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

The Upper Hunter Valley is a major coal mining area in New South Wales (NSW), Australia. Due to the ongoing increase in mining activities, PM10 (air-borne particles with an aerodynamic diameter less than 10 micrometres) pollution has become a major air quality concern in local communities. The present study was initiated to quantitatively examine the spatial and temporal variability of PM10 pollution in the region. An earlier paper of this study identified two air quality subregions in the valley. This paper aims to provide a holistic summarisation of the relationships between elevated PM10 pollution in two subregions and the local- and synoptic-scale meteorological conditions for spring and summer, when PM10 pollution is relatively high. A catalogue of twelve synoptic types and a set of six local meteorological patterns were quantitatively derived and linked to each other using the self-organising map (SOM) technique. The complex meteorology–air pollution relationships were visualised and interpreted on the SOM planes for two representative locations. It was found that the influence of local meteorological patterns differed significantly for mean PM10 levels vs. the occurrence of elevated pollution events and between air quality subregions. In contrast, synoptic types showed generally similar relationships with mean vs. elevated PM10 pollution in the valley. Two local meteorological patterns, the hot–dry–northwesterly wind conditions and the hot–dry–calm conditions, were found to be the most PM10 pollution conducive in the valley when combined with a set of synoptic counterparts. These synoptic types are featured with the influence of an eastward migrating continental high-pressure system and westerly troughs, or a ridge extending northwest towards coastal northern NSW or southern Queensland from the Tasman Sea. The method and results can be used in air quality research for other locations of NSW, or similar regions elsewhere. [ABSTRACT FROM AUTHOR]

Ozone at ground level (O3) is an air pollutant that is formed from primary precursor gases like nitrogen oxides (NOx) and volatile organic compounds (VOCs). It plays a significant role as a precursor to highly reactive hydroxyl (OH) radicals, which ultimately influence the lifespan of various gases in the atmosphere. The elevated surface O3 levels resulting from anthropogenic activities have detrimental effects on both human health and agricultural productivity. This paper provides a comprehensive analysis of the variations in surface O3 levels across various regions in the Indian subcontinent, focusing on both spatial and temporal changes. The study is based on an in-depth review of literature spanning the last thirty years in India. Based on the findings of the latest study, the spatial distribution of surface O3 indicates a rise of approximately 50–70 ppbv during the summer and pre-monsoon periods in the northern region and Indo-Gangetic Plain. Moreover, elevated levels of surface O3 (40–70 ppbv) are observed during the pre-monsoon/summer season in the western, southern, and peninsular Indian regions. The investigation also underscores the ground-based observations of diurnal and seasonal alterations in surface O3 levels at two separate sites (rural and urban) in Kannur district, located in southern India, over a duration of nine years starting from January 2016. The O3 concentration exhibits an increasing trend of 7.91% (rural site) and 5.41% (urban site), ascribed to the rise in vehicular and industrial operations. This review also presents a succinct summary of O3 fluctuations during solar eclipses and nocturnal firework displays in the subcontinent. [ABSTRACT FROM AUTHOR]

Fine particles (PM2.5) have generally been reported as the major contributor to the adverse health effects of air pollution. Lebanon is characterized by a high density of transport, the production of electricity by generators, and a problem of uncontrolled incineration of household waste. For the purpose of this paper, the physico-chemical properties of fine (PM2.5-0.3) and quasi-ultrafine (PM0.3) particulate matter sampled in Southern Lebanon, were studied. Then, an evaluation and comparison of the toxicity of the different extracted fractions from PM (i.e., native PM2.5-0.3 vs. organic extractable matter fraction (OEM2.5-0.3), and non-extractable matter fraction (NEM2.5-0.3)) was performed. Also, an examination of the toxicity of PM0.3 was conducted indirectly through the evaluation of the OEM0.3 harmfulness. The physico-chemical analysis showed that PM0.3 was much more concentrated than PM2.5-0.3 in organic compounds such as polycyclic aromatic hydrocarbons (PAHs) (28-fold) and their nitrated (N-PAHs, 14-fold) and oxygenated (O-PAHs, 10-fold) derivatives. Normal human bronchial epithelial cells (BEAS-2B) were exposed to PM2.5-0.3, its derived fractions (i.e., OEM2.5-0.3 and NEM2.5-0.3), and OEM0.3 before evaluating the global cytotoxicity, metabolic activation of organic compounds, genotoxicity, and inflammatory response. Different responses were observed depending on the considered fraction of particles. The global cytotoxicity showed a pronounced response related to ATP and LDH activities after exposure to the quasi-ultrafine organic extractable matter fraction (OEM0.3). There was no significant induction of the AhR cell-signaling pathway by NEM2.5-0.3. Despite the apparent difference in the kinetics of induction of the toxicological endpoints under study, OEM0.3 provoked a higher overall cytotoxicity and genotoxicity than OEM2.5-0.3 and total PM2.5-0.3. Taken together, these results clearly showed that the finest particles are more damaging to BEAS-2B cells than PM2.5-0.3 because they are richer in organic compounds, thereby inducing more remarkable toxic effects. [ABSTRACT FROM AUTHOR]

In this paper, we present a city-scale (possibly global) air pollution network made of low-cost sensors for particulate matter concentration in the air. The components of the presented system are based on our research and experience from previous studies focused on air quality instruments and sensors for addressing urban air pollution. Sensor nodes are produced locally and distributed over Sarajevo, Bosnia and Herzegovina. The system collects, calibrates, and displays particulate matter concentrations in real-time. In comparison to the available measurements from governmental institutions, our system demonstrated good agreement of measured parameters, with several advantages including, higher resolution in space and time, lower costs, both horizontal and vertical measurements. One of the capabilities of the system is the real-time air pollution city map with animations. By installing multiple sensor nodes over a slope, we receive vertical measurements of temperature, humidity, and particulate matter concentration in real time, which gives a valuable insight into the dynamics of temperature inversion episodes and air pollution below the inversion layer. [ABSTRACT FROM AUTHOR]

The port of Cartagena, south-east Spain, is noted for its intense activity. This paper presents the influence of the COVID-19 pandemic on the recorded levels of six pollutants. Fifteen years of measurements were used, with two objectives. The first was to investigate how these pollutants evolved, together with their usual cycles. The second objective was to ascertain whether the COVID-19 pandemic had an impact on the concentrations recorded. The results showed that nitrogen oxide concentrations remained steady in the first half of the measurement period and decreased in the second half. SO2 concentrations decreased irregularly, whereas O3 and PM10 presented steady concentrations. The annual cycle was observed for nitrogen oxides and O3. Only SO2 evidenced no weekly cycle. Finally, the analysis of pre- and post-pandemic concentrations revealed a sharp decrease in nitrogen oxides, whereas the pandemic was not seen to have had any impact on the other pollutants. In addition, NO2 emerged as the best indicator of human activity—represented by car and maritime traffic—due to its response to the relaxation measures. Finally, Lamb weather types were calculated. The unclassified type was the most frequent. However, the greatest concentration changes were observed for anticyclonic and eastern flow types. [ABSTRACT FROM AUTHOR]

