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Pulp and paper mills are known large emitters of air pollution creating potential environmental challenges and human health impacts. This study assesses air pollution and associated impacts from connected pulp and paper facilities which operate in Edmundston, New Brunswick, Canada, and Madawaska, Maine, United States (US). Despite operating within two regulatory jurisdictions on both sides of the international border (in Canada and the US), the mills have exceeded their approvals to operate and national air quality regulations multiple times between 2010 and 2017. Previous studies have linked many types of cancer as well as respiratory and cardiovascular diseases to air pollution emitted by pulp and paper mills, making this an important regional issue in Canada and the US. While potential environmental and community impacts are likely similar on both sides of the international border, emissions from each side are not reported on the other, and few studies have been conducted on either side. Historic and ongoing lack of local epidemiological health and environmental impact studies and recommendations for improvements to mill operations to improve environmental and human health are presented.

Worldwide, the need for thermal insulation materials used to increase the energy performance of buildings and ensure indoor thermal comfort is constantly growing. There are several traditional, well-known and frequently used thermal insulation materials on the building materials market, but there is a growing trend towards innovative materials based on agro-industrial waste. This paper analyses the performance of 10 such innovative thermal insulation materials obtained by recycling cellulosic and/or animal waste, using standardised testing methods. More precisely, thermal insulation materials based on the following raw materials were analysed: cellulose acetate, cigarette filter manufacturing waste; cellulose acetate, cigarette filter manufacturing waste and cigarette paper waste; cellulose acetate, waste from cigarette filter manufacturing, waste cigarette paper and waste aluminised paper; cellulose from waste paper (two types made by two independent manufacturers); wood fibres; cellulose from cardboard waste; cellulose from waste cardboard, poor processing, inhomogeneous product; rice husk waste and composite based on sheep wool, recycled PET fibres and cellulosic fibres for the textile industry. The analysis followed the performance in terms of thermal insulating quality, evidenced by the thermal conductivity coefficient (used as a measurable indicator) determined for both dry and conditioned material at 50% RH, in several density variants, simulating the subsidence under its own weight or under various possible stresses arising in use. The results showed in most cases that an increase in material density has beneficial effects by reducing the coefficient of thermal conductivity, but exceptions were also reported. In conjunction with this parameter, the analysis of the 10 types of materials also looked at their moisture sorption/desorption capacity (using as a measurable indicator the amount of water stored by the material), concluding that, although they have a capacity to regulate the humidity of the indoor air, under low RH conditions the water loss is not complete, leaving a residual quantity of material that could favour the development of mould. Therefore, the impact on indoor air quality was also analysed by assessing the risk of mould growth (using as a measurable indicator the class and performance category of the material in terms of nutrient content conducive to the growth of microorganisms) under high humidity conditions but also the resistance to the action of two commonly encountered moulds, Aspergillus niger and Penicillium notatum. The results showed a relative resistance to the action of microbiological factors, indicating however the need for intensified biocidal treatment. [ABSTRACT FROM AUTHOR]

There is a high growing demand for regulating ecosystem services such as air quality regulation, regulation of air temperature and humidity, and flood regulation, in urban ecosystems which is important for urban planning. A comprehensive review of the current studies of the urban ecosystem, regulating ecosystem services, and their connection with urban planning actions is needed. The current paper presents such a review conducted in six stages to evaluate the state-of-the-art of regulating ecosystem services and their relationship with urban planning. It includes 58 papers selected after a precise keywords search and developed by publication screening, defining indicators, developing an assessment template, and meta-analysis of the results. The analyses are focused on spatial data used in the studies, the methods applied for ecosystem services assessment, and the relationships between regulating ecosystem services, urban planning, and green infrastructure. The most studied regulating ecosystem services within reviewed publications are regulation of chemical composition of atmosphere and oceans, regulation of temperature and humidity, including ventilation and transpiration, and hydrological cycle and water flow regulation (including flood control, and coastal protection). Although urban planningrelated papers are only 1/3 of the pool of papers, appropriate results have been obtained for assessing the urban planning-regulating ecosystem services relation. The review also identified some significant knowledge gaps that can be used as a starting point for future studies. [ABSTRACT FROM AUTHOR]

This paper aims to explore the relationship between the Air Quality Index (AQI), COVID-19 incidence rates, and population density within Malaysia's ten most populous cities from January 2018 to December 2021. Data were sourced from the Department of Statistics Malaysia, the World Air Quality Index Project, and Our World in Statistics. The methodology integrated population-based city classification and AQI assessment, cluster analysis through SPSS, and Generalized Additive Mixed Model (GAMM) analysis using R Studio despite encountering a data gap in AQI for five months in 2019. Cities were organized into three clusters based on their AQI: Cluster One included Ipoh, Penang, Kuala Lumpur, and Melaka, Cluster Two comprised Kuantan, Seremban, Johor Bahru, and Kota Bharu, Cluster Three featured Kota Kinabalu and Kuching. GAMM analysis revealed prediction accuracies for AQI variations of 58%, 60%, and 41% for the respective clusters, indicating a notable impact of population density on air quality. AQI variations remained unaffected by COVID-19, with a forecasted improvement in air quality across all clusters. The paper presents novel insights into the negligible impact of COVID-19 on AQI variations and underscores the predictive power of population dynamics on urban air quality, offering valuable perspectives for environmental and urban planning. [ABSTRACT FROM AUTHOR]

