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Chemical transport models (CTMs), used for the prediction of, for example, nitrogen deposition or air quality changes, require estimates of the growing season of plants for a number of reasons. Typically, the growing seasons are defined in a very simplified way in CTMs, using e.g. fixed dates or simple functions. In order to explore the importance of more realistic growing season estimates, we have developed a new and simple method (the "T5" method) for calculating the start of the growing season (SGS) of birch (which we use as a surrogate for deciduous trees), suitable for use in CTMs and other modelling systems. We developed the "T5" method from observations, and here we compare with these and other methodologies, and show that with just two parameters "T5" captures well the spatial variation in SGS across Europe. We use the EMEP MSC-W chemical transport model to illustrate the importance of improved SGS estimates for ozone and two metrics associated with ozone-damage to vegetation. This study shows that although inclusion of more realistic growing seasons has only small effects on annual average concentrations of pollutants such as ozone, the metrics associated with vegetation-risk from ozone are significantly affected. This work demonstrates a strong need to include more realistic treatments of growing seasons in CTMs. The method used here could also be suitable for other types of models which require information on vegetation cover, such as meteorological and regional climate models. In future work, the "T5" and other methods will also be further evaluated for use with agricultural and grassland land-covers, which are important for emissions and deposition of reactive nitrogen compounds. [ABSTRACT FROM AUTHOR]

The article presents a study on differences in ozone production from carbon bond gas-phase chemical mechanism versions CB05 and CBMIV. It mentions the creation of the mechanisms' box model as enforced in the National Air Quality Forecasting Capability (NAQFC) modeling system as well as the development of 12 sensitivity simulations. It also notes the emulation of their chemical regime range in a continental-scale atmospheric chemistry model.

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]

An innovative Extended Response Surface Modeling technique (ERSM v1.0) is developed to characterize the nonlinear response of fine particles (PM2.5) to large and simultaneous changes of multiple precursor emissions from multiple regions and sectors. The ERSM technique is developed starting from the conventional Response Surface Modeling (RSM) technique; it first quantifies the relationship between PM2.5 concentrations and precursor emissions in a single region with the conventional RSM technique, and then assesses the effects of inter-regional transport of PM2.5 and its precursors on PM2.5 concentrations in the target region. We apply this novel technique with a widely used regional air quality model over the Yangtze River Delta (YRD) region of China, and evaluate the response of PM2.5 and its inorganic components to the emissions of 36 pollutant-region-sector combinations. The predicted PM2.5 concentrations agree well with independent air quality model simulations; the correlation coefficients are larger than 0.98 and 0.99, and the mean normalized errors are less than 1 and 2% for January and August, respectively. It is also demonstrated that the ERSM technique could reproduce fairly well the response of PM2.5 to continuous changes of precursor emission levels between zero and 150 %. Employing this new technique, we identify the major sources contributing to PM2.5 and its inorganic components in the YRD region. The nonlinearity in the response of PM2.5 to emission changes is characterized and the underlying chemical processes are illustrated. [ABSTRACT FROM AUTHOR]

Simulations of the global dust cycle and its interactions with a changing Earth system are hindered by the empirical nature of dust emission parameterizations in climate models. Here we take a step towards improving global dust cycle simulations by presenting a physically-based dust emission model. The resulting dust flux parameterization depends only on the wind friction speed and the soil's threshold friction speed, and can therefore be readily implemented into climate models. We show that our parameterization's functional form is supported by a compilation of quality-controlled vertical dust flux measurements, and that it better reproduces these measurements than existing parameterizations. Both our theory and measurements indicate that many climate models underestimate the dust flux's sensitivity to soil erodibility. This finding can explain why dust cycle simulations in many models are improved by using an empirical preferential sources function that shifts dust emissions towards the most erodible regions. In fact, implementing our parameterization in a climate model produces even better agreement against aerosol optical depth measurements than simulations that use such a source function. These results indicate that the need to use a source function is at least partially eliminated by the additional physics accounted for by our parameterization. Since soil erodibility is affected by climate changes, our results further suggest that many models have underestimated the climate sensitivity of the global dust cycle. [ABSTRACT FROM AUTHOR]

Elevated levels of fine particulate matter (PM2.5) in the atmosphere present substantial risks to human health and welfare. The accurate assessment of PM2.5 concentrations plays a pivotal role in facilitating prompt responses by pertinent regulatory bodies to mitigate air pollution. Additionally, it furnishes indispensable information for epidemiological studies concentrating on PM2.5 exposure. In recent years, predictive models based on deep learning (DL) have offered promise in improving the accuracy and efficiency of air quality forecasts when compared to other approaches. Long short-term memory (LSTM) networks have proven to be effective in time series forecasting tasks, including air pollution prediction. However, optimizing LSTM models for enhanced accuracy and efficiency remains an ongoing research area. In this paper, we propose a novel approach that integrates the novel binary chimp optimization algorithm (BChOA) with LSTM networks to optimize air pollution prediction models. The proposed BChOA, inspired by the social behavior of chimpanzees, provides a powerful optimization technique to fine-tune the LSTM architecture and optimize its parameters. The evaluation of the results is performed using cross-validation methods such as the coefficient of determination ( R 2 ), accuracy, the root mean square error (RMSE), and receiver operating characteristic (ROC) curve. Additionally, the performance of the BChOA-LSTM model is compared against eight DL architectures. Experimental evaluations using real-world air pollution data demonstrate the superior performance of the proposed BChOA-based LSTM model compared to traditional LSTM models and other optimization algorithms. The BChOA-LSTM model achieved the highest accuracy of 96.41% on the validation datasets, making it the most successful approach. The results show that the BChOA-LSTM architecture performs better than the other architectures in terms of the   R 2 convergence curve, RMSE, and accuracy. [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]

With the rapid development of the real estate industry in China, urban air quality is inevitably affected. By using data of Chinese cities located in the Yangtze River Delta from 2009 to 2018, this paper is aim at examining the casual impact of house prices on air quality. By considering the endogenous problems, in this study, first, the birth rate of the previous six years and the area of state-owned land supply per capita in the previous year are taken as instrumental variables of house price. Second, the two-stage least squares method is used to assess the causal impact of house price on air quality. The findings of this study show that there is no significant relationship between urban house prices and air quality, and these results are obtained without considering the endogenous problem. While, after considering the endogenous problem, urban house prices showed a significant negative effect on overall air quality, especially for low administrative level cities. The increase in house prices has hindered the improvement of air quality mainly through the blind-scale expansion of real estate development investments and the inhibitory effect of innovation. [ABSTRACT FROM AUTHOR]

