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Road transport plays a crucial role in sustaining all the personal and freight movement needs of residential, commercial, and industrial activities, and in Indian cities, big and small, vehicle exhaust emissions and dust from vehicle movement on the roads contribute to as much as 50% of particulate matter pollution in a year. Therefore, effective management of vehicle exhaust emissions is vital not only for improving current air quality but also for ensuring the long-term benefits from efforts to reduce air pollution. In the approved clean air action plans for 131 cities under the national clean air program (NCAP), more than 50% of the implementable actions are transport-centric. Having a reliable and replicable vehicle exhaust emissions inventory is essential for effective planning, which can help establish a baseline, support scenario analysis, and allow for tracking progress in the sector. This process begins with accessing accurate vehicle stock numbers, typically obtained from vehicle registration databases, traffic surveys, and other governmental records. Often, in low- and middle-income countries like India, these numbers require extensive data cleaning before they can be used for emissions and pollution analysis. This paper presents a cleaned, open-access vehicle stock database for India and outlines a methodology to build and maintain an in-use vehicle age-mix database for future years. The database covers the years 1993 to 2018 for the entire country and individual states, along with estimates of the age distribution of vehicles using survival functions. By offering a comprehensive and reliable data source, this paper aims to support sustainable national and urban air quality management efforts, helping policymakers and stakeholders make informed decisions to improve air quality and public health. [ABSTRACT FROM AUTHOR]

Air pollution knows no boundaries, which means for a city or a region to attain clean air standards, we must not only look at the emission sources within its own administrative boundary but also at sources in the immediate vicinity and those originating from long-range transport. And there is a limit to how much area can be explored to evaluate, govern, and manage designated airsheds for cities and larger regions. This paper discusses the need for an official airshed framework for India's air quality management and urban airsheds designated for India's 131 non-attainment cities under the national clean air program, and proposes climatically and geographically appropriate regional airsheds to support long-term planning. Between 28 states, eight union territories, 36 meteorological sub-regional divisions, and six regional meteorological departments, establishing the proposed 15 regional airsheds for integrated and collaborative air quality management across India is a unique opportunity. [ABSTRACT FROM AUTHOR]

This paper presents an analysis of NO2 emissions in Mashhad City utilizing statistical evaluations and the Cisco Network Model. The present study begins by evaluating NO2 emissions through statistical analysis, followed by the application of histograms and radar statistical appraisals. Subsequently, a model execution logic is developed using the Cisco Network Model to further understand the distribution and sources of NO2 emissions in the city. Additionally, the research incorporates managerial insights by employing Petri Net modeling, which enables a deeper understanding of the dynamic interactions within the air quality management system. This approach aids in identifying critical control points and optimizing response strategies, thus enhancing the overall effectiveness of urban air pollution mitigation efforts. The findings of this study provide valuable insights into the levels of NO2 pollution in Mashhad City and offer a structured approach to modeling NO2 emissions for effective air quality management strategies which can be extended to the other megacities as well. [ABSTRACT FROM AUTHOR]

A source apportionment scheme based on gas and aerosol phase reactive tracers has been implemented in the chemical-transport model FARM, to efficiently estimate contributions of different sources to ambient concentrations. The on-line scheme deals with all the main processes that the chemical species undergo in the model, to enhance consistency with the calculation of bulk concentrations. The fate of precursors through gas-phase chemical reactions is followed by an efficient solver that determines their incremental reactivity, while the contributions to the secondary particulate species from their gaseous precursors is determined by assuming the thermodynamic equilibrium between the two phases. The paper details the new employed methodologies and illustrates the application of the apportionment scheme (based on 6 source sectors) to PM10 and O3, simulated on three domains of different dimensions in Italy, all sharing the same horizontal resolution and a common region (Lombardy). Spatial patterns of results show, on average, a relevant contribution of heating on PM10 concentration in January, with local hotspots dominated by road traffic. Contributions appear consistent in the three simulated domains, apart from the boundary conditions, influenced by the dimension of the domain. Hourly series of contributions to O3 concentrations in July at three selected sites show the dominance of boundary conditions, underlining the large scale of O3 formation. Finally, for PM10 components, the resulting sectorial contributions are compared with the impacts computed via the brute force method, showing that results are similar for elemental carbon and sulfate, while they are different for nitrate and ammonium, due to a different allocation of contributions and impacts between the methods. Each approach responds in principle to a different purpose, and their combined use provides possibly a wide set of information useful for addressing the different air quality management needs. [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]

The United States has witnessed a concerning surge in the incidence of diseases like Coal Workers' Pneumoconiosis (CWP), despite numerous efforts aimed at prevention. This study delves into the realm of respiratory health by investigating the deposition of dust particles within the respiratory tract and lungs. By analyzing particles of varying sizes, shapes, velocities, and aerodynamic diameters, we aim to gain a comprehensive understanding of their impact on deposition patterns. This insight could potentially drive changes in dust exposure protocols within mining environments and improve monitoring practices. The interplay of several critical factors, including particle characteristics and an individual's breathing patterns, plays a pivotal role in determining whether particles settle in the lungs or are exhaled. This paper provides a comprehensive literature review on Respirable Coal Mine Dust (RCMD), with a specific focus on examining particle deposition across different regions of the airway system and lungs. Additionally, we explore the utility of Computational Fluid Dynamics (CFD) in simulating particle behavior within the respiratory system. Predicting the precise behavior of dust particles within the respiratory airway poses a significant challenge. However, through numerical simulations, we aspire to enhance our understanding of strategies to mitigate total lung deposition by comprehensively modeling particle interactions within the respiratory system. [ABSTRACT FROM AUTHOR]

Long-term exposure to air pollution will pose a serious threat to human health. Accurate prediction can help people reduce exposure risks and promote environmental pollution control. However, most previous studies have ignored the spatial spillover of air pollution, i.e., that the current region's air quality is also correlated with that of geographically adjacent areas. Therefore, this paper proposes an innovative spatiotemporal hybrid early warning system based on adaptive feature extraction and improved fuzzy cognition maps. Firstly, a spatial spillover analysis model based on the Moran index and local gravitational clustering was proposed to capture the diffusion and concentration characteristics of air pollution between regions. Then, an adaptive feature extraction model based on an optimized Hampel filter was put forward to process and correct the outliers in the original series. Finally, a hesitant fuzzy information optimized fuzzy cognitive maps model was proposed to forecast the air quality of urban agglomeration. The experimental results show that the air quality forecasting accuracy of urban agglomerations can be significantly improved when the geographical conditions and other interactions among cities are comprehensively considered, and the proposed model outperformed other benchmarks and can be used as a powerful analytical tool during urban agglomeration air quality management. [ABSTRACT FROM AUTHOR]

This I Atmosphere i Special Issue on "Fire and the Atmosphere" explores the atmosphere's role in a range of wildland fire related topics through 14 papers covering fire spread, fuels, climate change, fire behavior, smoke, fire weather and fire climate relationships. The papers can be loosely grouped into four categories: fire weather and climate, numerical modeling, smoke impacts and designing field measurement campaigns. Three of the four papers in the fire weather and climate category focus on fire weather indices, common tools for combining weather variables to reduce complex processes down to a more easily understood scale. [Extracted from the article]