Actual acquired air quality time series data are highly volatile and nonstationary, and accurately predicting nonlinear time series data containing complex noise is an ongoing challenge. This paper proposes an air quality prediction method based on empirical mode decomposition (EMD), a transformer and a bidirectional long short-term memory neural network (BiLSTM), which is good at addressing the ultrashort-term prediction of nonlinear time-series data and shows good performance for application to the air quality dataset of Patna, India (6:00 am on October 3, 2015–0:00 pm on July 1, 2020). The AQI sequence is first decomposed into intrinsic mode functions (IMFs) via EMD and subsequently predicted separately via the improved transformer algorithm based on BiLSTM, where linear prediction is performed for IMFs with simple trends. Finally, the predicted values of each IMF are integrated using BiLSTM to obtain the predicted AQI values. This paper predicts the AQI in Patna with a time window of 5 h, and the RMSE, MAE and MAPE are as low as 5.6853, 2.8230 and 2.23%, respectively. Moreover, the scalability of the proposed model is validated on air quality datasets from several other cities, and the results prove that the proposed hybrid model has high performance and broad application prospects in real-time air quality prediction. [ABSTRACT FROM AUTHOR]

In recent decades, sustainable development and environmental protection, especially air quality, have become key concerns for governments as well as scholars. As a typical regulation for air quality monitoring, the Ambient Air Quality Standard (AAQS) (2012) is a good attempt at balancing economic growth and environmental protection in development exploration. Therefore, this paper takes the AAQS (2012) as a quasi-natural experiment to detect its influence on the total factor productivity (TFP) of polluting firms. The results of a DID model suggest that the AAQS (2012) significantly improves the TFP of polluting firms in China even after several robust tests. Further, this paper also finds that firms in high urbanization and with over-investment experience are more sensitive to the new standard. As for the channels, the results show that air quality monitoring affects firms' TFP by adjusting the industry concentration level and innovation capabilities. This research offers a novel perspective for decision-makers, highlighting the importance of harmonizing economic growth with environmental sustainability. [ABSTRACT FROM AUTHOR]

The Sala del Dottorato (Hall of Graduates) is a magnificent library in the University of Perugia which plays the double role of providing optimal conservation of valuable books and manuscripts while also hosting important events. This double role is closely connected to contrasting indoor microclimatic conditions. This paper presents the results of a multidisciplinary study, begun in 2019, which investigates optimal conditions for the conservation of volumes by monitoring thermo-hygrometric and air quality parameters. The study describes the current conditions of the Hall (in terms of air temperature, relative humidity and concentration of CO2), highlighting critical aspects, defining strategies for their mitigation and control, and outlining future developments. Improvement measures relate to the installation of a permanent monitoring system with alarm settings and data storage, technical interventions on the windows, and the restoration of several volumes. The paper shows the importance of monitoring as an instrument of control in real time and provides guidelines for management to be implemented according to indoor microclimatic conditions. [ABSTRACT FROM AUTHOR]

In light of the escalating concerns surrounding climate change and air quality degradation, the imperative for energy conservation and emission reduction has garnered widespread attention. Given that factories represent a significant portion of electricity consumption within the power network, a comprehensive analysis of the electricity consumption behavior of energy- intensive enterprises becomes paramount. To meticulously dissect the electricity consumption patterns of energy-intensive enterprises, this paper categorizes them into four distinct production modes: 24-hour all-day production factories, pure daytime production factories, pure nighttime production factories, and environmentally friendly peaking production factories. Employing the dynamic electricity-carbon factor as a guiding force, the analysis encompasses electricity consumption, tariff expenditure, peaking costs, carbon emissions, and comfort levels associated with each production method throughout the year. A delicate equilibrium is sought among multiple objectives, aiming to optimize the user experience while simultaneously mitigating costs and carbon emissions. Furthermore, this paper conducts a comparative analysis of each objective, employing single-objective genetic algorithms and the interior point method. The resultant findings serve as invaluable insights for business users, aiding in informed decision- making processes. [ABSTRACT FROM AUTHOR]

The Omicron pandemic broke out in Shanghai in March 2022, and some infected people spread to some cities in the Yangtze River Delta (YRD) region. To achieve the dynamic zero-COVID target as soon as possible, Shanghai and nine cities that were heavily affected by Shanghai implemented the lockdown measures. This paper aims to quantify the impact of the lockdown on air quality and human health. A difference-in-difference (DID) model was first used to measure the impact of the lockdown on air quality in these ten cities. Based on the results of the DID model, we estimated the PM2.5-related health and economic benefits using the concentration–response function and the value of statistical life method. Results showed that the lockdown has reduced the concentrations of PM2.5, PM10, SO2, NO2, and CO by 9.87 μg/m3, 17.31 μg/m3, 0.75 μg/m3, 9.03 μg/m3, and 0.07 mg/m3, respectively. The number of avoided premature deaths due to PM2.5 reduction was estimated to be 35,342. The resulting economic benefits totaled 18.86 billion US dollars. We investigated the reasons for the air quality improvement in these ten cities and found the "3 + 11" policy has had a great impact on air quality. Compared with the first COVID-19 lockdown in early 2020, the effect of the lockdown in 2022 was smaller. These findings demonstrated that reductions in anthropogenic emissions would achieve substantial air quality improvement and health benefits. This paper re-emphasized continuous efforts to improve air quality are essential to protect public health. [ABSTRACT FROM AUTHOR]

Measuring the systemic impact of major public health emergencies on the light industry and preventing various uncertain future external risks have become the key challenges to ensuring the stability of the light industry. This paper takes the occurrence of major public health emergencies as the background and focuses on environmental issues such as air pollution and indoor air quality degradation during emergencies. And to explore the multiscale impact of major public health emergencies on the light industry, typical light industry subsectors, and light industry enterprises. The findings of our study reveal that major public health emergencies have a negative impact on the light industry, particularly in the form of a short-term decline in exports, which tends to converge in the long run. Further, it is also revealed that there is heterogeneity in the impact on environmentally sensitive industries, labor-intensive industries, and others. At the microfirm level, major public health emergencies have shown a negative effect, especially the recent pandemic, which has a longer duration and a wider reach. Through multiscale research, this paper provides policy suggestions to improve the macrogovernance mechanism and risk prevention system for the light industry. [ABSTRACT FROM AUTHOR]