Although green technology innovation is an important means to balance the environment and economy, few studies have analyzed the employment effects of green technology innovation in developing countries. Therefore, this paper employs the panel data of 286 prefecture-level cities in China from 2006 to 2017 to empirically test the impact of various green technology innovations on urban employment. Meanwhile, from the perspectives of air quality and industrial structure optimization, the influencing mechanism of green technology innovation on employment is deeply analyzed. The results reveal that all kinds of green technology innovation can significantly increase the employment level of the city, and the promotion effect is strongest in the 2nd year after the patent application. According to the results of heterogeneity analysis, key cities of environmental protection and non-resource cities are more likely to generate positive employment effects through green technology innovation. Finally, the mediation effect test model confirms that green technology innovation can promote employment by improving air quality and causing industrial structure optimization. [ABSTRACT FROM AUTHOR]

Purpose: This paper examines the asymmetry between the air protection policies of the central government and local authorities in Poland, using the city of Kraków as a case study. Methodology: The study employs a comparative method to contrast the Polish state's coal policy with the air protection efforts of the Kraków local government. A systems analysis approach is used to identify the causes and effects of the divergent policies. Results: The analysis reveals a lack of alignment between the central government's focus on maintaining coal extraction and the local government's efforts to reduce air pollution. Kraków authorities have taken significant measures to improve air quality, but face challenges due to insufficient support from the central government and neighboring municipalities. Theoretical contribution: The paper highlights the importance of policy symmetry and coordination between different levels of government in addressing complex environmental issues like air pollution. It contributes to understanding multi-level governance dynamics in the context of air protection. Practical implications: The findings underscore the need for a collaborative model of air protection policy that engages central authorities, local governments, private sector entities, and civil society. Establishing air protection councils at the voivodship, county and municipal levels is proposed as a mechanism to facilitate this coordination. Integrating air quality considerations into local governments' management control systems is also recommended. [ABSTRACT FROM AUTHOR]

The influence of machine learning technologies is rapidly increasing and penetrating almost in every field, and air pollution prediction is not being excluded from those fields. This paper covers the revision of the studies related to air pollution prediction using machine learning algorithms based on sensor data in the context of smart cities. Using the most popular databases and executing the corresponding filtration, the most relevant papers were selected. After thorough reviewing those papers, the main features were extracted, which served as a base to link and compare them to each other. As a result, we can conclude that: (1) instead of using simple machine learning techniques, currently, the authors apply advanced and sophisticated techniques, (2) China was the leading country in terms of a case study, (3) Particulate matter with diameter equal to 2.5 micrometers was the main prediction target, (4) in 41% of the publications the authors carried out the prediction for the next day, (5) 66% of the studies used data had an hourly rate, (6) 49% of the papers used open data and since 2016 it had a tendency to increase, and (7) for efficient air quality prediction it is important to consider the external factors such as weather conditions, spatial characteristics, and temporal features. [ABSTRACT FROM AUTHOR]

This paper analyzes and monitors an important environmental factor, namely air. This paper checks the air quality with the help of DSM501A sensors in the Jiu Valley. The device used in the paper uses the "DSM501A" dust sensor, which is one of the cheapest sensors on the market and is capable of detecting PM2.5 and PM10 particles. [ABSTRACT FROM AUTHOR]

Smart farming system is an emerging sector using IoT sensors provide information about farming areas and then respond based on the user input. We proposed in this paper to create an advanced agriculture system, which takes the advantages of state-of-the-art technology like Adafruit broker, IoT, and the Wireless Sensor Network. The paper focuses on making use of extensive technological i.e., IoT and smart farming system. Monitoring environmental conditions are the major factor to improve the production of efficient crops. IoT devices and communication methods related to wireless sensors encountered in farming applications are studied in detail. The system proposed in this paper uses ESP8266 NodeMCU, DHT11 Temperature, and Humidity Sensor, Soil Moisture Sensor, PIR sensors, mq135 air quality sensor, Jumper wires, LEDs bulb, exhaust fan, cooling fan, and live data feed can be monitored on serial monitor and Adafruit MQTT protocol platforms. This will allow the farmer to manage their crop easily and efficiently.. [ABSTRACT FROM AUTHOR]

This paper explores the enduring impact of freeway construction on urban neighborhoods and communities, focusing on Los Angeles. The legacy of freeways in Los Angeles, often and purposefully constructed through marginalized communities during the mid-20th century for the benefit of white suburbanites, is reflected in significant social, economic, and environmental symptoms today. This paper also examines de-freeway methods to reconnect these divided communities, assessing their need and effectiveness through data analysis and case studies.The case studies presented in this paper offer a diverse range of solutions. They include the capping of I-5 for Freeway Park in Seattle, the tunneling of the Central Artery for Boston's Big Dig, the activation of I-5 underpasses for Chicano Park in San Diego, and the creation of the Ricardo Lara embankment park along the I-105 freeway in Lynnwood. These examples provide valuable insights into the processes, challenges, and successes of de-freeway and community reconnection efforts, showcasing the adaptability of these solutions in different urban contexts.The paper introduces three design proposals for freeway sites across six communities in Los Angeles. These proposals are guided by urban reformation around freeways and incorporating urban design principles prioritizing social equity, environmental sustainability, and economic revitalization for the most vulnerable and impacted communities. The proposed designs aim to restore community cohesion and foster resilient urban ecosystems.This study contributes to the ongoing discourse on sustainable urban transformation and the reclamation of public spaces for community benefit by providing a comprehensive analysis of historical impacts and contemporary solutions. The paper's findings should help inform and guide urban planning and policy advocates and professionals inspired by the reconnecting communities movement, underscoring the need for innovative approaches to mending bifurcated and ecologically disadvantaged communities.