Mobile monitoring is used as an additional tool to collect air quality data at a high spatial resolution and to complement data from fixed air quality stations. Citizens are interested in contributing to air quality monitoring, and while the availability of low-cost air quality sensors can create opportunities to measure the air quality at a high spatial resolution, the data are often of lower quality, and sensors that measure combustion-related aerosols (like black carbon) are not commonly available. Mobile monitoring using a mid-range instrument can fill this gap. We present the results of a mobile BC (black carbon) monitoring campaign performed by citizens in Mechelen as part of a local citizen observatory (CO), Meet Mee Mechelen, initiated as part of the European H2020 project, Ground Truth 2.0. The goal of the study was two-fold: (1) to propose and evaluate a mobile monitoring method (data collection and data processing) to construct pollution maps of BC concentrations and (2) to demonstrate how to organize community-based air quality monitoring to measure both the spatial and temporal variations in air pollution levels. Measurements were taken during peak hours in four campaigns characterized by different meteorological conditions: October–November 2017, February–March 2018, June–July 2018 and September 2018. The results show large spatial and temporal variabilities. Spatial variability is influenced by traffic volume, stop-and-go traffic and also the building environment and the distance of biking paths from road traffic. The four different campaigns show similar spatial patterns, but due to background and meteorological influences, the absolute concentrations differ between seasons. A rescaling method using data from fixed stations in the air quality monitoring network (AQMN) was presented to construct maps representative of longer periods. This paper shows that mobile measurements can be used by CO to assess the spatial variability of air quality in a city. The data can be used to evaluate mobility plans, carry out hot spot detection, evaluate the exposure of cyclists as a function of cycling infrastructure and perform model validation. However, it is important to use high-quality instruments and apply the correct measurement methodology (number of repetitions, season) to obtain meaningful data. [ABSTRACT FROM AUTHOR]

Non-exhaust road transport emissions in cities contribute to poor air quality and have an impact on human health. This paper presents a new study of particles emitted by tyre wear in real driving conditions and gives their emission factors. The most frequently emitted particles were collected in urban, suburban and road areas. They were identified and analysed physically and chemically. Their level of toxicity is well known. An overall analysis of the measured pollutants was carried out to assess their emission factors in real driving situations. The highest emitting pollutants, considered separately, were found to have high emission factors. The values obtained exceed the Euro standard for vehicles but are below those of vehicles not equipped with particle filters. Significant test analysis confirmed that the inertia of chemical pollutants is homogeneous. Emission factors have also been provided for PM10 and PM2.5. These results should contribute to the emergence of future regulations of non-exhaust emissions and should help to analyse the exposure-impact relationship for particles from tyre wear. [ABSTRACT FROM AUTHOR]

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

With the rapid development of urbanization, ambient air pollution is becoming increasingly serious. Out of many pollutants, fine particulate matter (PM2.5) is the pollutant that affects the urban atmospheric environment to the greatest extent. Fine particulate matter (PM2.5) concentration prediction is of great significance to human health and environmental protection. This paper proposes a CNN-SSA-DBiLSTM-attention deep learning fusion model. This paper took the meteorological observation data and pollutant data from eight stations in Bijie from 1 January 2015 to 31 December 2022 as the sample data for training and testing. For the obtained data, the missing values and the data obtained from the correlation analysis performed were first processed. Secondly, a convolutional neural network (CNN) was used for the feature selection. DBILSTM was then used to establish a network model for the relationship between the input and actual output sequences, and an attention mechanism was added to enhance the impact of the relevant information. The number of units in the DBILSTM and the epoch of the whole network were optimized using the sparrow search algorithm (SSA), and the predicted value was the output after optimization. This paper predicts the concentration of PM2.5 in different time spans and seasons, and makes a comparison with the CNN-DBILSTM, BILSTM, and LSTM models. The results show that the CNN-SSA-DBiLSTM-attention model had the best prediction effect, and its accuracy improved with the increasing prediction time span. The coefficient of determination (R2) is stable at about 0.95. The results revealed that the proposed CNN-SSA-DBiLSTM-attention ensemble framework is a reliable and accurate method, and verifies the research results of this paper in regard to the prediction of PM2.5 concentration. This research has important implications for human health and environmental protection. The proposed method could inspire researchers to develop even more effective methods for atmospheric environment pollution modeling. [ABSTRACT FROM AUTHOR]

In recent years, air pollution in Xinjiang, the core region of the Silk Road Economic Belt, has become increasingly severe, posing a more significant threat to human health. This paper selects the tropospheric ozone, nitrogen dioxide, and formaldehyde datasets under OMI remote-sensing monitoring and the PM2.5 dataset in China High Air Pollutants (CHAP) for 2018–2021. The spatial and temporal distribution of multi-pollutants, the spatial autocorrelation of Moran's I index pollutants, and the correlation between pollutants in the warm period were studied in southern Xinjiang. Meanwhile, the geographical and temporal weighted regression (GTWR) model was used for influencing factor analysis, and the BenMap-CE model was used for health benefit analysis. The results showed that the spatial distribution of ozone concentration values in southern Xinjiang shows a decreasing distribution pattern from the east–central region to the western and southern regions. The spatial distribution of formaldehyde concentration values is opposite to that of ozone. There is a clear high-value area in the ozone concentration value in April–September. The NO2 column concentration values were in the range of 0.55~1.09 × 1015 molec/cm2 in most parts of southern Xinjiang. The area of high concentration values is located in the northeast of the study area; PM2.5 concentration values are higher in the middle area of southern Xinjiang. The spatial autocorrelation characteristics showed that the spatial aggregation of O3 and NO2 displayed a slow increasing trend year by year. The spatial aggregation of HCHO and PM2.5 fluctuated slightly in four years. The overall trend of HCHO is slowly decreasing, while PM2.5 is fluctuating and increasing. In the GTWR model analysis, overall, the atmospheric pressure has a strong influence on all pollutants. The effect of NO2 on O3 was higher than that of HCHO among the four pollutants. The correlation between O3 and PM2.5 was as high as −0.7872. The BenMap-CE health benefits assessment concluded that the number of premature deaths caused by ozone pollution was much higher than that of premature deaths caused by PM2.5. The highest number of premature deaths for both pollutants occurred in Kashgar. [ABSTRACT FROM AUTHOR]

In today's urban environments, accurately measuring and forecasting air pollution is crucial for combating the effects of pollution. Machine learning (ML) is now a go-to method for making detailed predictions about air pollution levels in cities. In this study, we dive into how air pollution in urban settings is measured and predicted. Using the PRISMA methodology, we chose relevant studies from well-known databases such as PubMed, Springer, IEEE, MDPI, and Elsevier. We then looked closely at these papers to see how they use ML algorithms, models, and statistical approaches to measure and predict common urban air pollutants. After a detailed review, we narrowed our selection to 30 papers that fit our research goals best. We share our findings through a thorough comparison of these papers, shedding light on the most frequently predicted air pollutants, the ML models chosen for these predictions, and which ones work best for determining city air quality. We also take a look at Skopje, North Macedonia's capital, as an example of a city still working on its air pollution measuring and prediction systems. In conclusion, there are solid methods out there for air pollution measurement and prediction. Technological hurdles are no longer a major obstacle, meaning decision-makers have ready-to-use solutions to help tackle the issue of air pollution. [ABSTRACT FROM AUTHOR]