The application of low‐cost air quality monitoring networks has substantially grown over the last few years, following the technological advances in the production of cheap and portable air pollution sensors, thus potentially greatly increasing the limited spatial information on air quality conditions provided by traditional stations. However, the use of low‐cost air quality sensors still presents many limitations, mostly related to the reliability of their measurements. Despite the increasing number of papers focusing on these issues, some of the challenges connected to the use of low‐cost air quality sensors are still poorly investigated and understood, considering in particular those related to long‐term applications of low‐cost air quality networks and their integration within the reference air quality monitoring system. The present review aims at filling this gap, by analysing the characteristics of low‐cost air quality monitoring networks that were run across long‐term field campaigns, including their geographical location, the pollutants monitored, the type and number of stations employed, and the length of the field campaign, with a particular attention on assessing the aims for their deployment and on the evaluation of their integration within official air quality monitoring networks. Moreover, a critical analysis of the most insightful suggestions and recommendations delivered in the literature, as well as of the relevant critical issues, is presented, highlighting still open research areas and outlining future challenges. [ABSTRACT FROM AUTHOR]

Accurate prediction of air quality is crucial for assessing the state of the atmospheric environment, especially considering the nonlinearity, volatility, and abrupt changes in air quality data. This paper introduces an air quality index (AQI) prediction model based on the Dung Beetle Algorithm (DBO) aimed at overcoming limitations in traditional prediction models, such as inadequate access to data features, challenges in parameter setting, and accuracy constraints. The proposed model optimizes the parameters of Variational Mode Decomposition (VMD) and integrates the Informer adaptive sequential prediction model with the Convolutional Neural Network-Long Short Term Memory (CNN-LSTM). Initially, the correlation coefficient method is utilized to identify key impact features from multivariate weather and meteorological data. Subsequently, penalty factors and the number of variational modes in the VMD are optimized using DBO. The optimized parameters are utilized to develop a variationally constrained model to decompose the air quality sequence. The data are categorized based on approximate entropy, and high-frequency data are fed into the Informer model, while low-frequency data are fed into the CNN-LSTM model. The predicted values of the subsystems are then combined and reconstructed to obtain the AQI prediction results. Evaluation using actual monitoring data from Beijing demonstrates that the proposed coupling prediction model of the air quality index in this paper is superior to other parameter optimization models. The Mean Absolute Error (MAE) decreases by 13.59%, the Root-Mean-Square Error (RMSE) decreases by 7.04%, and the R-square (R2) increases by 1.39%. This model surpasses 11 other models in terms of lower error rates and enhances prediction accuracy. Compared with the mainstream swarm intelligence optimization algorithm, DBO, as an optimization algorithm, demonstrates higher computational efficiency and is closer to the actual value. The proposed coupling model provides a new method for air quality index prediction. [ABSTRACT FROM AUTHOR]

The civil aircraft cabin is enclosed and highly occupied, making it susceptible to a decline in indoor environmental quality. The environmental quality of civil aircraft cabins not only depends on objective factors such as temperature, relative humidity, and the presence of air pollutants such as carbon dioxide (CO2), carbon monoxide (CO), ozone (O3), particle matter (PM), and volatile organic compounds (VOCs) but also the subjective factors pertaining to the perceptions and health symptoms of passengers and crew. However, few studies have thoroughly examined the air quality and thermal comfort parameters that are measured during in-flight testing in airplane cabins, as well as the passengers' subjective perceptions. In order to evaluate the in-flight thermal comfort and air quality status, this study conducted a review of the recent literature to compile data on primary categories, standard limits, and distribution ranges of in-flight environmental factors within civil aircraft cabins. Following a search procedure outlined in this paper, 54 papers were selected for inclusion. Utilizing the Monte Carlo method, the Predicted Mean Vote (PMV) distributions under different exercise intensities and clothing thermal resistance were measured with the in-cabin temperature and humidity from in-flight tests. Recommendations based on first-hand data were made to maintain the relative humidity in the cabin below 40%, ensure wind speed remains within the range of 0–1 m/s, and regulate the temperature between 25–27 °C (for summer) and 22–27 °C (for winter). The current estimated cabin air supply rate generally complies with the requirements of international standards. Additionally, potential carcinogenic and non-carcinogenic risks associated with formaldehyde, benzene, tetrachloroethylene, and naphthalene were calculated. The sorted data of in-flight tests and the evaluation of the subjective perception of the occupants provide an evaluation of current cabin thermal comfort and air quality status, which can serve as a reference for optimizing indoor environmental quality in future generations of civil aircraft cabins. [ABSTRACT FROM AUTHOR]

Air pollution is a growing concern due to severe threats to public health and the environment. The need for reliable air quality monitoring solutions has never been more critical. This research paper introduces an innovative approach to addressing this challenge by deploying a low-cost Internet of Things (IoT) air monitoring station and providing a blockchain technology solution to enhance environmental data transparency, reliability, and accessibility. Our paper adopts a concept of merging IoT and blockchain technologies and collecting some parameters that help to assess air quality by using three sensors, DHT11, MQ7, and MQ135, to collect temperature, humidity, carbon monoxide, and carbon dioxide parameters, respectively, to measure the gases and thus indicate the air quality within the surrounding area. Collecting and sharing these types of valuable data will be very important for various stakeholders, such as governmental bodies, researchers, and the public. This approach is consistent with the principles of sustainable development, facilitating informed decision-making and promoting eco-friendly policies. This research explores the technical architecture of the IoT air monitoring stations, offering a promising solution for addressing air pollution concerns while promoting sustainable development goals. The proposed system is a model for leveraging emerging technologies to advance environmental monitoring and create smarter, livable cities. This approach aligns with the principles of sustainable development and eco-friendly initiatives. This research offers a promising model for enhancing environmental monitoring efforts and advancing the creation of smarter, more sustainable urban environments. The proposed IoT, cloud platform and blockchain-based system not only addresses pressing air pollution challenges but also sets a benchmark for leveraging emerging technologies in environmental science. [ABSTRACT FROM AUTHOR]