The atmospheric pollutants and greenhouse gases emitted by ships have a significant impact on the air quality of the cities around the port and the physical and mental health of the residents. In order to promote the low-carbon, green, and sustainable development of the region, it is urgent to conduct comprehensive research and control the air pollution emissions from ships in the region. In this paper, the traditional power-based emission inventory calculation model is improved through field tests, and the engine propeller matching coefficient is proposed. Combined with the actual situation of local ships, the parameters suitable for the air pollution emission inventory of ships in the region are comprehensively selected. In the case of statistical comparison of the air pollutant emissions of the main and auxiliary engines under different navigation conditions, the uncertainty analysis was carried out, and the AIS (Automatic Identification System) combined with the power method was used to obtain the air pollution emission inventory of ships in the main urban area of Chongqing. The research in this paper can improve the calculation model of the power method emission inventory according to the situation of ships in the inland river area, which provides a reference for the development and improvement of the emission inventory in the inland river area, and also provides suggestions and thinking for the government to formulate energy saving and emission reduction measures in the inland river area. [ABSTRACT FROM AUTHOR]

Atmospheric chemistry plays a key role in determining the amounts and distributions of oxidants and gaseous precursors that control the formation of secondary gaseous and aerosol pollutants; all of those species can interact with the climate system. To understand the impacts of different gas-phase mechanisms on global air quality and climate predictions, in this work, a comprehensive comparative evaluation is performed using the Community Atmosphere Model (CAM) Version 5 with comprehensive tropospheric and stratospheric chemistry (CAM5-chem) within the Community Earth System Model (CESM) with two most commonly-used gas-phase chemical mechanisms: the 2005 Carbon Bond mechanism with Global Extension (CB05-GE) and the Model of OZone and Related chemical Tracers version 4 (MOZART-4) mechanism with additional updates (MOZART-4x). MOZART-4x and CB05-GE use different approaches to represent volatile organic compounds (VOCs) and different surrogates for secondary organic aerosol (SOA) precursors. MOZART-4x includes a more detailed representation of isoprene chemistry compared to CB05-GE. CB05-GE includes additional oxidation of SO2 by O3 over the surface of dust particles, which is not included in MOZART-4x. The results show that the two CAM5-chem simulations with CB05-GE and MOZART-4x predict similar chemical profiles for major gases (e.g., O3, CO, and NOx) compared to the aircraft measurements, with generally better agreement for NOy profile by CB05-GE than MOZART-4x. The concentrations of SOA at four sites in CONUS and organic carbon (OC) over the IMPROVE sites are well predicted by MOZART-4x (with NMBs of -1.9 and 2.1 %, respectively) but moderately underpredicted by CB05-GE (with NMBs of -23.1 and -20.7 %, respectively). This is mainly due to the higher biogenic emissions and hydroxyl radical levels simulated with MOZART-4x than with CB05-GE. The concentrations of OC over Europe are largely underpredicted by both MOZART-4x and CB05-GE, with NMBs of -73.0 and -75.1 %, respectively, indicating the uncertainties in the emissions of precursors and primary OC and relevant model treatments such as the oxidations of VOCs and SOA formation. Uncertainties in the emissions and convection scheme can contribute to the large bias in the model predictions (e.g., SO2, CO, black carbon, and aerosol optical depth). The two simulations also have similar cloud/radiative predictions, with slightly better performance of domain average cloud condensation nuclei (CCN) at supersaturation of 0.5% by CB05-GE, but slightly better 5 agreement with observed CCN (at supersaturation of 0.2%) profile over Beijing by MOZART-4x. The two gas-phase mechanisms result in a global average difference of 0.5Wm-2 in simulated shortwave cloud radiative forcing, with significant differences (e.g., up to 13.6Wm-2) over subtropical regions [ABSTRACT FROM AUTHOR]

In this study we develop a Secondary Inorganic Aerosol (SIA) module for the chemistry transport model MOCAGE developed at CNRM. Based on the thermodynamic equilibrium module ISORROPIA II, the new version of the model is evaluated both at the global scale and at the regional scale. The results show high concentrations of secondary inorganic aerosols in the most polluted regions being Europe, Asia and the eastern part of North America. Asia shows higher sulfate concentrations than other regions thanks to emissions reduction in Europe and North America. Using two simulations, one with and the other without secondary inorganic aerosol formation, the model global outputs are compared to previous studies, to MODIS AOD retrievals, and also to in situ measurements from the HTAP database. The model shows a better agreement in all geographical regions with MODIS AOD retrievals when introducing SIA. It also provides a good statistical agreement with in situ measurements of secondary inorganic aerosol composition: sulfate, nitrate and ammonium. In addition, the simulation with SIA gives generally a better agreement for secondary inorganic aerosols precursors (nitric acid, sulfur dioxide, ammonia) in particular with a reduction of the Modified Normalised Mean Bias (MNMB). At the regional scale, over Europe, the model simulation with SIA are compared to the in situ measurements from the EMEP database and shows a good agreement with secondary inorganic aerosol composition. The results at the regional scale are consistent with those obtained with the global simulations. The AIRBASE database was used to compare the model to regulated air quality pollutants being particulate matter, ozone and nitrogen dioxide concentrations. The introduction of the SIA in MOCAGE provides a reduction of the PM2.5 MNMB of 0.44 on a yearly basis and even 0.52 on a three spring months period (March, April, May) when SIA are maximum. [ABSTRACT FROM AUTHOR]

This document is a response to a letter regarding a recently published paper titled "Effectiveness of Asthma Home Management Manual and Low-Cost Air Filter on Quality of Life Among Asthma Adults: A 3-Arm Randomized Controlled Trial." The authors address concerns raised by the letter writer and explain their choices in the study design and analysis. They acknowledge that some keywords may not strictly adhere to MeSH guidelines but defend their inclusion based on their relevance to the research. The authors also discuss the impact of asthma duration on the study results and clarify that certain data were considered baseline rather than outcome measures. The authors report no conflicts of interest. [Extracted from the article]