PM2.5 and PM10 in the atmosphere seriously affect human health and air quality, a situation which has aroused widespread concern. In this paper, we analyze the temporal and spatial distribution of PM2.5 and PM10 concentrations from 2016 to 2021 based on real-time monitoring data. In addition, we also explore the influence of meteorological conditions on pollutants. The results show that PM2.5 and PM10 concentrations are similarly distribution in temporal and spatial from 2016 to 2021, and the average concentrations of both show a decreasing trend. The ratio of PM2.5 to PM10 is decreasing, indicating that the proportion of fine particles is declining. PM2.5 and PM10 concentrations are higher in spring and winter, but lower in summer. Spatially, it shows a gradual shift from the characteristic of "high in the south and low in the north" to a uniform homogenization across districts. The spatial distribution of PM2.5 and PM10 mass concentrations is synchronous by applying empirical orthogonal functions (EOF). The first EOF pattern exhibits a consistent characteristic of high in the southeast and low in the northwest. The second pattern EOF reflects the effect of impairing PM2.5 concentrations in the southeast during the winter of 2016–2018. The PM2.5 and PM10 concentrations are significantly negatively correlated with wind speed and precipitation in both spring and winter. On the other hand, from the perspective of the circulation situation, the southeasterly and weak westerly wind in spring produce convergence resulting in higher particulate matter concentrations in the south than in the north in Beijing. The westerly wind is flatter at 700 hPa geopotential height, which is conducive to the formation of stationary weather. The vertical direction of airflow in spring and winter is dominated by convergence and sinking, indicating the weak dispersion ability of the atmosphere. The reason for the accumulation of particulate matter at the surface is investigated, which is beneficial to provide the theoretical basis for air quality management and pollution control in Beijing. [ABSTRACT FROM AUTHOR]

This Special Issue of Energies, "Smart Cities and Positive Energy Districts: Urban Perspectives in 2021", introduces contemporary research on Smart Cities and on Positive Energy Districts. The present Special Issue, namely the fourth Special Issue, Smart Cities and Positive Energy Districts: Urban Perspectives in 2021, has been dedicated to tools, technologies, and system integration for Smart Cities and for Positive Energy Districts. The topic highlights the variety of research within this field, including research on: tools facilitating the evaluation of Sustainable Plus Energy neighborhoods, of enabling-technologies and procedures for Positive Energy Districts, and of multicriteria assessment for the identification of Positive Energy Districts; system integration related to optimized energy and air quality management in the COVID-19 scenario; system integration upgrading existing residential areas to the status of Positive Energy Districts; and renovation models for large scale actions. [ABSTRACT FROM AUTHOR]

High particulate matter (PM) concentrations in the cleanroom semiconductor factory have become a significant concern as they can damage electronic devices during the manufacturing process. PM can be predicted before becoming more concentrated based on its historical data to support factory management in regulating the air quality in the cleanroom. In this paper, a Multi-Dense Layer BiLSTM model is proposed to predict PM2.5 concentrations in the indoor environment of the cleanroom. To obtain reliability, validity, and interoperability data, the datasets containing temperature, humidity, PM0.3, PM0.5, PM1, PM2.5, PM5, and PM10 were retrieved in a standardized manner via oneM2M-defined representational state transfer application programmable interfaces by employing software platforms compliant with the Internet of Things (IoT) standard. Based on the proposed model, an algorithm was built providing short-term PM2.5 concentration predictions (one hour ahead, two hours ahead, and three hours ahead). The proposed model outperformed the RNN, LSTM, CNN-LSTM, and Single-Dense Layer BiLSTM models in terms of MSE, MAE, and MAPE values. The model created in this study could predict high PM2.5 concentration levels more accurately, thus providing vital support for operation and maintenance for the semiconductor industry. [ABSTRACT FROM AUTHOR]

This study investigates the characteristics of formaldehyde (HCHO) concentration and its reduction in newly constructed apartment buildings in Dubai. It addresses the significant health risks of high HCHO levels due to poor ventilation and extensive use of VOC-emitting materials. The research explores the effectiveness of various mitigation strategies, including air purifiers, photocatalyst applications, and window ventilation, in the living room, master bedroom (MBR), Room 1, and Room 2. HCHO concentrations were measured under different conditions: sealed windows, operation of an air purifier with a VOCs filter, and the application of a photocatalyst. The study followed a quantitative approach, recording real-time HCHO levels over 24 h. Results indicated that sealing windows led to HCHO concentrations surpassing the domestic standard of 0.1 ppm, with the MBR recording the highest concentration at 0.73 ppm. The air purifier showed minimal impact within the first 8 h but achieved an 18% reduction after 24 h of operation. Applying a photocatalyst to room surfaces resulted in a 50% reduction in HCHO levels. Ventilation through window openings significantly improved air exchange rates, with the air exchange per hour (ACH) increasing proportionally with window opening size. Smaller rooms with larger window-to-volume ratios, such as Room 1 and Room 2, maintained HCHO concentrations below the WHO standard of 0.1 ppm with partial window openings. The study concludes that window ventilation is the most effective strategy for reducing HCHO concentrations, highlighting the importance of integrating comprehensive ventilation designs in modern residential buildings to ensure healthier indoor environments in Dubai. [ABSTRACT FROM AUTHOR]

This study proposes a novel air pollution exposure index (APEI) metric, drawing from traditional elements in rhythmanalysis and public-life studies to help understand how people are exposed to air pollution in their urban environment and when the risks are greatest. It expands on the notion of rhythm as a socially constructed natural phenomenon connected to urban life and spaces. Air quality monitoring data, as well as bus times and in situ traffic and pedestrian counts, from Cable Street and Dalton Square in Lancaster were applied to define the APEI, which combines an air pollution index (NO2 and PM10), a surrogate for ambient air pollution level, with a population index, a surrogate for population flow. The index values were subsequently ranked in numeric order, i.e., a higher ranking shows increased exposure risk and vice versa, to determine total exposure on an individual level. This metric proves valuable in defining air pollution exposure status and recognizing factors associated with high pollution and population levels. Similarly, by comparing APEI values, one could evaluate exposure levels in different locations and seasons to verify when the APEI has increased at a given location and the different rhythms and flows responsible for an individual's exposure. Hence, it has potential use for urban planners and the city council's policymakers who are involved in Lancaster Air Quality Management. [ABSTRACT FROM AUTHOR]