Research on the quality of the air in rural areas is essential for determining base emissions of air pollutants, evaluating the effects of dust pollutants particular to rural areas, modeling the dispersion of pollutants, and developing appropriate pollution mitigation systems. The absence of a systematic review based on the assessment of air quality levels in agricultural settings based on integrated weather variables and air pollutants in the literature draws attention to the deficiencies and the necessity of further research in this area. Hence, our study aimed to develop an Arduino monitoring system with related sensors to acquire some air pollutants and weather parameters. Additionally, we proposed an innovative solution to compare air quality levels by suggesting a new criterion called an integrated indicator for air quality assessment (IAQA). It was created based on the weighted average method to combine the investigated air pollutants and weather parameters. This criterion was evaluated while conducting field measurements in 29 environmentally different agricultural regions located within the Kingdom of Saudi Arabia. To determine the integrated indicator, all the values of the variables were normalized between 0 and 1. The agricultural setting with the lowest integrated indicator was the best environmentally. The lowest and highest values of the integrated indicator ranged from 37.03% and 66.32%, respectively, with an arithmetic average of 48.24%. The developed criterion can change its value depending on the change in the weight value of the variables involved, and it is suitable for application to any other agricultural or non-agricultural setting to evaluate the pollution level in the air. Although similar research has been published, this paper presents novelty findings based on integrated values of air pollutants and weather variables for defining a new criterion called IAQA. Additionally, this paper presents original results for air pollutants and weather aspects in different agricultural settings. [ABSTRACT FROM AUTHOR]

The purpose of this study was to empirically evaluate the performance of fibrous materials that meet the criteria for inlet air filtration in internal combustion engines. The characteristics of filtration efficiency and accuracy, as well as the characteristics of flow resistance, were determined based on the mass of dust accumulated in the filter bed during the filtration process. Single-layer filter materials tested included cellulose, polyester, and glass microfiber. Multilayer filter media such as cellulose–polyester–nanofibers and cellulose–polyester were also examined. A new composite filter bed—consisting of polyester, glass microfiber, and cellulose—and its filtration characteristics were evaluated. Utilizing specific air filtration quality factors, it was demonstrated that the composite is characterized by high pre-filtration efficiency (99.98%), a short pre-filtration period (qs = 4.21%), high accuracy (dpmax = 1.5–3 µm) for the entire lifespan of the filter, and a 60–250% higher dust absorption coefficient compared to the other tested materials. A filtration composite bed constructed from a group of materials with different filtration parameters can be, due to its high filtration efficiency, accuracy, and dust absorption, an excellent filter material for engine intake air. The composite's filtration parameters will depend on the type of filter layers and their order relative to the aerosol flow. This paper presents a methodology for the selection and testing of various filter materials. [ABSTRACT FROM AUTHOR]

Nitrogen oxides (NOx = NO + NO2) are air pollutants which are co-emitted with CO2 during high-temperature combustion processes. Monitoring NOx emissions is crucial for assessing air quality and for providing proxy estimates of CO2 emissions. Satellite observations, such as those from the TROPOspheric Monitoring Instrument (TROPOMI) on board the Sentinel-5P satellite, provide global coverage at high temporal resolution. However, satellites measure only NO2 , necessitating a conversion to NOx. Previous studies have applied a constant NO2 -to- NOx conversion factor. In this paper, we develop a more realistic model for NO2 -to- NOx conversion and apply it to TROPOMI data of 2020 and 2021. To achieve this, we analysed plume-resolving simulations from the MicroHH large-eddy simulation model with chemistry for the Bełchatów (PL), Jänschwalde (DE), Matimba (ZA) and Medupi (ZA) power plants, as well as a metallurgical plant in Lipetsk (RU). We used the cross-sectional flux method to calculate NO , NO2 and NOx line densities from simulated NO and NO2 columns and derived NO2 -to- NOx conversion factors as a function of the time since emission. Since the method of converting NO2 to NOx presented in this paper assumes steady-state conditions and that the conversion factors can be modelled by a negative exponential function, we validated the conversion factors using the same MicroHH data. Finally, we applied the derived conversion factors to TROPOMI NO2 observations of the same sources. The validation of the NO2 -to- NOx conversion factors shows that they can account for the NOx chemistry in plumes, in particular for the conversion between NO and NO2 near the source and for the chemical loss of NOx further downstream. When applying these time-since-emission-dependent conversion factors, biases in NOx emissions estimated from TROPOMI NO2 images are greatly reduced from between - 50 % and - 42 % to between only - 9.5 % and - 0.5 % in comparison with reported emissions. Single-overpass estimates can be quantified with an uncertainty of 20 %–27 %, while annual NOx emission estimates have uncertainties in the range of 4 %–21 % but are highly dependent on the number of successful retrievals. Although more simulations covering a wider range of meteorological and trace gas background conditions will be needed to generalise the approach, this study marks an important step towards a consistent, uniform, high-resolution and near-real-time estimation of NOx emissions – especially with regard to upcoming NO2 -monitoring satellites such as Sentinel-4, Sentinel-5 and CO2M. [ABSTRACT FROM AUTHOR]