The Community Multiscale Air Quality (CMAQ) model is a state-of-the-science air quality model that simulates the emission, transformation, transport and fate of the many different air pollutant species that comprise particulate matter (PM), including dust (or soil). The CMAQ model version 5.0 (CMAQv5.0) has several enhancements over the previous version of the model for estimating the emission and transport of dust, including the ability to track the specific elemental constituents of dust and have the modelderived concentrations of those elements participate in chemistry. The latest version of the model also includes a parameterization to estimate emissions of dust due to wind action. The CMAQv5.0 modeling system was used to simulate the entire year 2006 for the continental United States, and the model estimates were evaluated against daily surface based measurements from several air quality networks. The CMAQ modeling system generally did well replicating the observed soil concentrations in the western United States; however the model consistently overestimated the observed soil con centrations in the eastern United States, regardless of season. The performance of the individual trace metals was generally good at the rural network sites, with relatively small biases for Fe, Al, Si and Ti throughout the year, while Ca, K and Mn were overestimated and Mg underestimated. For the urban sites, Fe, Mg and Mn, while overestimated, had comparatively better performance throughout the year than the other trace metals, which were consistently overestimated, including very large overestimations of Al, Ti and Si in the fall. An underestimation of nighttime mixing in the urban areas appears to contribute to the overestimation of trace metals. Removing the anthropogenic fugitive dust (AFD) emissions and the effects of wind-blown dust (WBD) lowered the model soil concentrations. However, even with both AFD emissions and WBD effects removed, soil concentrations were still often overestimated, suggesting that there are other sources of errors in the modeling system that contribute to the overestimation of soil components. Efforts are underway to improve both the nighttime mixing in urban areas and the spatial and temporal distribution of dust related emissions sources in the emissions inventory. [ABSTRACT FROM AUTHOR]

The article discusses a research study which introduces the Market Allocation (MARKAL) method for anthropogenic emission scenarios. The said approach focuses on energy use and production because these factors are the main sources of air pollution and uses two emission scenarios. Findings reveal that both scenarios demonstrate significant reductions in air pollutant emissions.

The article offers information on the modification of the U.S. Environmental Protection Agency (EPA) regional emission model known as SMOKE for Europe (SMOKE-EU). It mentions that SMOKE-EU is capable of producing Community Multiscale Air Quality (CMAQ) ready emissions not only in Europe but also in Western Russia, Turkey and North Africa. The purpose of the modification is to develop a flexible model capable of producing high resolution emission data for Chemical Transport Models (CTM).

The significance of air quality monitoring for analyzing impact on public health is growing worldwide. A crucial part of smart city development includes deployment of suitable air pollution sensors at critical locations. Note that there are various air quality measurement instruments, ranging from expensive reference stations that provide accurate data to low-cost sensors that provide less accurate air quality measurements. In this research, we use a combination of sensors and monitors, which we call hybrid instruments, and focus on optimal placement of such instruments across a region. The objective of the problem is to maximize a satisfaction function that quantifies the weighted closeness of different regions to the places where such hybrid instruments are placed (here weights for different regions are quantified in terms of the relative population density and relative PM 2.5 concentration). Note that there can be several constraints such as those on budget, the minimum number of reference stations to be placed, or the set of important regions where at least one sensor should be placed. We develop two algorithms to solve this problem. The first one is a genetic algorithm that is a metaheuristic and that works on the principles of evolution. The second one is a greedy algorithm that selects the locally best choice in each iteration. We test these algorithms on different regions from India with varying sizes and other characteristics such as population distribution, PM 2.5 emissions, or available budget. The insights obtained from this paper can be used to quantitatively place reference stations and sensors in large cities rather than using ad hoc procedures or rules of thumb. [ABSTRACT FROM AUTHOR]

This paper investigates the Indoor Environmental Quality (IEQ) factors influencing occupant satisfaction in commercial buildings in Iran, contrasting the views of building experts (architects and engineers) with those of building occupants. Employing the fuzzy analytical hierarchy process (FAHP), this study focuses on the four primary IEQ factors: thermal comfort, indoor air quality, visual comfort, and acoustic comfort. The study aims to bridge the gap between expert evaluations and occupant perceptions of IEQ factors in commercial buildings in Iran. By examining the disparities in prioritising IEQ factors between these two groups, the study sheds light on the complexities of IEQ assessment and highlights the importance of considering diverse perspectives in optimising indoor environments. Our methodology includes a survey conducted among 30 building experts (15 architects and 15 building engineers) and 102 occupants, employing FAHP to derive the relative importance weights of each IEQ factor. The results highlight significant disparities between architects, engineers, and occupants in prioritising these factors. Architects emphasise visual comfort (42%), while engineers and occupants view thermal comfort (53% and 41%) as the most crucial factor for occupant satisfaction. The study underscores the complexity of IEQ in commercial buildings and the diverse perspectives influencing its assessment. It contributes to the broader discourse on optimising IEQ, emphasising the need for a comprehensive approach that encompasses both technical expertise and occupant experience. [ABSTRACT FROM AUTHOR]

Nowadays, the study of air quality has become an increasingly prominent field of research, particularly in large urban centers, given its significant impact on human health. In many countries, government departments and research centers use official high-cost scientific instruments to monitor air quality in their regions. Meanwhile, concerned citizens interested in studying the air quality of their local areas often employ low-cost air quality sensors for monitoring purposes. The optimization and evaluation of low-cost sensors have been a field of research by many research groups. This paper presents an extensive study to identify the safe percentage change limits that low-cost electrochemical air quality sensors can have, in order to optimize their measurements. For this work, three low-cost air quality monitoring stations were used, which include an electrochemical sensor for nitrogen dioxide (NO2) (Alphasense NO2-B43F) and an electrochemical sensor for ozone (O3) (Alphasense OX-B431). The aim of this work is to explore the variance of the aforementioned sensors and how this variability can be used to optimize the measurements of low-cost electrochemical sensors, closer to real ones. The analysis is conducted by employing diagrams, boxplot and violin curves of the groups of sensors used, with satisfactory results. [ABSTRACT FROM AUTHOR]

In this paper, multi-source observation, such as aircraft, ground-based remote sensing, and satellite-retrieved data, has been utilized to compare and analyze the vertical characteristics of aerosol optical properties and the planetary boundary layer height (HPBL) over the North China Plain (NCP) region during May–June 2016. Aircraft observations show the vertical profiles of aerosol absorption coefficients (σabs), scattering coefficients (σsca), and extinction coefficients (σext) gradually decrease with altitude, with their maximum values near HPBL. The vertical profiles of σext depended most on the vertical distribution of measured σsca, indicating a significant contribution of scattering aerosols. In addition, the prominent characteristic of the inverse relationship between σext and moisture profile could serve as a reference for predicting air quality in the NCP region. The lower layer pollution during the field experiment was likely caused by the accumulation of fine-mode aerosols, characterized by the vertical distribution of the Ångström exponent and the Aerosol Robotic Network (AERONET) products. Typically, HPBL derived from aircraft and surface Micro Pulse Lidar (MPL) was approximate, while the predicted HPBL by meteorological data indicates an underestimation of ~192 m. Aerosol optical depth (AOD) calculated from aircraft and ground-based remote sensing (such as MPL and AERONET) experienced a strong correlation, and both of them exhibited a similar tendency. However, the AOD retrieved from satellites was significantly larger than that from aircraft and ground-based remote sensing. Overall, the inversion algorithm, cloud identification algorithm, representativeness of the space, and time of the observation may lead to an overestimation or underestimation of AOD under certain circumstances. This study may serve as a re-evaluation of AOD retrieved from multi-source observations and provide a reference to uncover the actual atmospheric environment in the NCP regions. [ABSTRACT FROM AUTHOR]