Carbonaceous aerosol is the most significant contributor to the particulate matter in the atmosphere. It is composed of a complex mixture of compounds containing carbon atoms and is usually classified into two main fractions: black carbon (BC) and organic carbon (OC). BC is essentially a primary pollutant emitted in particulate form, and its chemical stability excludes chemical transformations during its lifetime in the atmosphere. Therefore, it should be considered a tracer for the long-range transport of anthropogenic air pollution. OC has both primary and secondary origins: primary OC particulate is formed during combustion and emitted mainly as submicron particles, whereas secondary OC particulate originates from gas-to-particle conversion processes. This SI aims to deepen the state of the art on this important topic ranging from theory to the development of new instrumentation useful for this determination, to the chemical–physical problems in the atmosphere, to the health and toxicological implications related to exposure to these pollutants as well as papers showing historical data series both in urban, rural, and remote areas are also appreciated. [ABSTRACT FROM AUTHOR]

Air pollution is a global problem, which needs to be understood and controlled to ensure a healthy environment and inform sustainable development. Urban areas have been established as one of the main contributors to air pollution, and, as such, urban air quality is the subject of an increasing volume of research. One of the principal means of studying air pollution dispersion is to use computational fluid dynamics (CFD) models. Subject to careful verification and validation, these models allow for analysts to predict air flow and pollution concentration for various urban morphologies under different environmental conditions. This article presents a detailed review of the use of CFD to model air pollution dispersion in an urban environment over the last decade. The review extracts and summarises information from nearly 90 pieces of published research, categorising it according to over 190 modelling features, which are thematically systemised into 7 groups. The findings from across the field are critically compared to available urban air pollution modelling guidelines and standards. Among the various quantitative trends and statistics from the review, two key findings stand out. The first is that, despite the existence of best practice guidelines for pollution dispersion modelling, anywhere between 12% and 34% of the papers do not specify one or more aspects of the utilised models, which are required to reproduce the study. The second is that none of the articles perform verification and validation according to accepted standards. The results of this review can, therefore, be used by practitioners in the field of pollution dispersion modelling to understand the general trends in current research and to identify open problems to be addressed in the future. [ABSTRACT FROM AUTHOR]

Urban air pollutants are a major public health concern and include biological matters which composes about 25% of the atmospheric aerosol particles. Airborne microorganisms were traditionally characterized by culture-based methods recognizing just 1.5–15.3% of the total bacterial diversity that was evaluable by genome signature in the air environment (aerobiome). Despite the large number of exposed people, urban aerobiomes are still weakly described even if recently advanced literature has been published. This paper aims to systematically review the state of knowledge on the urban aerobiome and human health effects. A total of 24 papers that used next generation sequencing (NGS) techniques for characterization and comprised a seasonal analysis have been included. A core of Proteobacteria, Actinobacteria, Firmicutes, and Bacteroides and various factors that influenced the community structure were detected. Heterogenic methods and results were reported, for both sampling and aerobiome diversity analysis, highlighting the necessity of in-depth and homogenized assessment thus reducing the risk of bias. The aerobiome can include threats for human health, such as pathogens and resistome spreading; however, its diversity seems to be protective for human health and reduced by high levels of air pollution. Evidence of the urban aerobiome effects on human health need to be filled up quickly for urban public health purposes. [ABSTRACT FROM AUTHOR]

A natural experiment conducted on the shrinking Salton Sea, a saline lake in California, showed that each one foot drop in lake elevation resulted in a 2.6% average increase in P M 2.5 concentrations. The shrinking has caused the asthma rate continues to increase among children, with one in five children being sent to the emergency department, which is related to asthma. In this paper, several data-driven machine learning (ML) models are developed for forecasting air quality and dust emission to study, evaluate and predict the impacts on human health due to the shrinkage of the sea, such as the Salton Sea. The paper presents an improved long short-term memory (LSTM) model to predict the hourly air quality ( O 3 and CO ) based on air pollutants and weather data in the previous 5 h. According to our experiment results, the model generates a very good R2 score of 0.924 and 0.835 for O 3 and CO , respectively. In addition, the paper proposes an ensemble model based on random forest (RF) and gradient boosting (GBoost) algorithms for forecasting hourly P M 2.5 and P M 10 using the air quality and weather data in the previous 5 h. Furthermore, the paper shares our research results for P M 2.5 and P M 10 prediction based on the proposed ensemble ML models using satellite remote sensing data. Daily P M 2.5 and P M 10 concentration maps in 2018 are created to display the regional air pollution density and severity. Finally, the paper reports Artificial Intelligence (AI) based research findings of measuring air pollution impact on asthma prevalence rate of local residents in the Salton Sea region. A stacked ensemble model based on support vector regression (SVR), elastic net regression (ENR), RF and GBoost is developed for asthma prediction with a good R2 score of 0.978. [ABSTRACT FROM AUTHOR]

It is of great practical significance to analyze the hot issues, research frontiers, and trends concerning the relationship between air pollution and public health and to adopt reasonable strategies to control air pollution and prevent health hazards for follow-up research in this field. Unlike traditional literature reviews, this paper adopts a visual, flexible, and scientifically systematic approach to the analysis, which makes these analysis results more intuitive and comprehensive. Based on the core collection of the Web of Science and CNKI databases, this paper uses CiteSpace software to draw and comment on the maps of Chinese and English keywords, publishing time, author, country, and research institutions in this field. The results show the following: (1) The number of studies on the relationship between air pollution and health has increased year by year; researchers have formed sub cooperation networks, and the trend of cooperation and exchange has become more and more obvious in recent years; the impact of air pollution on health is a hot topic in the world. (2) Research hot topics mainly focus on the selection of air pollutants, health economic consequences of air pollution and the global burden of disease it causes, health indicators, research samples, which are gradually being refined, the synergistic governance of air pollution, and climate change. (3) The analysis of research frontiers and trends reveals that, first, the study of air pollutants in the English literature has undergone a refinement from nitrogen dioxide to fine particulate matter, and the sources of air pollutants in the Chinese literature have undergone changes in the petrochemical industry, indoor formaldehyde pollution, and haze. Second, atmospheric pollution has a significant negative impact on health, increasing the incidence of respiratory and cardiovascular diseases, and even causing death. Third, sustained exposure to pollution then causes greater damage to health and will be a key direction for future research. Fourth, the literature not only studies the correlation but also emphasizes the causal inference between air pollution and health and measures the economic costs associated with health. Finally, air pollution and climate change need to be governed synergistically. The article points out that the three areas of sustained pollution exposure, indirect consequences of negative health effects of air pollution, and air pollution and climate change may be the future focus of the field. [ABSTRACT FROM AUTHOR]

In recent years, the issue of air pollution has garnered significant public attention globally, with a particular emphasis on the challenge of atmospheric fine particulate matter (PM2.5) pollution. The efficient and precise simulation of changes in pollutant concentrations, as well as their spatial and temporal distribution, is essential for effectively addressing the air pollution issue. In this paper, the WRF-Chem model is used to simulate the meteorological elements including temperature (T), relative humidity (RH), wind speed (WS), and pressure (P), and the concentrations of PM2.5 and PM10 atmospheric pollutants in December 2020 in Xuzhou City. Simultaneously, the ADMS-Urban model was employed to conduct a higher spatial resolution study of PM2.5 concentrations during the heavy pollution days of 11–12 December 2020 in Xuzhou City. The study shows that the WRF-Chem model can simulate the meteorological conditions of the study time period better, and the correlation coefficients (R) of pressure, temperature, wind speed, and relative humidity are 0.99, 0.87, 0.75, and 0.70, respectively. The WRF-Chem model can accurately simulate the PM2.5 concentration on clean days (R of 0.66), but the simulation of polluted days is not satisfactory. Therefore, the ADMS-Urban model was chosen to simulate the PM2.5 concentration on polluted days in the center of Xuzhou City. The ADMS-Urban model can simulate the distribution characteristics and concentration changes of PM2.5 around roads and buildings in the center of Xuzhou City. Comparing the simulation results of the two models, it was found that the two models have their own advantages in PM2.5 concentration simulation, and how to better couple the two models is the next research direction. [ABSTRACT FROM AUTHOR]