The tracking of carbon and aerosol co-emissions is essential for environmental management. Satellite-based atmospheric synoptic observation networks provide large-scale and multifaceted data to help resolve emission behaviors. This study employs a comprehensive analysis of atmospheric dynamics, combustion byproducts, and aerosol characteristics around power plants. Strong correlations between Aerosol Optical Depth (AOD) at 500 nm and the column-averaged dry-air mole fraction of carbon dioxide (XCO2) were observed, revealing synchronous peaks in their emission patterns. The investigation into combustion completeness utilized metrics such as the ratio of carbon monoxide (CO)/XCO2 and Black Carbon Extinction (BCEXT)/Total Aerosol Extinction (TOTEXT). Discrepancies in these ratios across cases suggest variations in combustion efficiency and aerosol characteristics. Nitrogen dioxide (NO2) distributions closely mirrored XCO2, indicating consistent emission patterns, while variations in sulfur dioxide (SO2) distributions implied differences in sulfide content in the coal used. The influence of coal composition on AOD/XCO2 ratios was evident, with sulfide content contributing to variations besides combustion efficiency. This multifactorial analysis underscores the complex interplay of combustion completeness, aerosol composition, and coal components in shaping the air quality around power stations. The findings highlight the need for a nuanced understanding of these factors for effective air quality management. [ABSTRACT FROM AUTHOR]

The model environment is a key component that enables a virtual geographic environment (VGE) to meet the scientific requirements for simulating dynamic phenomena and performing analyses. Considering the comprehensiveness of geographic processes and the requirements for the replication of model-based research, this paper proposes a model base framework for a model environment of a VGE that supports both model construction and modelling management, resulting in improved reproducibility. In this framework, model management includes model metadata, creation, deposition, encapsulation, integration and adaptation; while modelling management focuses on invoking the model, model computation and runtime control of the model. Based on this framework, to consider the problem of ever-worsening air quality, we applied the Linux-Apache-MySQL-Perl stack plus Supervisor to implement the model base to support a VGE prototype using professional meteorological and air quality models. Using this VGE prototype, we simulated a typical air pollution case for January 2010. The prototype not only illustrates how a VGE application can be built on the proposed model base, but also facilitates air quality simulations and emergency management. [ABSTRACT FROM AUTHOR]

Vehicle emissions belong to the category of near-surface sources, occur close to human activity areas, and pose a greater threat to human health than other anthropogenic pollution sources. Furthermore, the study of the spatiotemporal characteristics of near-road traffic pollution is of great significance to urban and regional ambient air quality management, and is also an important basis for vehicle emission inventories, as well as the assessment of ambient air impact. Most previous studies have analyzed the spatiotemporal characteristics of hydrocarbons (HCs), carbon monoxide (CO), nitrogen oxides (NOx), and carbon dioxide (CO2) in urban vehicle emissions over a certain time, without considering the synergistic effect of mobile source particulate matter, NOx, and volatile organic compounds (VOCs). In this study, we analyze the composition of vehicles with different emission standards from road mobile sources in Anqing City, China. National category III and IV vehicles are the main contribution sources of various pollutants, accounting for more than 60% of emissions. Although national category I and II vehicles accounted for less than 1% of the total number of vehicles, their contribution to emissions cannot be ignored, especially for CO and HCs, the contribution of which from such vehicles can reach about 7%. This is mainly due to the low level of pollution control arising from the larger emission factor and greater age of these vehicles. Furthermore, eliminating old cars and increasing the proportion of national category VI vehicles can effectively reduce vehicle pollutant emissions. In terms of the spatiotemporal distribution characteristics, highways around urban areas are also the main sources of heavy vehicles, and the emission intensity of these pollutants is also higher on national roads and highways surrounding urban areas. In addition, the presence of m/p-xylene and toluene solvent-using species is detected, which indicates that petrol vehicle emissions, LPG and petrol volatilization, and solvent-using sources contribute significantly to ozone formation in the ozone pollution process. Comparing weekdays and non-weekdays, the PM2.5 peaks on non-weekdays are significantly higher than those on weekdays, and there is no "weekend effect", which indicates that traffic emissions have little influence on PM2.5 emissions, and may be related to energy use and industrial pollution. Overall, this study strengthens the understanding of the relationship between emissions, traffic volumes, and vehicle types on spatial and temporal scales, and emphasizes the need for further investigation and comprehensive measures to mitigate pollution from these emissions. [ABSTRACT FROM AUTHOR]

The Brazilian population grew approximately 9% from 2006 to 2016, and the number of deaths caused by air pollution increased by 14% in Brazil in the same period. Facing the lack of studies on air quality in the Metropolitan Area of Belo Horizonte (MABH)—the third most populous Brazilian metropolitan area—this study aimed to investigate the air quality and the trends of air pollutant concentrations in the MABH between 1995 and 2022, using data from the air quality monitoring network. The methodology consisted of checking MABHs air quality trends following the WHO air quality guidelines. The Mann–Kendall test was used to check statistically for the possibility of tendencies. The results showed a trend of stability in the concentrations of air pollutants in the MABH without any trend of improvement or worsening. However, the time series of the MABH exposed the challenge of ensuring better air quality that protects human health. Furthermore, the results reinforced the importance of focusing on pollutant sources and exposed the need for improvements in air quality management. Thus, it is essential to reverse the current dismantling scenario of the public environmental agencies in Brazil. [ABSTRACT FROM AUTHOR]

Currently, Tangshan confronts the dual challenge of elevated carbon emissions and substantial pollution discharge from the iron and steel industries (ISIs). While significant efforts have been made to mitigate air pollutants and carbon emissions within the ISIs, there remains a gap in comprehending the control of carbon emissions, air pollutant emissions, and their contributions to air pollutant concentrations at the enterprise level. In this study, we devised the Air Pollutant and Carbon Emission and Air Quality (ACEA) model to identify enterprises with noteworthy air pollution and carbon emissions, as well as substantial contributions to air pollutant concentrations. We constructed a detailed inventory of air pollutants and CO2 emissions from the iron and steel industry in Tangshan for the year 2019. The findings reveal that in 2019, Tangshan emitted 5.75 × 104 t of SO2, 13.47 × 104 t of NOx, 3.55 × 104 t of PM10, 1.80 × 104 t of PM2.5, 5.79 × 106 t of CO and 219.62 Mt of CO2. The ACEA model effectively pinpointed key links between ISI enterprises emitting air pollutants and carbon dioxide, notably in pre-iron-making processes (coking, sintering, pelletizing) and the Blast furnace. By utilizing the developed air pollutant emission inventory, the CALPUFF model assessed the impact of ISI enterprises on air quality in the Tangshan region. Subsequently, we graded the performance of air pollutant and CO2 emissions following established criteria. The ACEA model successfully identified eight enterprises with significant air pollution and carbon emissions, exerting notable influence on air pollutant concentrations. Furthermore, the ACEA outcomes offer the potential for enhancing regional air quality in Tangshan and provide a scientific instrument for mitigating air pollutants and carbon emissions. The effective application of the ACEA model in Tangshan's steel industry holds promise for supporting carbon reduction initiatives and elevating environmental standards in other industrial cities across China. [ABSTRACT FROM AUTHOR]