Trajectory ensemble receptor models (TERMs) were widely used to determine the likely source locations and apportionment of air pollutants. This paper describes the development and applications of the Trajectory-ensemble Potential Source Apportionment Web application (TraPSA-Web), a comprehensive toolkit for likely atmospheric pollutant source location apportionments using TERMs and back trajectories generated with the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model. The TERMs integrated within the TraPSA-web include Concentration Field Analysis (CFA), Concentration Weighted Trajectory (CWT), single-site and multiple-site Potential Source Contribution Function (PSCF), and Simplified Quantitative Transport Bias Analysis (SQBA). TraPSA-Web is designed as a web application with a user-friendly modern graphical user interface (GUI), which largely enhances the accessibility to the users. TraPSA-Web will provide the air quality research community with a sophisticated toolkit for (1) easy management of the research project and datasets, (2) efficient automatization for HYSPLIT configurations, calculations, and result aggregations, (3) flexible configurations for the research scenarios and TERM parameters, and (4) interactive visualizations for the pollutant pattern analysis and TERM result mapping. [ABSTRACT FROM AUTHOR]

Over the last few years, there has been a noticeable increase in interest in air quality issues in Poland. Information on this subject is increasingly appearing in the media and in public debate. However, it seems that awareness of the existence and importance of this problem is still too low in our society. In particular, few people have sufficient knowledge of the impact of air pollutants on health. Consequently, the problem of air pollution is very often underestimated. The increase in risk of occurrence of specific adverse health effects associated with exposure to air pollutants is usually relatively low (except in cases of high pollutant concentrations or people in high-risk groups). However, the prevalence of exposure as well as the association with common diseases result in the fact that the impact of air pollutants on health status of the population is significant. This paper attempts to analyse the relationship between ambient air quality and the number of people who visited a local health care facility and were diagnosed with respiratory and cardiovascular diseases. The analysis was performed based on the town of Żory located in the Silesian Voivodeship in southern Poland. Air pollutant concentrations of PM2.5, PM10 and SO2 for the summer and heating seasons were obtained from the database of the monitoring station owned by the Chief Inspectorate for Environmental Protection. Information on the number of people who visited a physician on particular days and who were diagnosed with respiratory or cardiovascular diseases was obtained from the local health care facility "Medyk" in Żory. The analysis was conducted for the summer period from 01.06.2021 to 31.08.2021 and the heating season from 01.11.2021 to 15.01.2022. The analysis showed that the increased concentrations of particulate pollution did not result in an increase in the number of people who visited a physician with respiratory diseases. In the case of the heating period, incidents of above-normal concentrations of PM10 particulate matter may have had a noticeable impact on the increase in the number of patients who visited a physician with cardiovascular diseases. This increase in the number of patients occurred several days after the smog incidents which occurred in mid-December. However, no short-term link was observed between the increase in the number of patients with cardiovascular and respiratory diseases and the occurrence of elevated concentrations of sulphur dioxide in the air. Furthermore, higher concentrations of particulate and gaseous pollutants during the heating season resulted in more respiratory and cardiovascular diseases than during the summer season. [ABSTRACT FROM AUTHOR]

As air pollution is regarded as the single largest environmental health risk in Europe it is important that communication to the public is up to date and accurate and provides means to avoid exposure to high air pollution levels. Long- and short-term exposure to outdoor air pollution is associated with increased risks of mortality and morbidity. Up-to-date information on present and coming days' air quality helps people avoid exposure during episodes with high levels of air pollution. Air quality forecasts can be based on deterministic dispersion modelling, but to be accurate this requires detailed information on future emissions, meteorological conditions and process-oriented dispersion modelling. In this paper, we apply different machine learning (ML) algorithms – random forest (RF), extreme gradient boosting (XGB), and long short-term memory (LSTM) – to improve 1, 2, and 3 d deterministic forecasts of PM 10 , NOx , and O 3 at different sites in Greater Stockholm, Sweden. It is shown that the deterministic forecasts can be significantly improved using the ML models but that the degree of improvement of the deterministic forecasts depends more on pollutant and site than on what ML algorithm is applied. Also, four feature importance methods, namely the mean decrease in impurity (MDI) method, permutation method, gradient-based method, and Shapley additive explanations (SHAP) method, are utilized to identify significant features that are common and robust across all models and methods for a pollutant. Deterministic forecasts of PM 10 are improved by the ML models through the input of lagged measurements and Julian day partly reflecting seasonal variations not properly parameterized in the deterministic forecasts. A systematic discrepancy by the deterministic forecasts in the diurnal cycle of NOx is removed by the ML models considering lagged measurements and calendar data like hour and weekday, reflecting the influence of local traffic emissions. For O 3 at the urban background site, the local photochemistry is not properly accounted for by the relatively coarse Copernicus Atmosphere Monitoring Service ensemble model (CAMS) used here for forecasting O 3 but is compensated for using the ML models by taking lagged measurements into account. Through multiple repetitions of the training process, the resulting ML models achieved improvements for all sites and pollutants. For NOx at street canyon sites, mean squared error (MSE) decreased by up to 60 %, and seven metrics, such as R2 and mean absolute percentage error (MAPE), exhibited consistent results. The prediction of PM 10 is improved significantly at the urban background site, whereas the ML models at street sites have difficulty capturing more information. The prediction accuracy of O 3 also modestly increased, with differences between metrics. Further work is needed to reduce deviations between model results and measurements for short periods with relatively high concentrations (peaks) at the street canyon sites. Such peaks can be due to a combination of non-typical emissions and unfavourable meteorological conditions, which are rather difficult to forecast. Furthermore, we show that general models trained using data from selected street sites can improve the deterministic forecasts of NOx at the station not involved in model training. For PM 10 this was only possible using more complex LSTM models. An important aspect to consider when choosing ML algorithms is the computational requirements for training the models in the deployment of the system. Tree-based models (RF and XGB) require fewer computational resources and yield comparable performance in comparison to LSTM. Therefore, tree-based models are now implemented operationally in the forecasts of air pollution and health risks in Stockholm. Nevertheless, there is big potential to develop generic models using advanced ML to take into account not only local temporal variation but also spatial variation at different stations. [ABSTRACT FROM AUTHOR]