Air pollution poses a great danger to human health and economic development, and understanding people's willingness to pay for improved air quality (WTPA) impacts environmental protection. This paper investigates WTPA based on the perspective of subjective well-being (SWB) and analyzes the mediating role of social interaction on the relationship between the two. This paper distinguishes social interactions into online and offline interactions and analyzes whether the mediating effect of the two different interactions on SWB and WTPA exists separately. Using data from the 2018 China General Social Survey (CGSS), we find that SWB has a significant positive effect on WTPA, individuals with higher well-being have higher pro-environmental willingness; there is no age, education level, sex of the person, or regional heterogeneity in the effect of SWB on WTPA; offline social interactions play a partially mediating role between SWB and WTPA, while online social interactions failed to mediate between the two. This paper's policy implication is that improving residents' subjective well-being is both an important development goal and an essential way to resolve the conflict between economic development and environmental protection. [ABSTRACT FROM AUTHOR]

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

Introduction: Aerosol pollutants are known to raise the risk of development of non-communicable respiratory diseases (NCRDs) such as asthma, chronic bronchitis, chronic obstructive pulmonary disease, and allergic rhinitis. Sub-Saharan Africa's rapid pace of urbanization, economic expansion, and population growth raise concerns of increasing incidence of NCRDs. This research characterizes the state of research on pollution and NCRDs in the 46 countries of Sub-Saharan Africa (SSA). This research systematically reviewed the literature on studies of asthma; chronic bronchitis; allergic rhinitis; and air pollutants such as particulate matter, ozone, NOx, and sulfuric oxide.Methods: We searched three major databases (PubMed, Web of Science, and Scopus) using the key words "asthma", "chronic bronchitis", "allergic rhinitis", and "COPD" with "carbon monoxide (CO)", "sulfuric oxide (SO)", "ozone (O3)", "nitrogen dioxide (NO2)", and "particulate matter (PM)", restricting the search to the 46 countries that comprise SSA. Only papers published in scholarly journals with a defined health outcome in individuals and which tested associations with explicitly measured or modelled air exposures were considered for inclusion. All candidate papers were entered into a database for review.Results: We found a total of 362 unique research papers in the initial search of the three databases. Among these, 14 met the inclusion criteria. These papers comprised studies from just five countries. Nine papers were from South Africa; two from Malawi; and one each from Ghana, Namibia, and Nigeria. Most studies were cross-sectional. Exposures to ambient air pollutants were measured using spectrometry and chromatography. Some studies created composite measures of air pollution using a range of data layers. NCRD outcomes were measured by self-reported health status and measures of lung function (spirometry). Populations of interest were primarily schoolchildren, though a few studies focused on secondary school students and adults.Conclusions: The paucity of research on NCRDs and ambient air pollutant exposures is pronounced within the African continent. While capacity to measure air quality in SSA is high, studies targeting NCRDs should work to draw attention to questions of outdoor air pollution and health. As the climate changes and SSA economies expand and countries urbanize, these questions will become increasingly important. [ABSTRACT FROM AUTHOR]

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

The paper presents a specific method of environmental impact assessment applied in Strategic Environmental Assessment (SEA) for the National Emission Reduction Plan (NERP) in the Republic of Serbia, based on air quality. The specificity of the approach is in the application of a semiquantitative method of multicriteria evaluation based on air dispersion modeling and the integration of SEA goals, indicators and criteria for assessing the impact of the NERP on the quality of air and other environmental elements in this method. When predicting changes in air quality for the planning horizon to 2028, the physical, geographical and climatic characteristics of the area were taken into account, as well as technical measures to reduce SO2 emissions, since this was the dominant pollutant from the Serbian coal-fired power plants studied by the NERP. Air pollution modeling was carried out using the AERMOD software package based on the data collected, and the quantitative results obtained were used in a multicriteria evaluation as part of the SEA. The results of the research indicated the importance of applying this approach in order to significantly increase objectivity in the SEA process, since it is an important element of decision making at the strategic level. In addition, a comparative presentation of the modeling results before and after application of the NERP was an important part of the SEA process, and it provided a clear insight into expected changes in the air quality. This is a key argument for making appropriate policy decisions on spatial, energy, environmental and socio-economic development in the Republic of Serbia, which, like other developing countries, is sluggishly following global trends in energy transition. [ABSTRACT FROM AUTHOR]

To explore the relationship between air pollution and total factor productivity and new pathways, This paper examines the impact of air pollution on total factor productivity of A-share listed companies in Shanghai and Shenzhen between 2015 and 2019. It investigates this relationship by considering two pathways: investor sentiment and government attention. The findings indicate that air pollution suppresses total factor productivity of firms. However, air pollution stimulates investor sentiment, which in turn increases R&D investment and total factor productivity, reducing to some extent the dampening effect of air pollution on total factor productivity. There exists a notable positive correlation between air quality and government attention, acting as a mediating variable. This implies that air pollution has the potential to capture the attention of governmental entities, leading to the implementation of appropriate measures aimed at managing and mitigating the occurrence of air pollution caused by industrial enterprises.And the relevant governments should formulate a series of policies to meet the different needs of different enterprises. These two approaches have varying impacts depending on the type of enterprises, thus governments should develop laws to cater to the various demands of different types of enterprises. [ABSTRACT FROM AUTHOR]

The first experimental results of the indoor air quality in two kindergartens located in the Republic of Srpska are presented in this paper. Kindergarten representatives for the year of construction (old and new), building materials, and energy efficiency have been chosen. Indoor air quality measurements (air temperature, relative humidity, ventilation rate, CO2, and radon concentration) were performed during the winter of 2015/2016. Measured indoor air quality parameters are discussed and compared to the international standards BAS EN 16798-1, ASHRAE 62.1, and ISO 7730. The average measured radon concentrations for both buildings have not exceeded the level of 200 Bq/m³, but for reliable results, long-term measurement needs to be performed. The CO2 concentration in the old kindergarten fulfills the BAS EN 16798-1 requirement for Category I during 62.43% of total occupancy time, while for the new kindergarten, it is only 5.79% of full occupancy time. Results of CO2 concentration confirm that good sealing of the envelope of new buildings and user behavior (number of users and natural ventilation) does affect air quality. Furthermore, a high correlation between CO2 concentration and relative humidity in both buildings and a more considerable correlation for the new building have been observed. [ABSTRACT FROM AUTHOR]

