuracy. • Compared multiple methods and demonstrated the accuracy of the created the model. [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]

Australia is a long-term key partner of China in coal trading, which is famous for high-quality coal and its price advantage. However, affected by the evolution of international political and economic situation, China has issued a coal ban on Australian coal since the end of 2020. The potential environmental consequences of this coal ban remain unclear. So, this paper quantifies the ban's impact on air pollution, human health and climate change by simulating the emission difference between the port scenario (simulating the emissions due to Australian coal import under business as usual) and the mine scenario (simulating the emissions due to domestic coal mining that converted from import share of Australian coal), and monetizes the losses into economic costs for direct comparison. The results illustrate the emission of SO 2 , VOCs and GHG in mine scenario grows up to 102, 12 and 6 times the amount of that in port scenario, mainly deriving from coal mining process in the North China Plain and Yunnan-Guizhou Plateau. It shows that the potential alternative of replacing import with mining leads to PM 2.5 pollution exacerbation and O 3 pollution abatement, which causes 148 (95 % CI: 101–183) non-communicable diseases and respiratory disease-related deaths in total. As for GHG, the emission of BC in transportation process and fugitive methane in coal mining process contribute to an impressive effect on warming potential with few quantities, which is worth noting in control process. In total, the environmental deterioration resulted in an economic loss of approximately 934 million yuan. The undiminished demand for high-quality industrial coal as well as growing carbon tax could make the economic value of the coal ban's environmental consequences more considerable in the near future. This research emphasizes the contradiction between economic development and environmental improvement caused by excessive dependence on coal and addresses a critical knowledge gap in assessing the ban's environmental impact by economic value. The result directly demonstrates the environmental consequences of the potential mining-replacing-import alternative and helps better understand environmental influences on the Chinese government's decision. [ABSTRACT FROM AUTHOR]

This paper presents an improved Teaching–Learning-Based Optimization Algorithm for controlling a UAV swarm to achieve real-time monitoring of indoor air quality and possible pollution source location search. The improvements to the Teaching–Learning-Based Optimization Algorithm focus on the following four points: proposing a variable teaching factor to improve search speed and accuracy, establishing an individual capability value model to integrate real-world UAV status with the algorithm, increasing the number of teachers and providing updated formulas to improve the algorithm's convergence speed, and proposing a grouping strategy to achieve real-time monitoring of indoor air quality and pollution source search within a unified algorithm framework. At the end of the article, possible legal challenges for deploying this algorithm are also discussed. • Improved TLBO and its use for UAV control in air composition testing. • Further consideration of the UAV's internal state to improve multi-UAV algorithm. • Obtained an algorithm for localizing gas sources in indoor environments. [ABSTRACT FROM AUTHOR]

Soot particles emitted from aero-engines are harmful to airport local air quality, and the adverse impact from soot particles is closely related to particle morphology, microstructure and chemical properties. To understand those properties of aviation soot particles, combustion experiments using conventional aviation fuel RP-3 were conducted on a Rich-Burn/Quick-Quench/Lean-Burn (RQL) aero-engine combustor. The soot emissions from the RQL combustor were analyzed via a high resolution transmission electron microscope (HRTEM), and the size distributions of primary particles at different power levels were determined based on the HRTEM images. Since the primary particle size is strongly related to the power level and follows an S-shaped curve, a phenomenological model with low-computational complexity and general applicability was established. Furthermore, the model was validated by using previously published experimental data of a CFM56–2C1 aero-engine burning four different fuels, including sustainable aviation fuels (SAF), demonstrating the influence of fuel properties on primary particle sizes at different power levels. Novelty and Significance Statement: In this paper, combustion experiments were conducted on an RQL combustor burning RP-3 fuel. Based on the HRTEM observation, the log-normal size distributions of primary particles at different power levels were determined. Furthermore, based on the fact that the relationship between primary particle size and power level followed an S-shaped curve, a phenomenological model linking primary particle size and combustor power was proposed and validated. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to review findings on the integration of solar thermal collectors, photovoltaic thermal collectors, and heat pumps to provide both electrical and thermal energy. The energy consumption of HP systems in buildings is significant. Studies have verified the benefits of using PVT collectors in heat pumps to provide electricity and solar assistance to their cold sources. This article provides an overview of advances and progress in solar assisted heat pumps (HPs). Increasingly, heat pumps are integrated with thermal and photovoltaic solar collectors. Providing both heat and hot water as well as electricity, this integration can be applied to a variety of applications. The services range from providing power and heating to industrial and agricultural needs. The aim of this review is to examine research conducted over the past twenty years with an emphasis on studies that have significant implications for the evolution of the technology in the near future. In demanding environmental conditions, the coupling of Photovoltaic/Thermal collectors (PVT) with HP systems has demonstrated a noticeable improvement in system performance and operational reliability. By addressing critical challenges and unresolved issues, the subsequent discussions and recommendations serve to channel attention. This will lead to enhanced performance improvements in HP systems by directing efforts in this way. [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]