Hybrid approaches combining machine learning with traditional inverse problem solution methods represent a promising direction for the further development of inverse modeling algorithms. The paper proposes an approach to emission source identification from measurement data for advection–diffusion–reaction models. The approach combines general-type source identification and post-processing refinement: first, emission source identification by measurement data is carried out by a sensitivity operator-based algorithm, and then refinement is done by incorporating a priori information about unknown sources. A general-type distributed emission source identified at the first stage is transformed into a localized source consisting of multiple point-wise sources. The second, refinement stage consists of two steps: point-wise source localization and emission rate estimation. Emission source localization is carried out using deep learning with convolutional neural networks. Training samples are generated using a sensitivity operator obtained at the source identification stage. The algorithm was tested in regional remote sensing emission source identification scenarios for the Lake Baikal region and was able to refine the emission source reconstruction results. Hence, the aggregates used in traditional inverse problem solution algorithms can be successfully applied within machine learning frameworks to produce hybrid algorithms. [ABSTRACT FROM AUTHOR]

The profound impacts of severe air pollution on human health, ecological balance, and economic stability are undeniable. Precise air quality forecasting stands as a crucial necessity, enabling governmental bodies and vulnerable communities to proactively take essential measures to reduce exposure to detrimental pollutants. Previous research has primarily focused on predicting air quality using only time-series data. However, the importance of remote-sensing image data has received limited attention. This paper proposes a new multi-modal deep-learning model, Res-GCN, which integrates high spatial resolution remote-sensing images and time-series air quality data from multiple stations to forecast future air quality. Res-GCN employs two deep-learning networks, one utilizing the residual network to extract hidden visual information from remote-sensing images, and another using a dynamic spatio-temporal graph convolution network to capture spatio-temporal information from time-series data. By extracting features from two different modalities, improved predictive performance can be achieved. To demonstrate the effectiveness of the proposed model, experiments were conducted on two real-world datasets. The results show that the Res-GCN model effectively extracts multi-modal features, significantly enhancing the accuracy of multi-step predictions. Compared to the best-performing baseline model, the multi-step prediction's mean absolute error, root mean square error, and mean absolute percentage error increased by approximately 6%, 7%, and 7%, respectively. [ABSTRACT FROM AUTHOR]

This paper highlights the advantages of an affordable multi-wavelength ground-based camera, called WaltRCam, for monitoring Aerosol Optical Depth (AOD) in a clear case over a peri-urban area. To simulate the performance of this low-cost camera, for which data are not yet available, we use data from an expensive hyperspectral camera (HSI) to mimic its characteristics. Our methodology is based on the construction of look-up tables using the DART (Discrete Anisotropic Radiative Transfer) 3D radiative transfer model. DART simulates the different spectra observed by the WaltRCam camera, which then provides the AODs for all image pixels in near-real-time. Moreover, DART is coupled to a 3D scale-model of the city of Toulouse (dating from 2014) to model complex urban geometries and to associate specific optical properties to the various objects that make up the environment. Moreover, we use a neural-network-based method to recognize the various objects in the image in order to take into account only pixels common between the observations. In this way, we take account of changes to the peri-urban area, such as vegetation growth, construction, demolition of buildings, etc. The results of this study show that the WaltRCam camera, by capturing eight wavelengths, can deliver convincing results compared with ground and satellite reference data, with a correlation coefficient of 0.9 and an average RMSE of less than 0.02. [ABSTRACT FROM AUTHOR]

Declining air quality is highly ignored and very common form of degradation of the environment in nations that are both developed and developing. There are several contaminants in the air that have been identified in various studies on air pollution. The crucial parameters of all air pollutants are gaseous and particle pollution. The present study was undertaken to estimate the quality of ambient air in Chandrapur district, Maharashtra state of India. In this paper, an effort has been made to study the standing and trend of Sulphur dioxide (SO2), Oxides of Nitrogen (NOx), Carbon Monoxide (CO), Ozone (O3), Ammonia (NH3), Respirable Suspended Particulate Matter i.e.; PM10, PM2.5, toxic pollutants i.e.; lead, arsenic, nickel, benzo [a] pyarene and benzene and hydrocarbons. The results clearly show that, all parameters of ambient air quality monitoring values were some shown slightly below permissible limit. The ambient air quality monitoring data show that the Chandrapur district has substantial air pollution concerns in terms of SO2, NOx, PM, and other air pollutants. However, after comparing these values with NAAQS levels, the yearly averages of these air pollutants have been found to be below than the NAAQS levels. However, without effective mitigation measures, the concentrations of these pollutants will rise at quite alarming rate. [ABSTRACT FROM AUTHOR]

Despite significant improvements in air quality during and after COVID-19 restrictions, haze continued to occur in Zhengzhou afterwards. This paper compares ionic compositions and sources of PM2.5 before (2019), during (2020), and after (2021) the restrictions to explore the reasons for the haze. The average concentration of PM2.5 decreased by 28.5% in 2020 and 27.9% in 2021, respectively, from 102.49 μg m−3 in 2019. The concentration of secondary inorganic aerosols (SIAs) was 51.87 μg m−3 in 2019, which decreased by 3.1% in 2020 and 12.8% in 2021. In contrast, the contributions of SIAs to PM2.5 increased from 50.61% (2019) to 68.6% (2020) and 61.2% (2021). SIAs contributed significantly to PM2.5 levels in 2020–2021. Despite a 22~62% decline in NOx levels in 2020–2021, the increased O3 caused a similar NO3− concentration (20.69~23.00 μg m−3) in 2020–2021 to that (22.93 μg m−3) in 2019, hindering PM2.5 reduction in Zhengzhou. Six PM2.5 sources, including secondary inorganic aerosols, industrial emissions, coal combustion, biomass burning, soil dust, and traffic emissions, were identified by the positive matrix factorization model in 2019–2021. Compared to 2019, the reduction in PM2.5 from the secondary aerosol source in 2020 and 2021 was small, and the contribution of secondary aerosol to PM2.5 increased by 13.32% in 2020 and 12.94% in 2021. In comparison, the primary emissions, including biomass burning, traffic, and dust, were reduced by 29.71% in 2020 and 27.7% in 2021. The results indicated that the secondary production did not significantly contribute to the PM2.5 decrease during and after the COVID-19 restrictions. Therefore, it is essential to understand the formation of secondary aerosols under high O3 and low precursor gases to mitigate air pollution in the future. [ABSTRACT FROM AUTHOR]