This paper presents an indoor air quality (IAQ) monitoring system designed for a better end-user experience. The monitoring system consists of elements, from the monitoring sensor to the monitoring interface, designed and implemented by the research team, especially for the proposed monitoring system. The monitoring solution is intended for users who live in houses without automatic ventilation systems. The air quality sensor is designed at a minimum cost and complexity to allow multi-zone implementation without significant effort. The user interface uses a spatial graphic representation that facilitates understanding areas with different air quality levels. Presentation of the outdoor air quality level supports the user's decision to ventilate a space. An innovative element of the proposed monitoring interface is the real-time forecast of air quality evolution in each monitored space. The paper describes the implementation of an original monitoring solution (monitoring device, Edge/Cloud management system, innovative user monitoring interface) and presents the results of testing this system in a relevant environment. The research conclusions show the proposed solution's benefits in improving the end-user experience, justified both by the technical results obtained and by the opinion of the users who tested the monitoring system. [ABSTRACT FROM AUTHOR]

Increasing urbanisation and industrialisation produce major environmental challenges such as air pollution, which endangers human, animal, and vegetation life. Reliable measurement, monitoring, and prediction of Air Quality (AQ) have emerged as key global concerns. The State Government-Municipal Corporation is working on policy reforms to fight the deterioration of air quality in Pune and other Indian cities. In this paper, Real Time Air Quality Surveillance & Forecasting System (RTAQSFS) has been developed, which work in the cascaded model incorporating electronics hardware as well as machine learning algorithms. The presence of air pollutants is measured using sensors like MQ135, MQ7, MQ131 etc. The performance of machine learning algorithms viz. Linear regression, Ridge regression, Lasso regression, Decision tree and Random Forest has been evaluated wrt. RMSE and R2. The experimental results show that Random Forest outperforms the other algorithm providing less RMSE and R2 as 99.9% for all the parameters. [ABSTRACT FROM AUTHOR]

Predicting particulate matter with a diameter of 10 μm (PM10) is crucial due to its impact on human health and the environment. Today, aerosol optical depth (AOD) offers high resolution and wide coverage, making it a viable way to estimate PM concentrations. Recent years have also witnessed in-creasing promise in refining air quality predictions via deep neural network (DNN) models, out-performing other techniques. However, learning the weights and biases of the DNN is a task classified as an NP-hard problem. Current approaches such as gradient-based methods exhibit significant limitations, such as the risk of becoming ensnared in local minimal within multi-objective loss functions, substantial computational requirements, and the requirement for continuous objective functions. To tackle these challenges, this paper introduces a novel approach that combines the binary gray wolf optimizer (BGWO) with DNN to improve the optimization of models for air pollution prediction. The BGWO algorithm, inspired by the behavior of gray wolves, is used to optimize both the weight and bias of the DNN. In the proposed BGWO, a novel sigmoid function is proposed as a transfer function to adjust the position of the wolves. This study gathers meteorological data, topographic information, PM10 pollution data, and satellite images. Data preparation includes tasks such as noise removal and handling missing data. The proposed approach is evaluated through cross-validation using metrics such as correlation rate, R square, root-mean-square error (RMSE), and accuracy. The effectiveness of the BGWO-DNN framework is compared to seven other machine learning (ML) models. The experimental evaluation of the BGWO-DNN method using air pollution data shows its superior performance compared with traditional ML techniques. The BGWO-DNN, CapSA-DNN, and BBO-DNN models achieved the lowest RMSE values of 16.28, 19.26, and 20.74, respectively. Conversely, the SVM-Linear and GBM algorithms displayed the highest levels of error, yielding RMSE values of 36.82 and 32.50, respectively. The BGWO-DNN algorithm secured the highest R2 (88.21%) and accuracy (93.17%) values, signifying its superior performance compared with other models. Additionally, the correlation between predicted and actual values shows that the proposed model surpasses the performance of other ML techniques. This paper also observes relatively stable pollution levels during spring and summer, contrasting with significant fluctuations during autumn and winter. [ABSTRACT FROM AUTHOR]

Aircraft operations from above ground level to 3000 feet impact air quality and cause health issues, particularly for people working and living in and around airports. This paper evaluates the current emission calculation methods to identify the most accurate way to generate an emission inventory. Journal articles on aircraft influence on air quality were selected for a systematic literature review (SLR). After screening 277 articles written in English, 60 articles on emission calculation methods were included in the analysis. Based on the analysis, air quality can be more accurately assessed when considering direct emissions from an aircraft than when measuring atmospheric pollutant concentrations. While the International Civil Aviation Organization's (ICAO) advanced approach was the most widely used from the literature reviewed, airport-specific, time-in-mode, and actual atmospheric conditions where aircraft operate offer the potential for significant improvement. The SLR demonstrates a need for more accurate emission calculation methods to assess the aircraft's influence on air quality. The SLR guides airlines and airports to maintain an accurate emission inventory, which will set future targets to improve air quality. [ABSTRACT FROM AUTHOR]

Air pollution is a serious problem that affects economic development and people's health, so an efficient and accurate air quality prediction model would help to manage the air pollution problem. In this paper, we build a combined model to accurately predict the AQI based on real AQI data from four cities. First, we use an ARIMA model to fit the linear part of the data and a CNN-LSTM model to fit the non-linear part of the data to avoid the problem of blinding in the CNN-LSTM hyperparameter setting. Then, to avoid the blinding dilemma in the CNN-LSTM hyperparameter setting, we use the Dung Beetle Optimizer algorithm to find the hyperparameters of the CNN-LSTM model, determine the optimal hyperparameters, and check the accuracy of the model. Finally, we compare the proposed model with nine other widely used models. The experimental results show that the model proposed in this paper outperforms the comparison models in terms of root mean square error (RMSE), mean absolute error (MAE) and coefficient of determination (R2). The RMSE values for the four cities were 7.594, 14.94, 7.841 and 5.496; the MAE values were 5.285, 10.839, 5.12 and 3.77; and the R2 values were 0.989, 0.962, 0.953 and 0.953 respectively. [ABSTRACT FROM AUTHOR]