In this study, we analyze the performance of ventilation modules to improve air quality in educational facilities. Using (CFD), we examine the flow design variables of a window-mounted ventilation module. Using computational analysis, we analyze various flow design characteristics of window-mounted ventilation modules and review optimal conditions. First, we measure the carbon dioxide concentration in the classroom and use CFD to analyze the internal air characteristics according to the ventilation module's inflow speed, inflow angle, and indoor temperature conditions. According to classroom air quality management standards, the concentration of carbon dioxide must be managed below 1000 ppm. When the ventilation module's inflow velocity was 2.0 m/s, a carbon dioxide concentration of less than 1000 ppm was measured in the classroom. Additionally, an air filter was selected to prevent the inflow of external fine dust through the ventilation module. The suitability of HEPA H14 was reviewed to design the weight concentration of fine dust flowing from the ventilation module to be less than 50 μg/m3. Through research, flow design conditions for a window-mounted ventilation module were presented to reduce carbon dioxide concentration inside the classroom. The analysis of the ventilation system flow characteristics proposed in this study derived primary data for improving the classroom ventilation system. [ABSTRACT FROM AUTHOR]

Photocatalytic concrete constitutes a promising technique to reduce a number of air contaminants such as NOx and VOC's, especially at sites with a high level of pollution: highly trafficked canyon streets, road tunnels, the urban environment, etc. Ideally, the photocatalyst, titanium dioxide, is introduced in the top layer of the concrete pavement for best results. In addition, the combination of TiO2 with cement-based products offers some synergistic advantages, as the reaction products can be adsorbed at the surface and subsequently be washed away by rain. A first application has been studied by the Belgian Road Research Center (BRRC) on the side roads of a main entrance axis in Antwerp with the installation of 10.000 m² of photocatalytic concrete paving blocks. For now however, the translation of laboratory testing towards results in situ remains critical of demonstrating the effectiveness in large scale applications. Moreover, the durability of the air cleaning characteristic with time remains challenging for application in concrete roads. From this perspective, several new trial applications have been initiated in Belgium in recent years to assess the "real life" behavior, including a field site set up in the Leopold II tunnel of Brussels and the construction of new photocatalytic pavements on industrial zones in the cities of Wijnegem and Lier (province of Antwerp). This paper first gives a short overview of the photocatalytic principle applied in concrete, to continue with some main results of the laboratory research recognizing the important parameters that come into play. In addition, some of the methods and results, obtained for the existing application in Antwerp (2005) and during the implementation of the new realizations in Wijnegem and Lier (2010-2012) and in Brussels (2011-2013), will be presented. [ABSTRACT FROM AUTHOR]

Most of the microfluidics-related literature describes devices handling liquids, with only a small part dealing with gas-based applications, and a much smaller number of papers are devoted to the separation and/or detection of airborne inorganic particles. This review is dedicated to this rather less known field which has become increasingly important in the last years due to the growing attention devoted to pollution monitoring and air quality assessment. After a brief introduction summarizing the main particulate matter (PM) classes and the need for their study, the paper reviews miniaturized devices and/or systems for separation, detection and quantitative assessment of PM concentration in air with portable and easy-to-use platforms. The PM separation methods are described first, followed by the key detection methods, namely optical (scattering) and electrical. The most important miniaturized reported realizations are analyzed, with special attention given to microfluidic and micromachined or micro-electro-mechanical systems (MEMS) chip-based implementations due to their inherent capability of being integrated in lab-on-chip (LOC) type of smart microsystems with increased functionalities that can be portable and are easy to use. The operating principles and (when available) key performance parameters of such devices are presented and compared, also highlighting their advantages and disadvantages. Finally, the most relevant conclusions are discussed in the last section. [ABSTRACT FROM AUTHOR]

The solution of chemical kinetics is one of the most computationally intensive tasks in atmospheric chemical transport simulations. Due to the stiff nature of the system, implicit time stepping algorithms which repeatedly solve linear systems of equations are necessary. This paper reviews the issues and challenges associated with the construction of efficient chemical solvers, discusses several families of algorithms, presents strategies for increasing computational efficiency, and gives insight into implementing chemical solvers on accelerated computer architectures. [ABSTRACT FROM AUTHOR]

The predictions from air quality models are subject to many sources of uncertainty; among them, grid resolution has been viewed as one that is limited by the availability of computational resources. A large grid size can lead to unacceptable errors for many pollutants formed via nonlinear chemical reactions. Further, insufficient grid resolution limits the ability to perform accurate exposure assessments. To address this issue in parallel to increasing computational power, modeling techniques that apply finer grids to areas of interest and coarser grids elsewhere have been developed. Techniques using multiple grid sizes are called nested grid or multiscale modeling techniques. These approaches are limited by uncertainty in the placement of finer grids since pertinent locations may not be known a priori, loss in solution accuracy due to grid boundary interface problems, and inability to adjust to changes in grid resolution requirements. A different approach to achieve local resolution involves using dynamic adaptive grids. Various adaptive mesh refinement techniques using structured grids as well as mesh enrichment techniques on unstructured grids have been explored in atmospheric modeling. Recently, some of these techniques have been applied to full blown air quality models. In this paper, adaptive grid methods used in air quality modeling are reviewed and categorized. The advantages and disadvantages of each adaptive grid method are discussed. Recent advances made in air quality simulation owing to the use of adaptive grids are summarized. Relevant connections to adaptive grid modeling in weather and climate modeling are also described. [ABSTRACT FROM AUTHOR]

Chemical data assimilation is the process by which models use measurements to produce an optimal representation of the chemical composition of the atmosphere. Leveraging advances in algorithms and increases in the available computational power, the integration of numerical predictions and observations has started to play an important role in air quality modeling. This paper gives an overview of several methodologies used in chemical data assimilation. We discuss the Bayesian framework for developing data assimilation systems, the suboptimal and the ensemble Kalman filter approaches, the optimal interpolation (OI), and the three and four dimensional variational methods. Examples of assimilation real observations with CMAQ model are presented. [ABSTRACT FROM AUTHOR]

Wireless sensor networks equipped with various gas sensors have been actively used for air quality monitoring. Previous studies have typically explored system issues that include middleware or networking performance, but most research has barely considered the details of the hardware and software of the sensor node itself. In this paper, we focus on the design and implementation of a sensor board for air pollutant monitoring applications. Several hardware and software issues are discussed to explore the possibilities of a practical WSN-based air pollution monitoring system. Through extensive experiments and evaluation, we have determined the various characteristics of the gas sensors and their practical implications for air pollutant monitoring systems. [ABSTRACT FROM AUTHOR]