Accurate and fine-grained individual air quality index (IAQI) prediction is the basis of air quality index (AQI), which is of great significance for air quality control and human health. Traditional approaches, such as time series, recurrent neural network or graph convolutional network, cannot effectively integrate spatial-temporal and meteorological factors and manage the dynamic edge relationship among scattered monitoring stations. In this paper, a ST-CCN-IAQI model is proposed based on spatial-temporal causal convolution networks. Both the spatial effects of multi-source air pollutants and meteorological factors were considered via spatial attention mechanism. Time-dependent features in the causal convolution network were extracted by stacked dilated convolution and time attention. All the hyper-parameters in ST-CCN-IAQI were tuned by Bayesian optimization. Shanghai air monitoring station data were employed with a series of baselines (AR, MA, ARMA, ANN, SVR, GRU, LSTM and ST-GCN). Final results showed that: (1) For a single station, the RMSE and MAE values of ST-CCN-IAQI were 9.873 and 7.469, decreasing by 24.95% and 16.87% on average, respectively. R2 was 0.917, with an average 5.69% improvement; (2) For all nine stations, the mean RMSE and MAE of ST-CCN-IAQI were 9.849 and 7.527, respectively, and the R2 value was 0.906. (3) Shapley analysis showed PM10, humidity and NO2 were the most influencing factors in ST-CCN-IAQI. The Friedman test, under different resampling, further confirmed the advantage of ST-CCN-IAQI. The ST-CCN-IAQI provides a promising direction for fine-grained IAQI prediction. [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]

This paper assesses the impact of transport infrastructure quality on economic competitiveness in the European Union (EU), context in which we argue that the quality of the different modal transport networks has a differentiated contribution on competitiveness. The purpose of our analysis aims to quantify the qualitative contribution of each modal transport network to economic competitiveness in the EU. The econometric quantification of the mentioned impact emphasizes a contributory hierarchy as follows: the quality of port and railroad infrastructure contributes most substantially to economic competitiveness, followed by the quality of the air transport infrastructure, the inland waterways and the road network. Based on the iterated results, we sequenced the EU member states with the lowest quality of networks with the highest impact on competitiveness. Thus, we identified the states for which the priority improvement of the quality of the port and railway infrastructures would significantly improve their economic competitiveness. [ABSTRACT FROM AUTHOR]

Although numerous spatiotemporal approaches have been presented to address the problem of missing spatiotemporal data, there are still limitations in concurrently capturing the underlying spatiotemporal dependence of spatiotemporal graph data. Furthermore, most imputation methods miss the hidden dynamic connection associations that exist between graph nodes over time. To address the aforementioned spatiotemporal data imputation challenge, we present an attention-based message passing and dynamic graph convolution network (ADGCN). Specifically, this paper uses attention mechanisms to unify temporal and spatial continuity and aggregate node neighbor information in multiple directions. Furthermore, a dynamic graph convolution module is designed to capture constantly changing spatial correlations in sensors utilizing a new dynamic graph generation method with gating to transmit node information. Extensive imputation tests in the air quality and traffic flow domains were carried out on four real missing data sets. Experiments show that the ADGCN outperforms the state-of-the-art baseline. [ABSTRACT FROM AUTHOR]

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From the ensemble of stations that monitor surface air quality over the United States and Europe, we identify extreme ozone pollution events and find that they occur predominantly in clustered, multi-day episodes with spatial extents of more than 1000 km. Such scales are amenable to forecasting with current global atmospheric chemistry models. We develop an objective mapping algorithm that uses the heterogeneous observations of the individual surface sites to calculate surface ozone averaged over 1° by 1° grid cells, matching the resolution of a global model. Air quality extreme (AQX) events are identified locally as statistical extremes of the ozone climatology and not as air quality exceedances. With the University of California, Irvine chemistry-transport model (CTM) we find there is skill in hindcasting these extreme episodes, and thus identify a new diagnostic using global chemistry-climate models (CCM) to identify changes in the characteristics of extreme pollution episodes in a warming climate. [ABSTRACT FROM AUTHOR]

Emissions to air are reported by countries to EMEP. The emissions data are used for country compliance checking with EU emission ceilings and associated emission reductions. The emissions data are also necessary as input for air quality modelling. The quality of these "official" emissions varies across Europe. As alternative to these official emissions, a spatially explicit high resolution emission inventory (7km×7 km) for UNECE-Europe for all years between 2003 and 2009 for the main air pollutants was made. The primary goal was to supply air quality modellers with the input they need. The inventory was constructed by using the reported emission national totals by sector where the quality is sufficient. The reported data were analysed by sector in detail, and completed with alternative emission estimates as needed. This resulted in a complete emission inventory for all countries. For particulate matter, for each source emissions have been split in coarse and fine particulate matter, and further disaggregated to EC, OC, SO4, Na and others using fractions based on literature. Doing this at the most detailed sectoral level in the database implies that a consistent set was obtained across Europe. This allows better comparisons with observational data which can, through feedback, help to further identify uncertain sources and/or support emission inventory improvements for this highly uncertain pollutant. The resulting emission dataset was spatially distributed consistently across all countries by using proxy parameters. Point sources were spatially distributed using the specific location of the point source. The spatial distribution for the point sources was made year-specific. The TNO-MACC_II is an update of the TNO-MACC emission dataset. Major updates included the time extension towards 2009, use of the latest available reported data (including updates and corrections made until early 2012) and updates in distribution maps. [ABSTRACT FROM AUTHOR]