Different urban block morphologies can greatly influence the air quality inside the buildings of the block. The model presented in this paper determines the correlation between block morphology and air quality, and outputs the indoor air quality via computational fluid dynamics (CFD) simulations. In this study, stagnant air was assumed to have a velocity lower than 0.15 m/s and considered to be low-quality air in the context of human health. The geometry of the urban blocks was simplified based on real-life buildings. Doors and windows were not 3D-modeled, and all the vertical surfaces of the buildings were considered as potential locations for them. Eight of the highest-frequency wind directions out of sixteen main directions per block were used. Wind directions and velocities were determined based on the weather data for one location chosen for testing. The simulation used the Reynolds-averaged Navier–Stokes (RANS) equations with the k-ε turbulence model. The results were then interpreted through the specific algorithm using 3D graphic software. The surface of the building envelope was divided into smaller meshes. For each mesh, the average velocity was calculated and meshes were marked for values below the stagnant air threshold. The eight results, one from each wind direction, were synthesized into one final result. The model was tested on eight different urban block morphologies based on real-life blocks, i.e., blocks in Novi Sad, Serbia. The pressure on the building surfaces determined via CFD analyses is presented alongside results from the method described in this paper. The results show that urban block morphologies with clustered buildings inside the urban block, which are typical for the most newly built structures in Novi Sad, have areas on the facades where windows cannot provide elemental natural ventilation throughout the year. To interpret the results obtained in this research, graphs and 3D color-coding models were used. The best results show a 1-tower urban block morphology with only 0.7% of all vertical faces of the model registering a wind velocity lower than the set minimum. The worst results were measured for a traditional urban block typical in old city centers. A total of 54.5% of all the vertical surfaces show no problems with air stagnation in close proximity to them. [ABSTRACT FROM AUTHOR]

Understanding air quality requires a comprehensive understanding of its various factors. Most of the association rule techniques focuses on high frequency terms, ignoring the potential importance of low- frequency terms and causing unnecessary storage space waste. Therefore, a dynamic genetic association rule mining algorithm is proposed in this paper, which combines the improved dynamic genetic algorithm with the association rule mining algorithm to realize the importance mining of low- frequency terms. Firstly, in the chromosome coding phase of genetic algorithm, an innovative multi-information coding strategy is proposed, which selectively stores similar values of different levels in one storage unit. It avoids storing all the values at once and facilitates efficient mining of valid rules later. Secondly, by weighting the evaluation indicators such as support, confidence and promotion in association rule mining, a new evaluation index is formed, avoiding the need to set a minimum threshold for high-interest rules. Finally, in order to improve the mining performance of the rules, the dynamic crossover rate and mutation rate are set to improve the search efficiency of the algorithm. In the experimental stage, this paper adopts the 2016 annual air quality data set of Beijing to verify the effectiveness of the unit point multi-information coding strategy in reducing the rule storage air, the effectiveness of mining the rules formed by the low frequency item set, and the effectiveness of combining the rule mining algorithm with the swarm intelligence optimization algorithm in terms of search time and convergence. In the experimental stage, this paper adopts the 2016 annual air quality data set of Beijing to verify the effectiveness of the above three aspects. The unit point multi-information coding strategy reduced the rule space storage consumption by 50%, the new evaluation index can mine more interesting rules whose interest level can be up to 90%, while mining the rules formed by the lower frequency terms, and in terms of search time, we reduced it about 20% compared with some meta-heuristic algorithms, while improving convergence. [ABSTRACT FROM AUTHOR]

This paper proposes a clustering approach for multivariate time series with time-varying parameters in a multiway framework. Although clustering techniques based on time series distribution characteristics have been extensively studied, methods based on time-varying parameters have only recently been explored and are missing for multivariate time series. This paper fills the gap by proposing a multiway approach for distribution-based clustering of multivariate time series. To show the validity of the proposed clustering procedure, we provide both a simulation study and an application to real air quality time series data. [ABSTRACT FROM AUTHOR]

Objectives: The COVID-19 pandemic has been a major public health concern for the past 3 years. Scientific evidence on the relationship between SARS-CoV-2 infection and indoor air quality still needs to be demonstrated. This scoping review aims to study the association between air quality indoors and COVID-19. Methods: A scoping review analyzing the association between indoor air quality and epidemiological outcomes was conducted. Papers published between 1 January 2020 and 31 October 2022 were included. Hospital settings were excluded from the study. Results: Eight relevant articles met the inclusion criteria. Indoor settings included workplaces, schools, restaurants, and public transport. Types of ventilation used to improve indoor air quality were dilution methods (opening windows) and mechanical systems with or without filtration or purifier. CO2 sensors were employed in one study. All the studies showed a positive association between indoor air quality and its improvement and epidemiological indicators. Conclusion: The findings of this scoping review indicate that indoor air quality, which can be improved with ventilation methods, may reduce the risk of developing COVID-19. Ventilation could thus be viewed as a possible effective mitigating method. [ABSTRACT FROM AUTHOR]