Particulate Matter (PM) air pollution has been linked to major adverse health effects. Road transport still contributes significantly to ambient PM concentrations, but mainly due to the non-exhaust emissions from vehicles. For the first time worldwide, limits for non-exhaust emissions have been proposed by the European Union for the upcoming Euro 7 step. For these reasons, interest in brake emissions has increased in the past few years. Realistic emission factors are necessary to accurately calculate the contribution of brake emissions to air pollution but also to estimate the emissions reduction potential of new or existing technologies and improved brake formulations. This paper reviews emission factors from light-duty vehicles reported in the literature, with a focus on those that followed the recently introduced Global Technical Regulation (GTR 24) methodology on brakes in light-duty vehicles. Reduction efficiencies of non-asbestos organic (NAO) pads, brake dust filters, ceramic discs, coated discs, and regenerative braking are also discussed. Finally, the emission factors are compared with roadside measurements of brake emissions and emission inventories worldwide. The findings of this study can be used as an input in emission inventories to estimate the contribution of brakes to air pollution. [ABSTRACT FROM AUTHOR]

Background: Air pollution is an important factor affecting residents' health. Methods: Based on the health data of 9959 residents from 28 provinces in China in the 2017 Chinese General Social Survey (CGSS), this paper uses a hierarchical linear model (HLM) to scientifically analyze the impact of air pollution on residents' health and to explore the moderating role of air pollution in various factors influencing residents' health. Results: Air pollution has significant negative effects on residents' health (coef. −0.103, p < 0.01), while frequency of exercise (coef. 0.070, p < 0.01), education level (coef. 0.012, p < 0.1) and income level (coef. 0.383, p < 0.01) have positive effects on residents' health. Air pollution has a negative moderating effect on the positive impact of exercise frequency on residents' health (coef. −0.015, p = 0.004). Conclusions: Air pollution causes serious damage to residents' health and it is necessary to control air pollution. Increasing residents' income, education level and exercise frequency can help improve residents' health level. Air pollution significantly reduce the positive impact of exercise frequency on residents' health. In order to effectively improve their own health, it is a better choice for residents to exercise when the air quality is good. [ABSTRACT FROM AUTHOR]

This paper investigates the effect of the architectural design of deep learning models in combination with a feature engineering approach considering the temporal variation in the features in the case of tropospheric ozone forecasting. Although deep neural network models have shown successful results by extracting features automatically from raw data, their performance in the domain of air quality forecasting is influenced by different feature analysis approaches and model architectures. This paper proposes a simple but effective analysis of tropospheric ozone time series data that can reveal temporal phases of the ozone evolution process and assist neural network models to reflect these temporal variations. We demonstrate that addressing the ozone evolution phases when developing the model architecture improves the performance of deep neural network models. As a result, we evaluated our approach on the CNN model and showed that not only does it improve the performance of the CNN model, but also that the CNN model in combination with our approach boosts the performance of the other deep neural network models such as LSTM. The development of the CNN, LSTM-CNN, and CNN-LSTM models using the proposed approach improved the prediction performance of the models by 3.58%, 1.68%, and 3.37%, respectively. [ABSTRACT FROM AUTHOR]

In China, blue sky defense is a crucial part of ecological environment governance. Objective environmental governance performance needs to be perceived by the public to more truly affect the public's evaluation of the government's environmental governance. This paper focuses on the public's subjective perception of air pollution and evaluation of the local government's environmental governance. Based on the Chinese General Social Survey data, the matched economic indicators, and air pollution data, we conduct a diachronic study on the public's evaluation of local governments' environmental protection work, and we analyze the relationship between the subjective perception of air pollution, the objective air pollution data, and the evaluation of local governments' environmental protection work. The results showed the following: (1) People's evaluation of local governments' environmental protection work significantly improved from 2013 to 2021. The objective indicator improved, while the subjective perception declined. (2) The subjective perception of air pollution has a significant negative impact on the evaluation of local governments' environmental protection work and needs to be better considered to improve air quality. At the same time, the effect of the objective indicator is insignificant. [ABSTRACT FROM AUTHOR]

Air pollution is one of the most important topics as it can cause various reactions of the organisms, such as mental health disorders, respiratory problems or various cardiovascular despises. Many of the side effects of pollution are caused by particulate matter (PM). Therefore air pollution, especially the concentration of PM is monitored in many European countries. In the past years, Skopje has been one of the top-ranked cities in the world concerning the concentration of PM. This paper investigates the effect of the pandemic with COVID-19 and the restriction measures on air quality. The data collected before the pandemic (May 2018), during the global pandemic (May 2020 and May 2021), and after the period with restriction measures (May 2022) are analyzed. The measurement parameters are collected at the technical campus of the Ss Cyril and Methodius University in Skopje, North Macedonia, in May 2018, May 2020, May 2021, and May 2022. In this research, it can be confirmed that the restriction measures had a significant positive impact on air pollution. [ABSTRACT FROM AUTHOR]

Air-quality monitoring and forecasting are crucial for atmosphere pollution control and management. We propose an innovative data-driven framework for air quality index (AQI) prediction by integrating GNSS radio occultation (GNSS-RO) observation and weather modeling. Empowered by the state-of-the-art machine learning approach, our method can effectively predict regional AQI with a comparable accuracy much more quickly than the traditional numerical modeling and simulation approach. In a real case study using a representative region of China, our data-driven approach achieves a 2000 times speedup; meanwhile, the prediction error measured by rRMSE is only 2.4%. We investigate further the effects of different models, hyperparameters, and meteorological factors on the performance of our AQI prediction framework, and reveal that wind field and atmospheric boundary-layer height are important influencing factors of AQI. This paper showcases a direct application of GNSS-RO observation in assisting in forecasting regional AQI. From a machine learning point of view, it provides a new way to leverage the unique merits of GNSS atmospheric remote sensing technology with the help of the more traditional weather forecasting modeling approach. [ABSTRACT FROM AUTHOR]