With the rapid development of economy and the frequent occurrence of air pollution incidents, the problem of air pollution has become a hot issue of concern to the whole people. The air quality big data is generally characterized by multi-source heterogeneity, dynamic mutability, and spatial–temporal correlation, which usually uses big data technology for air quality analysis after data fusion. In recent years, various models and algorithms using big data techniques have been proposed. To summarize these methodologies of air quality study, in this paper, we first classify air quality monitoring by big data techniques into three categories, consisting of the spatial model, temporal model and spatial–temporal model. Second, we summarize the typical methods by big data techniques that are needed in air quality forecasting into three folds, which are statistical forecasting model, deep neural network model, and hybrid model, presenting representative scenarios in some folds. Third, we analyze and compare some representative air pollution traceability methods in detail, classifying them into two categories: traditional model combined with big data techniques and data-driven model. Finally, we provide an outlook on the future of air quality analysis with some promising and challenging ideas. • The air quality early warning system are systematically reviewed. • Some air quality analysis methods by big data techniques are overviewed. • The difficulties and some ideas of air quality research are discussed briefly. [ABSTRACT FROM AUTHOR]

• Policy specific beliefs underpin the attitudes held towards LEZs. • Emotional reactions towards LEZs are important in public acceptability. • Trust in government supports the beliefs held about LEZs. In recent years, the topic of air quality has grown in prominence due to an improved understanding of the detrimental impacts of local air pollutants on human health and wellbeing. The introduction of Urban Vehicle Access Regulations such as Low Emission Zones represents one policy that is being actively considered in city governance to address this problem, whereby the access of highly polluting vehicles is restricted to reduce traffic-related emissions. For such a policy to be implemented, an understanding of public support can prove useful by identifying the issues that underpin citizen reaction. This paper presents an assessment of public acceptability to Low Emission Zones through the application of a conceptual framework. This framework integrates an array of socio-psychological constructs sourced from theoretical models of behaviour and empirical findings on acceptability to Transport Demand Management measures. The framework is applied through a Structural Equation Model with the results of the analysis indicating that attitudes, policy specific beliefs, trust in government, and problem awareness all represent significant constructs in terms of their direct and in-direct effects on acceptability. This information can contribute to the discussion within local governments by providing guidance in their policy development on what contentious issues need to be addressed in public engagement strategies. [ABSTRACT FROM AUTHOR]

Accurate PM 2.5 forecasting provides a possibility for establishing an early warning system to notify the public and take precautionary measures to prevent negative effects on ambient air quality and public health. Considering strong seasonal variation in meteorological conditions, in this paper, a seasonal stacked autoencoder model combining seasonal analysis and deep feature learning is proposed for forecasting the hourly PM 2.5 concentration, named DL-SSAE model. The original data are firstly decomposed into four seasonal subseries according to the Chinese calendar, and then the Kendall correlation coefficient method is employed to search inherent relationships between PM 2.5 concentrations and meteorological parameters within 1-h ahead for each seasonal time series. The inherent relationships of each seasonal subseries are finally extracted, learned, and modeled by different deep neural networks (stacked autoencoders for regression), and the hourly PM 2.5 forecasts are yielded. The addressed model is tested by the dataset collected from three environmental monitoring stations in Beijing, China. The results demonstrate that the proposed model outperforms all other considered models with/without seasonality consideration in this paper. [ABSTRACT FROM AUTHOR]

Abstract Those opposed to hosting mega events often claim that it may lead to negative environmental outcomes. This paper investigates the environmental impact of hosting the G20 summit in Hangzhou and asks two questions: (1) has hosting the G20 summit led to urban air quality improvement and (2) if so, is this improvement a short-term or long-term phenomenon? This research uses an event study method and finds that the air quality in Hangzhou was significantly improved during the G20 period and, most importantly, this positive impact seems to be a long-term phenomenon, contrary to previous studies. A placebo analysis on a similar city does not find comparable patterns, offering empirical support for a causal explanation. This paper further analyzes the steps that Hangzhou took in preparation for the G20 summit and finds many long-term oriented measures such as technological upgrades and transition from fossil fuels to clean energies. These findings show that mega events could be used as a catalyst to overcome industrial and individual resistance in improving environmental quality. Highlights • The air quality in Hangzhou was significantly improved during the G20 period. • The improvement seems to persist and the reasons are explained. • Policy makers may utilize mega events for long-term environmental benefits. [ABSTRACT FROM AUTHOR]