Current remote sensing techniques fail to address the task of air quality monitoring over complex regions where multiple pollution sources produce high spatial variability. This is due to a lack of suitable satellite-sensor combinations and appropriate aerosol optical thickness (AOT) retrieval algorithms. The new generation of small satellites, with their lower costs and greater flexibility has the potential to address this problem, with customised platform-sensor combinations dedicated to monitoring single complex regions or mega-cities. This paper demonstrates the ability of the European Space Agency's small satellite sensor CHRIS/PROBA to provide reliable AOT estimates at a spatially detailed level over Hong Kong, using a modified version of the dense dark vegetation (DDV) algorithm devised for MODIS. Since CHRIS has no middle-IR band such as the MODIS 2,100 nm band which is transparent to fine aerosols, the longest waveband of CHRIS, the 1,019 nm band was used to approximate surface reflectance, by the subtraction of an offset derived from synchronous field reflectance spectra. Aerosol reflectance in the blue and red bands was then obtained from the strong empirical relationship observed between the CHRIS 1,019 nm, and the blue and red bands respectively. AOT retrievals for three different dates were shown to be reliable, when compared with AERONET and Microtops II sunphotometers, and a Lidar, as well as air quality data at ground stations. The AOT images exhibited considerable spatial variability over the 11 × 11km image area and were able to indicate both local and long distance sources. [ABSTRACT FROM AUTHOR]

Legislative regulations on atmospheric pollution have been established in different parts of the world for addressing air quality management. An important public commitment, common among all nations, is to ensure environmental safety and health protection, particularly for the most fragile population groups. Each country has its own rules and practices to provide adequate and timely information on ambient air quality. Information is given either through easily accessible media, including websites and apps, or by traditional means of telecommunication. An air quality index (AQI) is definitely a valuable tool for disseminating data on the main regulated pollutants and represents a readable indicator of the prevailing situation of air quality in the area. Several calculating expressions were formulated to combine, in a unique value, different parameters, and a few methods were created to determine and compare different AQIs. This paper gives almost a global overview of approaches and tools used to inform the public about the status of the ambient air quality. Different AQIs are analyzed to contribute to the sharing of air quality management practices and information to raise public awareness and to help policymakers to act accordingly. [ABSTRACT FROM AUTHOR]

The impact of urban morphology on air quality, particularly within deep canyons with longer residence times for complex chemical processes, remains insufficiently addressed. A flexible multi-box framework was used to simulate air quality at different canyon heights (3 m and 12 m). This approach incorporated essential parameters, including ventilation rates, background concentrations, photochemical schemes, and reaction coefficients. A field campaign within a deep canyon with an aspect ratio of 3.7, in Naples, Italy was conducted and used for the model evaluation. The model performance demonstrated good agreement, especially at the street level, when employing a realistic light intensity profile and incorporating volatile organic compound (VOC) chemistry. Our findings indicate that peroxyl radical production affects NO2 and O3 levels by up to 9.5% in deep canyons and underscore the significance of vertical distribution (approximately 5% variance) in health assessments and urban air quality strategy development. The model response was sensitive to changes in emissions as expected, but also, somewhat more surprisingly, to background conditions, emphasizing that policies to remove pollution hotspots must include local and broader citywide action. This work advances the understanding of air quality dynamics in deep urban canyons and presents a valuable tool for effective air quality management in intricate urban environments. [ABSTRACT FROM AUTHOR]

Limitations in the current capability of monitoring PM2.5 adversely impact air quality management and health risk assessment of PM2.5 exposure. Commonly, ground-based monitoring networks are established to measure the PM2.5 concentrations in highly populated regions and protected areas such as national parks, yet large gaps exist in spatial coverage. Satellite-derived aerosol optical properties serve to complement the missing spatial information of ground-based monitoring networks. However, satellite remote sensing AODs are hampered under cloudy/hazy conditions or during nighttime. Here we strive to overcome the long-standing restriction that surface PM2.5 cannot be obtained with satellite remote sensing under cloudy/hazy conditions or during nighttime. In this work, we introduce a deep spatiotemporal neural network (ST-NN) and demonstrate that it can artfully fill these observational gaps. We quantified the quantitative impact of input variables on the results using sensitivity and visual analysis of the model. This technique provides ground-level PM2.5 concentrations with a high spatial resolution (0.01°) and 24-h temporal coverage, hour-by-hour, complete coverage. In central and eastern China, the 10-fold cross-validation results show that R2 is between 0.8 and 0.9, and RMSE is between 6 and 26 (µg m−3). The relative error varies in different concentration ranges and is generally less than 20%. Better constrained spatiotemporal distributions of PM2.5 concentrations will contribute to improving health effects studies, atmospheric emission estimates, and air quality predictions. [ABSTRACT FROM AUTHOR]

The objective of this paper was to offer a comparative analysis of currently implemented statutory and technical regulations in Taiwan and Japan for volatile organic compounds (VOC) in indoor atmospheres. The findings should help to manage indoor air quality (IAQ) based on public and occupational health considerations. The first part of the present study summarizes the Indoor Air Quality Management Act in Taiwan and related regulations for building materials. We further highlight that Taiwan became the second country in the world to enact an IAQ management law in 2011. In addition, the permissible exposure limits (PEL) are also addressed to recognize safe levels of VOC concentrations below which adverse health effects are not expected to occur in the workplace environment. In the second part of the paper, the statuses of statutory and voluntary regulations for IAQ issues in Japan are compiled from the official websites of the central ministries, including the Ministry of Education, Culture, Sports, Science and Technology, the Ministry of Land, Infrastructure, Transport and Tourism, and the Ministry of Health, Labor and Welfare. This analysis shows that both countries have adopted similar processes to establish the IAQ standard/guideline values and low-emission building materials, despite slight differences in their methods and central ministries. In contrast, the VOCs regulated by these regulations differ completely, with the exception of formaldehyde. Although the IAQ standards in Taiwan seem to be more stringent than those in Japan, Japan’s longer experience shows a diversity of management tools and regulations based on the guideline values. [ABSTRACT FROM AUTHOR]

Featured Application: The results presented in the article can be used to optimize activities in the field of road traffic management to improve air quality in urban street canyons. Constantly changing vehicle stock, modification of road infrastructure, and other conditions result in a need to update the knowledge on the effectiveness of individual traffic management strategies, which could form the basis for actions taken by local authorities to improve air quality in crowded city centers, especially in street canyons. The article presents research results that evaluate the theoretical effects of introducing select traffic reorganization scenarios in the example of four street canyons located in Krakow (Poland) that are different in terms of vehicle traffic volume and canyon geometry. These scenarios were based on a reduction in the average traffic speed, road capacity or the admission of cars meeting certain exhaust emission standards. The authors estimated changes in emissions of nitrogen oxides (NO, NO2 and total NOx) and particulate matter (PM10 and PM2.5) as well as investigated the effect of these changes on air quality in the canyons using the Operational Street Pollution Model (OSPM). Significant effects in terms of improving air quality were identified only in scenarios based on a significant reduction in traffic volume and the elimination of passenger cars and light commercial vehicles with internal combustion engines that did not meet the requirements of the Euro 4, Euro 5 or Euro 6 emission standards. For these scenarios, depending on the variant and canyon analyzed, the emission reduction was achieved at a level of approximately 36–66% for NO, 28–77% for NO2, 35–67% for NOx and 44–78% for both PM10 and PM2.5. The expected effect of improving air quality in individual street canyons for these substances was 15–44%, 5–14%, 11–36% and 3–14%, respectively. The differences obtained in the percentage reduction of emissions and pollutant concentrations in the air were the result of a relatively high background of pollutants that suppress the achieved effect of improving air quality to a large extent. [ABSTRACT FROM AUTHOR]