During the "Parade Blue" period in 2015, Beijing and its surrounding areas implemented mandatory temporary source control strategies, which provided experimental conditions for studying the effects of source emission control measures on the sources of atmospheric PM2.5. Analyzing the source contribution rate of the main particulate matters via the source analysis method of the receptor model is an important method for studying the atmospheric pollution. Previous studies on the "Parade Blue" period only discussed the change in PM2.5 concentration during the source control period and the source non-control period, but did not select appropriate emission sources and acceptor components to analyze the contribution ratio of each emission source to fine particulate matter pollution. In this paper, based on the receptor component spectrum and emission source component spectrum, the chemical mass balance receptor model was used for source analysis. The results showed that outdoor PM2.5 concentration was 26.31 μg/m3 in the source control period, which was less than the 40.08 μg/m3 in the source non-control period. In the source control period, motor vehicle emissions, industrial combustion and urban fugitive dust contributed significantly to the fine particulate pollution, accounting for 76–81%, 8–9% and 11–16%, respectively. In the source non-control period, the contributions of motor vehicle emissions and industrial combustion accounted for 57–59% and 41–43%, respectively, significantly higher than that of urban fugitive dust, which accounted for less than 2%. The correlation between the contribution rate of emission sources and the source control strategy was studied and analyzed during the "Parade Blue" period, and the effectiveness of the source control strategy was proved. [ABSTRACT FROM AUTHOR]

Air pollution is an important factor affecting human health and daily life. The Chinese government is making vigorous efforts to control air pollution. The upgrading of the industrial structure is a problem-solving tool in the environment and economic growth cases. This paper aims to explore the relationships among environmental regulation, the upgrading of the industrial structure and air pollution. The PVAR (Panel Vector Auto Regression) model and moderating effect model are used to conduct empirical analysis based on panel data of 30 provinces in China from 2004 to 2020. The analysis of the results provides the following findings. Firstly, environmental regulations can significantly reduce emissions, but the deterioration of air quality does not have a significant impact on the improvement of environmental regulations. Secondly, industrial structure upgrading can reduce air pollution, but the worsening of the air quality will hinder the upgrading of industrial structures. Thirdly, environmental regulation can promote industrial structure upgrading. Lastly, industrial structure upgrading is a moderating variable and can positively moderate the impact of environmental regulations on air pollution. [ABSTRACT FROM AUTHOR]

During the second half of August 2022, a dust intrusion event occurred when dust that originated in the dry regions of the Kalmyk steppe (located in Russia, northeast of the Black Sea, north of Georgia, and northwest of the Caspian Sea) and the Precaspian plain was transported over the eastern region of Romania. The arid soil found in these areas can be attributed to an extended period of intense drought, with notable instances occurring in 2002, 2003, 2015, and 2018. This situation was further intensified by heatwaves experienced in May and June of 2022. The dust event was captured in MODIS images. In addition, smoke trains originating from fires in the north of the Azov were detected, but these did not reach Romania. Optical parameters from AERONET were used to confirm the dust event. To determine the trajectory of the particles, the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model was used in this paper. The ensemble median model was used to highlight the presence and concentration of dust in the eastern part of Romania. Aerosols were detected between 0 and 4 km, according to radar and ceilometer data from the REXDAN cloud remote sensing facility in Galați, Romania. This dust intrusion event was the result of the dominant easterly circulation caused by the extension of the East European High to the northeast of the continent, which transported the dust towards the eastern part of Romania for more than 2 days. Moreover, the torrential rains between 22 and 24 August did not clear the atmosphere of dust, since the intense easterly circulation kept carrying the dust into the Moldavian area. [ABSTRACT FROM AUTHOR]

Air pollutant forecasting can be used to quantitatively estimate pollutant reduction trends. Combining bibliometrics with the evolutionary tree and Markov chain methods can achieve a superior quantitative analysis of research hotspots and trends. In this work, we adopted a bibliometric method to review the research status of statistical prediction methods for air pollution, used evolutionary trees to analyze the development trend of such research, and applied the Markov chain to predict future research trends for major air pollutants. The results indicate that papers mainly focused on the effects of air pollution on human diseases, urban pollution exposure models, and land use regression (LUR) methods. Particulate matter (PM), nitrogen oxides (NOx), and ozone (O3) were the most investigated pollutants. Artificial neural network (ANN) methods were preferred in studies of PM and O3, while LUR were more widely used in studies of NOx. Additionally, multi-method hybrid techniques gradually became the most widely used approach between 2010 and 2018. In the future, the statistical prediction of air pollution is expected to be based on a mixed method to simultaneously predict multiple pollutants, and the interaction between pollutants will be the most challenging aspect of research on air pollution prediction. The research results summarized in this paper provide technical support for the accurate prediction of atmospheric pollution and the emergency management of regional air quality. [ABSTRACT FROM AUTHOR]

In the last few decades, urban planning has expanded regarding environmental considerations. However, air quality, which is regarded as an important aspect of the green development of cities, is not considered in urban planning. This research aims to propose a tool to easily introduce air quality considerations into urban projects. Nowadays, the usual air pollutants (NOx, PM, SO2, and O3) are measured via sophisticated monitoring stations—or even low-cost devices—to give near-real-time air quality indices. However, stations are not adapted to local air pollution and real-time data are not helpful for planning purposes. An index able to rank areas and projects based on urban "air proxy data" would help decision makers. This paper presents how to create an air quality index as a decision support tool for urban planning. No pollutant measurement campaign will be necessary and only data that are easily accessible, even to nonexperts, are used. This paper describes the methodological development of an index that we call AQOI (Air Quality Observed Index), and the results obtained for four different locations (industrial, urban, and rural) considered as preliminary tests. [ABSTRACT FROM AUTHOR]

Air pollution is a severe problem for the global environment. Most people spend 80% to 90% of the day indoors; therefore, indoor air pollution is as important as outdoor air pollution. The problem is more severe on school campuses. There are several ways to improve indoor air quality, such as air cleaners or ventilation. Air-quality sensors can be used to detect indoor air quality in real time to turn on air cleaner or ventilation. With an efficient and accurate clustering technique for indoor air-quality data, different ventilation strategies can be applied to achieve a better ventilation policy with accurate prediction results to improve indoor air quality. This study aims to cluster the indoor air quality data (i.e., CO2 level) collected from the school campus in Taiwan without other external information, such as geographical location or field usage. In this paper, we propose the Max Fast Fourier Transform (maxFFT) Clustering Approach to classify indoor air quality to improve the efficiency of the clustering and extract the required feature. The results show that without using geographical information or field usage, the clustering results can correctly reflect the ventilation condition of the space with low computation time. [ABSTRACT FROM AUTHOR]

Air pollution affects people's life and health, and controlling air pollution requires the collection of polluting gas information. Unmanned aerial vehicles (UAVs) have been used for environmental detection due to their characteristics. However, the limitation of onboard energy sources of UAVs will limit the coverage of the detection area and the number of gas samples collected, which will affect the assessment of pollution levels. In addition, to truly and completely reflect the distribution of atmospheric pollutants, it is necessary to sample the entire three-dimensional space. This paper proposes a three-dimensional space path planning method suitable for UAV atmospheric environment detection, which can generate a full-coverage path with optimal coverage density under energy constraints. Simulation results show that the proposed method can effectively improve the coverage density compared with other path generation methods. Field experiments show that the proposed method is reliable and accurate in the application of UAV atmospheric environment space detection. [ABSTRACT FROM AUTHOR]