Vehicular emissions are one of the most important sources of pollution in China, and they can increase the ambient concentrations of air pollutants and degrade the air quality. Using data released by the National Bureau of Statistics, vehicular emissions in China in 2006 and 2010 were calculated at a high spatial resolution, by taking the emission standards into consideration. The results show that China's vehicular emissions of CO, NOx, VOCs, NH3, PM2.5, PM10, black carbon (BC), and organic carbon (OC) were 30113.9, 4593.7, 6838.0, 20.9, 400.2, 430.5, 285.6, and 105.1 Gg, respectively, in 2006 and 34175.2, 5167.5, 7029.4, 74.0, 386.4, 417.1, 270.9, and 106.2 Gg, respectively, in 2010. CO, VOCs, and NH3 emissions were mainly from motorcycles and light-duty gasoline vehicles, whereas NOx, PM2.5, PM10, and BC emissions were mainly from rural vehicles and heavy-duty diesel trucks. OC emissions were mainly from motorcycles and heavy-duty diesel trucks. Euro 0 and Euro I vehicles were the primary contributors to all of the pollutant emissions except NH3, which was mainly from Euro III and Euro IV vehicles. The spatial distribution of vehicular emissions in China in 2006 and 2010 were developed at a high resolution of 0.25° × 0.25°, by using the road traffic density to characterize the busyness of a road. This method could overcome the problem of getting traffic flow information and make the spatial allocation more closed to the actual road emissions. The results showed that vehicular emissions presented significant regional spatial distribution, and emissions in the eastern and southern parts of China were much higher than those in western and northern China in both years. The North China Plain, Yangtze River Delta, and Pearl River Delta regions jointly accounted for nearly half of the emissions. NH3 emissions increased greatly in big cities from 2006 to 2010. Emissions of CO, NOx, and VOCs could increase 52%, 9%, and 68%, if the emission standard and oil quality remained in the Euro I stage, so the policies on vehicular emissions implemented in China were demonstrated to be effective. Nevertheless, greater efforts are needed to improve the oil quality so that the new emission standard can implement timely and catch the international level quickly, especially the sulfur level because NOx emission is very sensitive to it. By comparing with coal consumption and NO2 column density observed by SCIA satellite, the increase of NOx emission in China from 2006 to 2010 was mainly caused by coal consumption. [ABSTRACT FROM AUTHOR]

Ozone, PM10 and PM2.5 concentrations over Paris, France and Stockholm, Sweden were modeled at 4 and 1 km horizontal resolutions respectively for the present and 2050 periods employing decade-long simulations.We account for large-scale global climate change (RCP-4.5) and fine resolution bottom-up emission projections developed by local experts and quantify their impact on future pollutant concentrations. Moreover, we identify biases related to the implementation of regional scale emission projections over the study areas by comparing modeled pollutant concentrations between the fine and coarse scale simulations. We show that over urban areas with major regional contribution (e.g., the city of Stockholm) the bias due to coarse emission inventory may be significant and lead to policy misclassification. Our results stress the need to better understand the mechanism of bias propagation across the modeling scales in order to design more successful local-scale strategies. We find that the impact of climate change is spatially homogeneous in both regions, implying strong regional influence. The climate benefit for ozone (daily average and maximum) is up to -5% for Paris and -2% for Stockholm city. The joined climate benefit on PM2.5 and PM10 in Paris is between -10 and -5% while for Stockholm we observe mixed trends up to 3% depending on season and size class. In Stockholm, emission mitigation leads to concentration reductions up to 15% for daily average and maximum ozone and 20% for PM and through a sensitivity analysis we show that this response is entirely due to changes in emissions at the regional scale. On the contrary, over the city of Paris (VOC-limited photochemical regime), local mitigation of NOx emissions increases future ozone concentrations due to ozone titration inhibition. This competing trend between the respective roles of emission and climate change, results in an increase in 2050 daily average ozone by 2.5% in Paris. Climate and not emission change appears to be the most influential factor for maximum ozone concentration over the city of Paris, which may be particularly interesting in a health impact perspective. [ABSTRACT FROM AUTHOR]

The negative impacts of fine particulate matter (PM2.5) exposure on human health are a primary motivator for air quality research. However, estimates of the air pollution health burden vary considerably and strongly depend on the datasets and methodology. Satellite observations of aerosol optical depth (AOD) have been widely used to overcome limited coverage from surface monitoring and to assess the global population exposure to PM2.5 and the associated premature mortality. Here we quantify the uncertainty in determining the burden of disease using this approach, discuss different methods and datasets, and explain sources of discrepancies among values in the literature. For this purpose we primarily use the MODIS satellite observations in concert with the GEOS-Chem chemical transport model. We contrast results in the United States and China for the years 2004-2011. We estimate that in the United States, exposure to PM2.5 accounts for approximately 4 % of total deaths compared to 22 % in China (using satellite-based exposure), which falls within the range of previous estimates. The difference in estimated mortality burden based solely on a global model vs. that derived from satellite is approximately 9 % for the US and 4 % for China on a nationwide basis, although regionally the differences can be much greater. This difference is overshadowed by the uncertainty in the methodology for deriving PM2.5 burden from satellite observations, which we quantify to be on order of 20 % due to uncertainties in the AOD-to-surface-PM2.5 relationship, 10 % due to the satellite observational uncertainty, and 30 % or greater uncertainty associated with the application of concentration response functions to estimated exposure. [ABSTRACT FROM AUTHOR]

Drawing on fine particulate matter (PM2.5) from satellite raster data and matching with the county-level socio-economic data from 2008 to 2015 in China, this paper investigates the impacts of the establishment of National Key Ecological Functional Zones (NKEFZ) on environmental quality by employing the difference-in-differences method, which was stablished in June 2011. The results indicate that the establishment of the NKEFZ significantly decreased the concentration values of PM2.5, a drop of about 20% during the study period, after the paper controls for other factors affecting air quality. The robustness tests using the maximum and medium concentration values of PM2.5 show similar results. Through further analysis, the paper finds that the establishment of NKEFZ can improve the ecological utilization efficiency of land. [ABSTRACT FROM AUTHOR]

10.3390/app11199014 5 Park B., Kim S., Park S., Kim M., Kim T.Y., Park H. Development of Multi-Item Air Quality Monitoring System Based on Real-Time Data. The researchers also investigated the impact of the COVID-19 lockdown on air pollutant concentrations, suggesting advancements in modeling and calibration techniques to improve the accuracy of air quality monitoring under different conditions [[7]]. In recent years, notable advancements have been achieved in the science of aerosol and precursor gas measurements as well as the techniques used to apply them. [Extracted from the article]