Two simple mass-balance models for estimating the concentration of air pollutants inside buildings are presented for pollutants originating from outdoors or generated indoors. The models can be used to establish average pollution loads on heritage objects inside buildings and assist in risk assessment for conservation. The models can be run with a minimum of data, either based on fixed conditions or as a Monte Carlo simulation based on plausible intervals of the input factors. Input data can be obtained by simple measurements or based on the literature. A museum storage hall in Denmark was used as a test site for demonstrating the models. They were evaluated with regard to the prediction of the indoor/outdoor concentration ratio for ozone and nitrogen dioxide and the build-up concentration of indoor generated organic acids. The pros and cons of such models were discussed, where the main reservation is related to shortcomings when real buildings are more complicated than the single-zone structure of the models. A strength of the models is the easy adaption to an indoor environment and, despite being semi-quantitative at times, the simplicity of the models, which allows for practical everyday use in air quality management of heritage buildings. [ABSTRACT FROM AUTHOR]

The Atmosphere Special Issue "Special Issue Air Quality and Smoke Management" explores our ability to simulate wildland fire smoke events and the potential to link such modeling to future studies of human health impacts. Using an atmospheric chemical transport model, Koman et al. examined general air quality with and without wildland fire smoke particulate matter (PM2.5) to assess potential smoke exposure in California, USA. The goals for this Special Issue was to assess the current science related to smoke from wildland fire, identify tools and technologies for improving smoke management, and identify knowledge gaps limiting our ability to manage smoke and mitigate smoke impacts. [Extracted from the article]

In this article, we aimed to study the forecasting of hourly PM2.5 surface mass concentrations in the city of Tripoli, Libya. We employed three state-of-the-art deep learning models, namely long short-term memory, gated recurrent unit, and convolutional neural networks, to forecast PM2.5 levels using univariate time series methodology. Our results revealed that the convolutional neural networks model performed the best, with a coefficient of variation of 99% and a mean absolute percentage error of 0.04. These findings provide valuable insights into the use of deep learning models for forecasting PM2.5 and can inform decision-making regarding air quality management in the city of Tripoli. [ABSTRACT FROM AUTHOR]

Thermal power plants (TPPs) and national industrial complexes (NICs) are widely known as being among the major causes of changes in the concentrations of fine particulate matter (PM2.5). However, little is known about the changes in PM2.5 concentration caused by the operation of these facilities in South Korea and the health burden attributable to them, including early death. There were two purposes to this study. The first was to quantitatively evaluate the changes in PM2.5 concentration caused by TPPs and NICs in Korea. The second was to estimate the number of early deaths as a health burden attributable to such changes in PM2.5 concentration. The changes in PM2.5 concentration caused by the operation of TPPs and NICs were investigated within TPPs in 2013 and within NICs in 2015. The number of early deaths in 2015 caused by changes in PM2.5 concentration was estimated using the Environmental Benefits Mapping and Analysis Program (BenMAP). Nationwide, the annual average concentration of PM2.5 caused by the operation of TPPs and NICs was estimated to increase by 0.611 μg/m3 and 1.245 μg/m3, respectively, suggesting that NICs contributed about twice as much to this concentration as TPPs. The same trend was also observed regarding the number of early deaths, with TPPs and NICs accounting for 1017 and 2091 early deaths per year, respectively, indicating that the operation of NICs causes a health burden about twice as high as that caused by TPPs. However, the changes in PM2.5 concentration were found to be high near TPPs and NICs, while the health burden caused by exposure to PM2.5 varied according to the level of population distribution and mortality in each air (quality) control zone (ACZ) to which one is exposed. The findings of this study are expected to be utilized as reference data when setting goals to strengthen air quality management (AQM) in each ACZ in Korea. [ABSTRACT FROM AUTHOR]

In November 2019, the Supreme Court of India issued a notification to all the states in the National Capital Region of Delhi to install smog towers for clean air and allocated INR 36 crores (~USD 5.2 million) for a pilot. Can we vacuum our air pollution problem using smog towers? The short answer is "no". Atmospheric science defines the air pollution problem as (a) a dynamic situation where the air is moving at various speeds with no boundaries and (b) a complex mixture of chemical compounds constantly forming and transforming into other compounds. With no boundaries, it is unscientific to assume that one can trap air, clean it, and release into the same atmosphere simultaneously. In this paper, we outline the basics of atmospheric science to describe why the idea of vacuuming outdoor air pollution is unrealistic, and the long view on air quality management in Indian cities. [ABSTRACT FROM AUTHOR]

Nanoparticles, defined as particles with one dimension below 100 nm, contribute little to the total mass concentration in respirable coal mine dust (RCMD) toxicological studies, but they could have a considerable part in the adverse health effects by RCMD inhalation. It has been shown that inhaled nanoparticles can penetrate deep into the lung and could plausibly contribute to acute and chronic pulmonary diseases by triggering oxidative stress formation and inducing inflammation. RCMD nanoparticles from samples collected in an underground mine in the United States were analyzed by a particle separation technique, field-flow fractionation (FFF), for size, morphology, and elemental composition. Two sub-techniques, asymmetrical flow field-flow fractionation (AsFlFFF), and sedimentation field-flow fractionation (SdFFF) were used to increase the detection size range. Nanoparticles with a size range between 25–400 nm were detected. Fractions were collected throughout the size distribution and were analyzed by electron microscopy and inductively-coupled plasma mass spectrometry (ICP-MS). Electron micrographs showed the presence of carbonaceous particles, mineral particles, diesel particles, and aggregates. Major and trace elements such as Si and Ca were detected in high abundance in all fractions. Other metals included Mg, Fe, Al, Cs, and Pb. Higher relative concentrations of Cs and Pb were observed at the size range below 30 nm. The data suggests that nanoparticles in RCMD can be highly reactive, either as a result of their size or their potential to carry toxins such as transition and heavy metals. To the best of the authors' knowledge, this is the first data on the size, morphology, and composition of RCMD nanoparticles with a size below 100 nm. [ABSTRACT FROM AUTHOR]

Mine dust is one of the main disasters that threatens the safety of coal mine production, deteriorates the underground working environment and induces various occupational diseases. Based on the harm of mine dust identified and the current domestic and foreign dust-proof technology, the technical research of the foam dust reduction is reviewed; the advantages and disadvantages of foam dust reduction are also discussed. According to the experimental exploration and the study of foam flow characteristics, the process of foam device and foam nozzle separation was adopted, and some site tests were conducted in fully mechanized mining and excavation working faces in Zhangcun, Shanjiaoshu and Jinjia coal mines. The foam technology led to a 59.5% dust removal efficiency in Zhangcun coal mine, which is much higher than the dust removal efficiency of the water spray dust reduction. The efficiency of total mine dust in Shanjiaoshu coal mine reached as high as 72%, and the efficiency of respirable mine dust reached 67%. Compared with the circumstance with no dust-control measure, the average density of the total mine dust in Jinjia coal mine decreased by 22.75~32.95% using foam technology. The results show that the use of the foam to inhibit the transformation of mine dust from the source of dust production to the floating state and the efficiency of dust control is greatly improved. This method can effectively reduce the mine dust density in underground mining and excavation working faces, clean the working environments, and guarantee the health of miners. The research results are of great significance for improving the production conditions of mining and excavating face and building green mines and standardized mines. Moreover, foam technology for dust control has been widely used in coal mines. [ABSTRACT FROM AUTHOR]