High concentration levels of air pollutants may cause damage to plants, animals, and the health of some groups of human beings. Therefore, it is important to investigate different topics related to the high air pollution levels and to find reliable answers to the questions about the possible damages, which might take place when these levels exceed some limits. A few of the numerous questions, the answers of which are highly desirable, are listed below: (a) When are the air pollution levels dangerous? (b) What is the reason for the increased air pollution levels? (c) How can the air pollution levels be decreased? (d) Will the future climate changes result in higher and more dangerous air pollution levels? It is necessary to study carefully many issues connected with the distribution of air pollutants in a given region and with the reasons for the increases of the concentrations to high levels, which might be damaging. In order to do this, it is necessary to develop a Digital Twin of all relevant physical processes in the atmosphere and to use after that this tool in different applications. Such a tool, its name is DIGITAL AIR, has been created. Digital Twins are becoming more and more popular). Many complex problems, arising taking place in very complicated surroundings, can be handled and resolved successfully by applying Digital Twins. The preparation of such a digital tool as well as its practical implementation in the treatment of a special problem, the increase of some potentially dangerous ozone levels, will be discussed and tested in this paper. The Unified Danish Eulerian Model (UNI-DEM) is a very important part of DIGITAL AIR. This mathematical model, UNI-DEM, can be applied in many different studies related to damaging effects caused by high air pollution levels. We shall use it in this paper to get a reliable answer to a very special but extremely important question: will the future climatic changes lead to an increase in the ozone pollution levels in Bulgaria and Europe, which can potentially become dangerous for human health? [ABSTRACT FROM AUTHOR]

Most of these papers focused on the characterization and mitigation of air pollutants in the rural areas, and few of them considered the air pollutants from urban area. With rapid development of modern agriculture, a multitude and a large amount of air pollutants were generated by agricultural activities, which are becoming more and more serious issues with regards to air pollution [[1]]. (3) Residential combustion Compared with industrial sources, rural residential combustion is more harmful to air quality and human health under the same emission values of air pollutants. Strategies on the Air Emission Control for Rural Areas With the rapid development of modern agriculture, the impact of pollutants emitted by agricultural activities on the atmosphere is gradually non-negligible, and more and more relevant studies have been conducted to reduce the threat of agricultural air pollution [[1]]. [Extracted from the article]

Prediction of air pollutant concentrations is currently one of the most important methods for the prevention and control of urban air pollution in most countries, and accurate and timely prediction of pollutant concentrations is of great significance for urban pollution control. Using Taiyuan, China, as a case study, this study examines how to predict hourly air pollutant concentrations over longer periods of time while ensuring their accuracy. In this paper, an air pollutant concentration prediction method based on improved inverse distance interpolation and Informer model (EIDW-Informer), and hour-by-hour prediction of PM2.5, NO2, and O3 concentrations in Taiyuan, China is carried out. In this study, historical data from seven environmental monitoring stations in Taiyuan City were used to build multidimensional environmental vectors and calculate the similarity between sample points. Then, the missing values in the dataset were interpolated according to the similarity and distance weights, and the long series prediction was performed by Informer. The experimental results show that the EIDW-Informer method has advantages in hour-by-hour prediction compared to LSTM, CNN-LSTM, and Attention-LSTM models, which improves by 20%, 27%, and 43% on 1 h, 8 h, and 72 h time scales, respectively. [ABSTRACT FROM AUTHOR]

Thorough examination of various aspects related to the distribution of air pollutants in a specific region and the factors contributing to high concentrations is essential, as these elevated levels can be detrimental. To accomplish this, the development and improvement of a digital twin that encompasses all relevant physical processes in the atmosphere is necessary. This tool, known as DIGITAL AIR, has been created, and it is now necessary to extend it with precise sensitivity analysis. DIGITAL AIR is gaining popularity due to its effectiveness in addressing complex problems that arise in intricate environments; this motivates our further investigations. In this paper, we focus on the preparation and further investigation of DIGITAL AIR through sensitivity analysis with improved stochastic approaches for investigating high-level air pollutants. We discuss and test the utilization of this digital tool in tackling the issue. The unified Danish Eulerian model (UNI-DEM) plays a crucial role within DIGITAL AIR. This mathematical model, UNI-DEM, is highly versatile and can be applied to various studies concerning the adverse effects caused by elevated air pollution levels. [ABSTRACT FROM AUTHOR]

Economic development and fast growing urbanization in China have caused severe air pollution, with frequent pollution episodes endangering the health of inhabitants and disturbing social activities, and as an expanding metropolis, Chengdu has suffered ever since. The concentration variations of main air pollutants, such as PM10, PM2.5 and NO2, often show periodicity because of meteorological impact and anthropic activities, and display orientation discrepancies due to influences of wind speed (WS), frequency and pollutant sources. These features have complicated the mechanisms of pollution episodes and deepened the difficulty in pollution control evaluation. The WS has significant influences on the periodicity and orientation variations in pollutant concentrations, and quantifying the influence of which is of high significance and provides sustainable foundations for pollution alleviation strategies. Different time-scale cycles (i.e., Diurnal, weekly, seasonal and annual), along with the WS, wind frequency, wind and spatial orientations in urban areas, were analyzed in this paper. Results show that the periodicity of diurnal, seasonal and annual cycles is remarkable, and weekly cycle is obvious by adding the influence of the WS in 16 orientations. The WS has direct impacts on pollutants varying in the range of 1.5–2.5 m/s, and has a remarkable diffusion effect on pollutants once above 2.5 m/s. Over heavy pollution hours in diurnal, weekly, annual cycles and transitional seasons, the WS had more significant influences on pollutants, and whereas the wind frequency is not the main impact factor for orientation variations. For Chengdu, the northeast orientation is suitable to construct a wind panel with a remarkable diffusion effect on pollutants, while air pollutions in the northwest and southwest orientations were severe with the WS below 1.5 m/s, and pollution diffusion in the north-northwest orientation was the worst. This work can provide guidance and reference for urban planning optimization and air environment protection in cities with air quality control considerations impacted by city wind. [ABSTRACT FROM AUTHOR]

The variation of PM2.5 concentration in the atmosphere is closely related to the variation in weather patterns. The change in weather pattern is accompanied by the corresponding change in atmospheric circulation characteristics. It is necessary to explore the relationship between PM2.5 concentration changes and atmospheric circulation characteristics during pollution periods. In this paper, Lamb-Jenkinson objective classification method is applied to classify daily atmospheric circulation. The pollution periods are calculated and the atmospheric circulation variation rule is obtained. Combined with the physical parameter field (humidity, potential temperature, and potential height), a typical pollution period is analyzed. Additionally, the influence of atmospheric circulation type variation on PM2.5 concentration and transport channel during the pollution period was obtained. The results show that atmospheric circulation types in the study period are dominated by A-type (anticyclonic), N-type (north), and NE-type (northeast), indicating obvious seasonal differences, and the proportion of C-type (cyclonic) circulation was increased significantly in summer. During the pollution period analysis from 2 to 4 January 2019, atmospheric circulation type changed from N-type to NE-type (northeast), the wind direction changed from southeast wind, and the change of pressure gradient was consistent with the trend of the wind field. Moreover, the physical parameter field assisted in verifying the process of the pollution period from the conducive to the accumulation of PM2.5 to conducive to the deposition of pollutants and external transport. The research results would provide theoretical support for PM2.5 prediction during the pollution period and also supply a theoretical and technical basis for the establishment of ecological compensation standards for air pollution and atmospheric environmental control. [ABSTRACT FROM AUTHOR]

As the main energy source for thermal power generation, coal generates a large amount of NOx during its incineration in boilers, and excessive NOx emissions can cause serious pollution to the air environment. Selective catalytic reduction denitrification (SCR) selects the optimal amount of ammonia to be injected for denitrification based on the measurement of NOx concentration by the automatic flue gas monitoring system. Since the automatic flue gas monitoring system has a large delay in measurement, it cannot accurately reflect the real-time changes of NOx concentration at the SCR inlet when the unit load fluctuates, leading to problems such as ammonia escape and NOx emission exceeding the standard. In response to these problems, this paper proposes an SCR inlet NOx concentration prediction algorithm based on BMIFS-LSTM. An improved mutual information feature selection algorithm (BMIFS) is used to filter out the auxiliary variables with maximum correlation and minimum redundancy with NOx concentration, and reduce the coupling and dimensionality among the variables in the data set. The dominant and auxiliary variables are then fed together into a long short-term memory neural network (LSTM) to build a prognostic model. Simulation experiments are conducted using historical operation data of a 300 MW thermal power unit. The experimental results show that the algorithm in this paper reduces the average relative error by 3.45% and the root mean square error by 1.50 compared with the algorithm without auxiliary variable extraction, which can accurately reflect the real-time changes of NOx concentration at the SCR inlet, solve the problem of delay in NOx concentration measurement, and reduce the occurrence of atmospheric pollution caused by excessive NOx emissions. [ABSTRACT FROM AUTHOR]

Air pollution is a global environmental issue, and there is an urgent need for sustainable remediation techniques. Thus, phytoremediation has become a popular approach to air pollution remediation. This paper reviewed 28 eco-friendly indigenous plants based on both the air pollution tolerance index (APTI) and anticipated performance index (API), using tolerance level and performance indices to evaluate the potential of most indigenous plant species for air pollution control. The estimated APTI ranged from 4.79 (Syzygium malaccense) to 31.75 (Psidium guajava) among the studied indigenous plants. One of the selected plants is tolerant, and seven (7) are intermediate to air pollution with their APTI in the following order: Psidium guajava (31.75) > Swietenia mahogany (28.08) > Mangifera indica L. (27.97) > Ficus infectoria L. (23.93) > Ficus religiosa L. (21.62) > Zizyphus Oenoplia Mill (20.06) > Azadirachta indica A. Juss. (19.01) > Ficus benghalensis L. (18.65). Additionally, the API value indicated that Mangifera indica L. ranges from best to good performer; Ficus religiosa L. and Azadirachta indica A. Juss. from excellent to moderate performers; and Cassia fistula L. from poor to very poor performer for air pollution remediation. The Pearson correlation shows that there is a positive correlation between API and APTI (R2 = 0.63), and this implies that an increase in APTI increases the API and vice versa. This paper shows that Mangifera indica L., Ficus religiosa L., and Azadirachta indica A. Juss. have good potential for sustainable reduction in air pollution for long-term management and green ecomanagement development. [ABSTRACT FROM AUTHOR]

The incineration of sacrificial offerings generates numerous hazardous air pollutants, including particulate matter (PM), CO, SO2, NOx and non-methane hydrocarbons (NMHC), which has significant effects on the environment and human health. However, due to the concealment of sacrificial offerings incineration, the emission of such pollutants has not received sufficient attention. Relevant quantification of the emission, emission factors and pollution control measures for this pollution source are lacking. To address these problems, herein, we quantified the particulate matter and its chemical composition and the emission levels of gaseous pollutants, including SO2, NOx, NMHC and CO, by performing incineration experiments of four typical sacrificial offerings (Joss paper, Funeral wreath, Taoist paper art and Yuanbao paper), and obtained the emission factors and emission characteristics for the incineration of sacrificial offerings. Therefore, this study lays the foundation and provides support for establishing an emission inventory of the air pollutants from the incineration of sacrificial offerings and introducing corresponding pollution control measures. The results show that the emission concentrations of CO and total suspended particulate (TSP) from the incineration of sacrificial offerings greatly exceed the emission standard, with averages of 621.4 mg m−3 and 142.9 mg m−3 at 11% oxygen content, respectively. The average emission factors of SO2, NOx, NMHC, CO, PM10 and PM2.5 for the incineration of the four offerings are (0.47 ± 0.17) kg t−1, (2.46 ± 0.35) kg t−1, (5.78 ± 2.41) kg t−1, (32.40 ± 8.80) kg t−1, (4.23 ± 0.71) kg t−1 and (2.62 ± 0.48) kg t−1, respectively, among which the emission intensities of NMHC and CO are relatively high. Among the different types of sacrificial offerings, the overall average emission factor of air pollutants generated from the incineration of Yuanbao paper is the highest, which is mainly due to the low burning efficiency and the coating material. For the chemical composition of the particulate matters, ions, OC, EC and metal elements account for proportions of the PM2.5 at (23.55 ± 10.37) %, (29.74 ± 9.95) %, (14.83 ± 6.55) % and (13.45 ± 4.88) %, respectively, indicating that the organic pollution is severe [ABSTRACT FROM AUTHOR]

Air quality monitoring in polluted environments is of great significance to human health. Traditional methods use various pieces of meteorological equipment, which have limited applications in complex terrains and high costs. In this paper, a novel idea is put forward to solve the problem of air pollution monitoring in urban areas. We investigate whether air quality can be assessed visually by examining the haziness of photos from a far distance. Specifically, the correlation between the air quality indexes, such as the AQI, PM2.5, and PM10, of real outdoor scenarios and the haziness level evaluation scores of the monitoring images is calculated. The results show that the objective indicators can indeed reflect the level of air pollution, and the degree of correlation is invariant to the image size. To apply this new observation to a practical system, a novel method called fastDBCP (fast dark and bright channel prior) is developed. Based on a down-sampling strategy, a ratio is calculated between the dark and bright channel prior information in scaled images and adopted as the visual index of air pollution. Our experimental results show that the proposed metric not only demonstrates advantages in terms of its correlation degree and computational speed, but also shows a high level of classification accuracy compared to that of competing metrics. [ABSTRACT FROM AUTHOR]

In this paper, an integrative analysis is conducted for both sides of the border of Mexicali B.C. and Imperial Valley, regarding the official procedures and ways in which the collection, organization and disposal of data is carried out for the following: pollutant molecules such as PM2.5, PM10, CO, and O3; meteorological data such as temperature and relative humidity; data produced by epidemiological surveillance of acute respiratory infections; and deaths from all causes minus external ones. Some points around the binational surveillance system, raised in a technical report, are collected, giving continuity to the previous work carried out on this matter. Challenges are identified, recommendations are made, and possible solutions are offered to achieve a binational, unified and integrative system that centralizes meteorological, clinical, and pollution data, and that guarantees the quality and expeditious availability of the data concerning of the effects of air pollution on respiratory diseases and/or natural deaths of the local population. According to our results, air monitoring in Mexicali, clinical data on acute respiratory infections, morbidity, and mortality records must be improved, as well as the cooperation and coordination with the institutions of Imperial Valley. Finally, we show that despite the deficiencies and limitations found in the data generated in the locality, especially data on pollution and clinical data, it is possible, with great effort, to build models that measure and explain the effects of air pollution on health. Examples include the relative risk of death from exposure to PM2.5, PM10, CO, and O3; particulate matter air pollution effects on activation of pulmonary tuberculosis; the association between personal PM10 exposure and pulmonary function; and the estimation of costs and public health benefits by PM10 mitigation, among others. [ABSTRACT FROM AUTHOR]