Due to some harmful effects on humans and the environment, particulate matter (PM) has recently become among the most studied atmospheric pollutants. Given the growing sensitivity to the problem and, since production and accumulation phenomena involving both primary and secondary P M 10 fractions are complex and non-linear, environmental authorities need tools to assess their plans designed to improve the air quality as requested from environmental laws. Multi-criteria decision analysis (MCDA) can be applied to support decision makers, by processing quantitative opinions provided by pools of experts, especially when different views on social aspects should be considered. The results obtained through this approach, however, can be highly dependent on the subjectivity of experts. To partially overcome these challenges, this paper suggests a two-step methodology in which an MCDA is fed with the solution of a multi-objective analysis (MOA). The methodology has been applied to a test case in northern Italy and the results show that this approach is a viable solution for the inclusion of subjective criteria in decision making, while reducing the impact of uncertain expert opinions for data that can be computed through the MOA. [ABSTRACT FROM AUTHOR]

Air pollution is becoming a prominent social problem in fast-growing Asian economies. Taking the Bangkok Metropolitan Region (BMR) as a case, we conducted an observational study of fine particulate matter (PM2.5) and acid deposition, consisting of their continuous monitoring at two sites. To find the major contributing sources of PM2.5, the PM composition data were analyzed by a receptor modeling approach while the pollution load from BMR sources to the air was characterized by an emission inventory. Our data show generally alarming levels of PM2.5 in the region, of which transportation and biomass burning are two major sources. In this paper, we present a general overview of our observational findings, contrast the scientific information with the policy context of air quality management in BMR, and discuss policy implications. In BMR, where a set of conventional regulatory instruments on air quality management are already in place, a solution for the air pollution problem should lie in a combination of air quality regulation and other policies, such as energy and agricultural policies. [ABSTRACT FROM AUTHOR]

In a regenerative air sweeper, airflow and dust particles entering the system are filtered and recirculated within the system. The uncirculated portion of the exhaust air in the system spreads to the ambient air, and PM2.5 dust in the air can poison the environment and adversely affect human health. The development of an airflow control system to reduce road dust emissions and improve air quality was the main contribution of this study. A regenerative air sweeper airflow control system is designed to direct the air from the centrifugal fan back into the pickup head to fully absorb the dust particles and balance the positive and negative air pressures inside the pickup head. The modeling and analysis of the dust control system were performed using an experimental test rig system. A mathematical model of the fundamental parameters of the regenerative air sweeper and dust control system was established. Computational fluid dynamics (CFD) ANSYS was used for the analysis to determine the direction of airflow via the suction and inlet ducts. The discrete particle model (DPM) accurately predicted particle trajectories and measured the suction efficiency of particles of different shapes and types. By controlling the circulating harmful air flow in the system, the amount of PM2.5 released into the atmosphere was reduced by 90%. The suction efficiency of the 200 μ m sized sand particles was higher than 95%. The results provide theoretical and methodological assistance for the development of improved road sweeper dust control systems. [ABSTRACT FROM AUTHOR]

China has imposed an ultra-low emissions (ULE) standards policy regarding the emission concentrations of the thermal power industry, and the region of the Fen-Wei Plain (China), which is an important area regarding blue-sky protection, is in urgent need of accelerating the transformation of its thermal power units to conform with China's ULE standards. Based on China's high-resolution thermal power industry emission inventory, this study used the Comprehensive Air Quality Model with extensions (CAMX) to examine emissions from the thermal power industry and the associated impacts on the atmospheric environment of the Fen-Wei Plain. (1) Spatially, the highest emissions and the greatest air quality contributions were concentrated in the Shanxi and Henan areas, which are regions that have considerable power generation capacity. (2) In terms of species, NOX represented the largest air quality contribution and registered the smallest reduction. (3) Temporally, emission sources presented the greatest contributions in summer. (4) In terms of policy evaluation, implementation of ULE standards resulted in the reduction of the contribution of thermal power industry emissions to air quality by 56.82%, 27.44%, and 44.55% for SO2, NOX, and PM2.5, respectively. The results reflect the current levels of pollution on the Fen-Wei Plain attributable to the thermal power industry and provide insight in relation to decision-making processes regarding regional air quality management. [ABSTRACT FROM AUTHOR]

Air quality issues are frequent in urbanized valleys, particularly in wintertime when a temperature inversion forms and the air within the valley is stably stratified over several days. In addition to pollutant sources, local winds can have a significant impact on the spatial distribution and temporal evolution of pollutant concentrations. They can be very complex and difficult to represent in numerical weather prediction models, particularly under stable conditions. Better knowledge of these local winds from observations is also a prerequisite to improving air quality prediction capability. This paper analyses local winds during the Passy-2015 field experiment that took place in a section of the Arve river valley, near Chamonix–Mont-Blanc. This location is one of the worst places in France regarding air quality. The wind analysis, which is mainly based on scanning Doppler lidar data sampling a persistent temperature inversion episode, reveals features consistent with the higher pollutant concentrations observed in this section of the valley as well as their spatial heterogeneities. In particular, an elevated down-valley jet is observed at night in the northern half of the valley, which, combined with a weak daytime up-valley wind, leads to very poor ventilation of the lowest layers. A northeast–southwest gradient in ventilation is observed on a daily-average, and is consistent with the PM10 heterogeneities observed within the valley. [ABSTRACT FROM AUTHOR]

The concentration of total suspended particles (TSP) and nanoparticles (PM0.1) over Hat Yai city, Songkhla province, southern Thailand was measured in 2019. Organic carbon (OC) and elemental carbon (EC) were evaluated by carbon aerosol analyzer (IMPROVE-TOR) method. Thirteen trace elements including Al, Ba, K, Cu, Cr, Fe, Mg, Mn, Na, Ni, Ti, Pb, and Zn were evaluated by ICP-OES. Annual average TSP and PM0.1 mass concentrations were determined to be 58.3 ± 7.8 and 10.4 ± 1.2 µg/m3, respectively. The highest levels of PM occurred in the wet season with the corresponding values for the dry seasons being lower. The averaged OC/EC ratio ranged from 3.8–4.2 (TSP) and 2.5–2.7 (PM0.1). The char to soot ratios were constantly less than 1.0 for both TSP and PM0.1, indicating that land transportation is the main emission source. A principal component analysis (PCA) revealed that road transportation, industry, and biomass burning are the key sources of these particles. However, PM arising from Indonesian peatland fires causes an increase in the carbon and trace element concentrations in southern Thailand. The findings make useful information for air quality management and strategies for controlling this problem, based on a source apportionment analysis. [ABSTRACT FROM AUTHOR]