An important task in the construction of China's ecological civilization, the selection and implementation of policy instruments fully reflect the actual effectiveness of the government's efforts to control air pollution. Based on the content analysis method, this study examines the changing process of air pollution control policy instruments in southwest China from 2010 to 2021 in terms of implementation, synergy, and integration of policy instruments. The results show that, in terms of the degree of mandatory, the frequency of using policy instruments generally increased with time, but the overall balance of the instrument portfolio was poor. In terms of the degree of synergy, a gradual shift occurred from government-led to government-society governance. However, the concept and modes of inter-governmental linkage and cross-regional collaborative governance need to be improved. As for the degree of systemic, a clear trend of instrument integration and more frequent information interaction was found. Emergency-oriented characteristics appear strong, but a regular governance mechanism is lacking. Therefore, this paper provides policy suggestions and academic considerations for further improving the effectiveness of air pollution management in southwest China from three aspects: optimizing the policy tool system, deepening the regional joint prevention and control mechanism of air pollution, and promoting intelligent air pollution management. [ABSTRACT FROM AUTHOR]

This paper explored the changes of six significant pollutants (PM2.5, PM10, SO2, NO2, O3, and CO) in Jilin City during the coronavirus disease 2019 (COVID-19) epidemic in 2022, and compared them with the same period of previous years to analyze the impact of anthropogenic emissions on the concentration of pollutants; The Weather Research and Forecasting Community Multiscale Air Quality (WRF–CMAQ) model was used to evaluate the effect of meteorological factors on pollutant concentration. The results showed that except for O3, the concentrations of the other five pollutants decreased significantly, with a range of 21–47%, during the lockdown period caused by the government's shutdown and travel restrictions. Compared with the same period in 2021, the decrease of PM2.5 was only 25% of PM10. That was because there was still a large amount of PM2.5 produced by coal-fired heating during the blockade period, which made the decrease of PM2.5 more minor. A heavy pollution event caused by adverse meteorological conditions was found during the lockdown period, indicating that only controlling artificial emissions cannot eliminate the occurrence of severe pollution events. The WRF–CMAQ results showed that the lower pollutant concentration in 2022 was not only caused by the reduction of anthropogenic emissions but also related to the influence of favorable meteorological factors (higher planetary boundary layer thickness, higher wind speed, and higher temperature). [ABSTRACT FROM AUTHOR]

It is well known that air pollution has a negative impact on human health. Research has shown an increasing trend in hospital admissions due to respiratory and heart diseases during and after consecutive days of high or even medium air pollution levels. The objective of this paper is to provide quantitative and qualitative data concerning the impact of long-term air pollution on the health of residents living in the Greater Athens Area (GAA). More accurately, the prevalence of bronchitis in children and the incidence of chronic bronchitis cases in adults due to particulate matter exposure are estimated utilizing the AirQ+ model. For this purpose, daily average concentrations of particulate matter with an aerodynamic diameter less than or equal to 10 μm (PM10) from five different locations within the GAA, covering the period 2001–2020, are used. The results show a significant correlation between PM10 concentrations and adverse health effects (R2 = 0.9). Interestingly, there were more cases of children suffering from bronchitis disease than cases of adults. In addition, it was observed that the unhealthiest areas in the GAA are the center of Athens city (mean annual PM10 concentration in 2019: 36 μgr/m3), as well as suburban areas (Lykovrissi and Marousi: mean annual PM10 concentrations in 2019 were 27 μgr/m3 and 28 μgr/m3, respectively). Finally, a decreasing trend for both PM10 concentrations and the prevalence of chronic bronchitis across the GAA was observed through the examined 20 years, which was significantly higher over the period 2010–2020. [ABSTRACT FROM AUTHOR]

Nanoparticles (NPs), nanoaerosols (NAs), ultrafine particles (UFPs), and PMs0.1 (diameters ≤ 0.1 µm or 100 nm) are used interchangeably in the field of atmospheric studies. This review article summarizes recent research on PM0.1 in Thailand. The review involved peer-reviewed papers that appeared in the Scopus and the Web of Science databases and included the most recently published articles in the past 10 years (2013–2022). PM0.1 mainly originate from combustion processes such as in motor vehicles. The highest mass concentration of PMs0.1 occurs during the dry season, in which open fires occur in some regions of Thailand. The northern area of the country has higher PM0.1 mass concentrations, followed by the central and southern areas. Carbonaceous nanoaerosols are produced during normal periods, and the proportions of organic to elemental carbon and char to soot suggest that these originate from motor vehicles. However, in haze periods, biomass fires can also produce carbon-containing particles. PM0.1 pollution from local and cross-border countries also needs to be considered. The overall conclusions reached will likely have a beneficial long-term impact on achieving a blue sky over Thailand through the development of coherent policies and managing new air pollution challenges and sharing knowledge with a broader audience. [ABSTRACT FROM AUTHOR]

Air pollution caused by particulate matter (PM) is a current problem in many cities. With the introduction of strict emission limits and electric cars, lower particle production is expected in the future. However, there are sources of particles that cannot be easily influenced. These include resuspension, where particles deposited on surfaces re-enter the air, causing pollution multiple times. Resuspension can account for up to 18% of the total emissions in some cases. The present paper focuses on the use of the computational fluid dynamics (CFD) tools to describe the flow in a street canyon where resuspension by wind occurs. Based on the calculated flow, a resuspension model is applied to see where resuspension occurs and how far the particles can travel. The shear stresses on the surfaces and the character of the flow field in the boundary layer are evaluated. Different building configurations and flow parameters are tested using a simple 2D model. The model makes it possible to see in which parts of the street canyon resuspension can occur. It shows that the particles leave the street canyon only from the surfaces where the conditions are suitable for resuspension. These particles then enter the mainstream. However, most of the particles stay in the canyon, which can cause resuspension to pollute the air repeatedly. This effect can have a severe impact on human health. The total dispersion of particles in the urban environment is evaluated. The results may be useful for cities that clean the streets, as it is clear which areas will benefit most from the cleaning. [ABSTRACT FROM AUTHOR]

To monitor the spread of the novel coronavirus (COVID-19), India, during the last week of March 2020, imposed national restrictions on the movement of its citizens (lockdown). Although India's economy was shut down due to restrictions, the nation observed a sharp decline in particulate matter (PM) concentrations. In recent years, Delhi has experienced rapid economic growth, leading to pollution, especially in urban and industrial areas. In this paper, we explored the linkages between air quality and the nationwide lockdown of the city of Delhi using a geographic information system (GIS)-based approach. Data from 37 stations were monitored from 12 March, 2020 to 2 April, 2020 and it was found that the Air Quality Index for the city was almost reduced by 37% and 46% concerning PM2.5 and PM10, respectively. The study highlights that, in regular conditions, the atmosphere's natural healing rate against anthropogenic activities is lower, as indicated by a higher AQI. However, during the lockdown, this sudden cessation of anthropogenic activities leads to a period in which the natural healing rate is greater than the induced disturbances, resulting in a lower AQI, and thus proving that this pandemic has given a small window for the environment to breathe and helped the districts of Delhi to recover from serious issues related to bad air quality. If such healing windows are incorporated into policy and decision-making, these can prove to be effective measures for controlling air pollution in heavily polluted regions of the World. [ABSTRACT FROM AUTHOR]