The current U.S. emission control requirements for on-road motor vehicles are driven by the ozone problem in the South Coast Air Basin (SoCAB) in southern California. Based on ozone modeling performed for Air Quality Management Plans (AQMPs), the SoCAB ozone attainment plan requires large (>80%) amounts of emission reductions in oxides of nitrogen (NOx) from current levels with more modest (~40%) controls on Volatile Organic Compounds (VOC). The shelter in place orders in response to the 2020 COVID-19 pandemic resulted in an immediate reduction in emissions, but instead of ozone being reduced, in 2020 the SoCAB saw some of the highest observed ozone levels in decades. We used the abrupt emissions reductions from 2019 to 2020 caused by COVID-19 to conduct a dynamic model evaluation of the Community Multiscale Air Quality (CMAQ) model to evaluate whether the models used to develop ozone control plans can correctly simulate the ozone response to the emissions reductions. Ozone modeling was conducted for three scenarios: 2019 Base, 2020 business-as-usual (i.e., without COVID reductions), and 2020 COVID. We found that modeled ozone changes between 2019 and 2020 were generally consistent with the observed ozone changes. We determined that meteorology played the major role in the increases in ozone between 2019 and 2020; however, the reduction in NOX emissions also caused ozone increases in Los Angeles County and into western San Bernardino County, with more widespread ozone decreases further to the east. [ABSTRACT FROM AUTHOR]

As air quality sensors increasingly become commercially available, a deeper consideration of their usability and usefulness is needed to ensure effective application by the public. Much of the research related to sensors has focused on data quality and potential applications. While this information is important, a greater understanding of users' experience with sensors would provide complementary information. Under a U.S. EPA-funded Science to Achieve Results grant awarded to the South Coast Air Quality Management District in California, titled "Engage, Educate, and Empower California Communities on the Use and Applications of Low-Cost Air Monitoring Sensors", approximately 400 air quality sensors were deployed with 14 California communities. These communities received sensors and training, and they participated in workshops. Widely varying levels of sensor installation and engagement were observed across the 14 communities. However, despite differences between communities (in terms of participation, demographics, and socioeconomic factors), many participants offered similar feedback on the barriers to sensor use and strategies leading to successful sensor use. Here, we assess sensor use and participant feedback, as well as discuss the development of an educational toolkit titled "Community in Action: A Comprehensive Toolkit on Air Quality Sensors". This toolkit can be leveraged by future community and citizen science projects to develop networks designed to collect air quality information that can help reduce exposure to and the emissions of pollutants, leading to improved environmental and public health. [ABSTRACT FROM AUTHOR]

Particulate matter (PM) represents an air quality management challenge for confined swine production systems. Due to the limited space and ventilation rate, PM can reach relatively high concentrations in swine barns. PM in swine barns possesses different physical, chemical, and biological characteristics than that in the atmosphere and other indoor environments. As a result, it exerts different environmental and health effects and creates some unique challenges regarding PM measurement and mitigation. Numerous research efforts have been made, generating massive data and information. However, relevant review reports are sporadic. This study aims to provide an updated comprehensive review of swine barn PM, focusing on publications since 1990. It covers various topics including PM characteristics, sources, measurement methods, and in-barn mitigation technologies. As PM in swine barns is primarily of biological origins, bioaerosols are reviewed in great detail. Relevant topics include bacterial/fungal counts, viruses, microbial community composition, antibiotic-resistant bacteria, antibiotic resistance genes, endotoxins, and (1→3)-β-D-glucans. For each topic, existing knowledge is summarized and discussed and knowledge gaps are identified. Overall, PM in swine barns is complicated in chemical and biological composition and highly variable in mass concentrations, size, and microbial abundance. Feed, feces, and skins constitute the major PM sources. Regarding in-barn PM mitigation, four technologies (oil/water sprinkling, ionization, alternation of feed and feeders, and recirculating air filtration) are dominant. However, none of them have been widely used in commercial barns. A collective discussion of major knowledge gaps and future research needs is offered at the end of the report. [ABSTRACT FROM AUTHOR]

Worldwide increasing awareness of energy sustainability issues has been the main driver in developing the concepts of (Nearly) Zero Energy Buildings, where the reduced energy consumptions are (nearly) fully covered by power locally generated by renewable sources. At the same time, recent advances in Internet of Things technologies are among the main enablers of Smart Homes and Buildings. The transition of conventional buildings into active environments that process, elaborate and react to online measured environmental quantities is being accelerated by the aspects related to COVID-19, most notably in terms of air exchange and the monitoring of the density of occupants. In this paper, we address the problem of maximizing the energy efficiency and comfort perceived by occupants, defined in terms of thermal comfort, visual comfort and air quality. The case study of the University of Pisa is considered as a practical example to show preliminary results of the aggregation of environmental data. [ABSTRACT FROM AUTHOR]

Revealed by the effect of indoor pollutants on the human body, indoor air quality management is increasing. In particular, indoor smoking is one of the common sources of indoor air pollution, and its harmfulness has been well studied. Accordingly, the regulation of indoor smoking is emerging all over the world. Technical approaches are also being carried out to regulate indoor smoking, but research is focused on detection hardware. This study includes analytical and machine learning approach of cigarette detection by detecting typical gases (total volatile organic compounds, CO2 etc.) being collected from IoT sensors. In detail, data set for machine learning was built using IoT sensors, including training data set securely collected from the rotary smoking machine and test data set gained from actual indoor environment with spontaneous smokers. The prediction accuracy was evaluated with accuracy, precision, and recall. As a result, the non-linear support vector machine (SVM) model showed the best performance with 93% in accuracy and 88% in the F1 score. The supervised learning k-nearest neighbors (KNN) and multilayer perceptron (MLP) models also showed relatively fine results, but shows effectivity simplifying prediction with binary classification to improve accuracy and speed. [ABSTRACT FROM AUTHOR]

Many areas of the United States are working toward achieving the 2015 ozone National Ambient Air Quality Standard (NAAQS) attainment level. The objective of this study was to develop future-year (2030) volatile organic compounds and nitrogen oxides (VOC-NOx) isopleth diagrams of the 4th highest maximum daily 8-h average ozone design value concentrations at monitors of interest in the South Coast Air Basin (SoCAB) and San Joaquin Valley (SJV) in California, and in Maryland. The simulation results showed there would be attainment of the 2015 ozone NAAQS in 2030 without further controls at the selected monitors: 27% in SoCAB, 57% in SJV, and 100% in Maryland. The SoCAB ozone isopleths developed in this study were compared with those reported in the South Coast Air Quality Management District 2016 Air Quality Management Plan. There are several differences between the two modeling studies, the results are qualitatively similar for most of the monitors in the relative amounts of additional emission reductions needed to achieve the ozone NAAQS. The results of this study provide insight into designing potential control strategies for ozone attainment in future years for areas currently in non-attainment. Additional photochemical modeling using these strategies can then provide confirmation of the effectiveness of the controls. [ABSTRACT FROM AUTHOR]