The Environmental Protection Agency of the Republic of Serbia continuously monitors and collects air quality parameters at numerous measuring points throughout the country. The results revealed that the levels of air pollution recorded during the lockdown differed from those recorded during the same period the previous years. In this paper, we examined pollutant concentration trends in 2020 and a few previous years to determine the underlying causes of these trends. Pollutants are measured at five stations in Belgrade from the beginning of March to the end of July between 2015 and 2020. Because no hourly data were available, NO2, SO2, and suspended particles PM10 and PM2.5 are average daily values, whereas CO and ozone are daily 8-hour maximums. Concentrations were compared and averaged only when data for the same period over time was available, i.e. when comparing 2020 to previous years. The results were compared to the annual and daily limit values (for ozone and CO 8 hours maximum, respectively), as well as the variations in concentrations over time. The goal of calculating and displaying these values is to specifically identify a change in air pollution during the COVID-19 virus pandemic isolation period. In general, for Belgrade in 2020, NO2 concentrations are lower at four stations, PM2.5 concentrations are lower at two stations, PM10 concentrations are higher at one station and lower at another, and SO2 concentrations are higher than in previous years (2015-2019). [ABSTRACT FROM AUTHOR]

The distribution and quantity of trees within a city impacts issues such as urban heat islands, air quality and general city planning. Having an automatic procedure for cataloguing them can be a valuable aid for future urban planning and design. In this paper, the forestry surveying neural network, DeepForest, is utilised for tree detection in the urban environment. The study area covers District Lozenets, which is the greenest part of Sofia, Bulgaria. Three distinct approaches are implemented considering the urban vegetation context - a simple tree detection, a tree cluster detection and a mixing approach between the two based on approximation with Poisson Disk Sampling. The evaluation of the developed models, in terms of F-1 score, shows that the achieved results are comparable to the ones achieved by the original application of the DeepForest model. Due to the specifics of urban data, all models tended to achieve a higher precision but a lower recall than the original DeepForest. Conditions, such as shade from the sun, buildings or other trees, make the detection more challenging. The obtained results prove the feasibility of the proposed approaches, even with a small amount of labelled data. The tree cluster and mixed approaches have the potential to resolve part of the issues coming from the urban environment context of application. [ABSTRACT FROM AUTHOR]

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The paper draws on environmental history and applies this concept to the issue of air pollution in the Most region in the 1960s. Combustion processes had caused the air in the region to be polluted by both solid and gaseous substances, released into the air in varying amounts. What options did society have to respond to this newly recognised problem in a region that had long been used for industrial production? The paper tries to provide an answer by tracing the evolution of environmental legislation and the reactions of the media and authorities at all levels. It proposes three hypotheses, namely, (1) that the state chose to focus on economic and industrial development rather than environmental protection, (2) that it tried to centralize control of technological solutions to the air pollution problem, and (3) that it attempted to control public discourse and awareness of air quality and the factors that affected it. The results arrived at are as follows: the state did indeed choose to prioritise industrial development over environmental protection, although it had attempted to take several (mainly legislative) measures to mitigate pollution. The hypothesis that there was an attempt to centralise technological solutions to the problem and to control them was not borne out; instead, the choice of solutions (if any) was left to companies. Control of the flow of information on air pollution was minimal during the 1960s, and only changed after 1968 with what is known as Czechoslovak normalization. [ABSTRACT FROM AUTHOR]

The expected increase in urbanization changes the morphology of urban areas. These changes affect the urban environment and impact several aspects, such as climate, energy, air quality, and socioeconomic factors, among others. Therefore, it is important to lead cities towards sustainable development. The goal of this paper is to understand which domains should be considered to assess the sustainability of cities, at an environmental level and with a focus on air quality, and how those domains are connected, based on a comprehensive literature review, which resulted in 1479 articles. The results show that "Urban Climate", "Air Quality", "Urban Morphology", "Health", "Energy" and "Mobility" are the most common domains in research, and that 39% of articles only assess one domain. It is possible to understand that though 96% of articles contain up to three domains, the great majority do not assess those domains in an integrated way. There are many studies that evaluate important domains of urban areas; however, they mostly evaluate these effects in isolation, and rarely in tandem. Moving forward, it is important to understand how to best connect the most relevant domains, under an integrated multicriteria approach, thus allowing a more complete assessment of the sustainability of urban areas. [ABSTRACT FROM AUTHOR]

The pollutants dispersion in the environment and the positioning of the emission sources, identified from the measured air quality data, are the main knowledge for defining the environmental status. This study aims to locate the pollution source in the urban environment starting from experimental measurements obtained from the monitoring networks. In detail, the source identification algorithm uses artificial intelligence for source identification (AISI), which was applied to the case study of an industrial paper mill located in a region of southern Italy. In this case study, the air quality monitoring network consists of four smart measuring devices arranged in such a way as to obtain triangular meshes, capable of providing real-time 24/24 h measurements with a resolution of 1 min, information on NO2, PM10, and PM2.5 concentrations, temperature, pressure, relative humidity, wind direction, and intensity. The developed AISI algorithm allows combining the information on the concentrations of pollutants and the wind intensity and direction with their positions to identify the probable pollution source position. Some interesting days were analyzed identifying the pollution sources' locations that were external to the network. [ABSTRACT FROM AUTHOR]

Air quality significantly influences human health and the environment, necessitating a robust monitoring to detect abnormalities. This paper aims to develop a new model to accurately capture air quality data's structural changes and asymmetrical patterns. We introduce the neo-normal Markov Switching Autoregressive (MSAR) Modified Skew Normal Burr (MSN-Burr) model, called neo-normal MSAR MSN-Burr. This model extends the MSAR normal framework, handling symmetrical and asymmetrical patterns in air quality data. The MSN-Burr distribution is employed for accurate estimation of skewed and symmetric data. The model efficiency is demonstrated through simulation studies generating symmetric data with normal, double exponential, and Student- t distributions, followed by application to real air quality data using Stan language. The proposed model successfully adapts to asymmetric structural changes, as evidenced by creating the Highest Posterior Distribution (HPD) for upper and lower limits. The model identifies two regimes representing normal and abnormal air quality conditions, with modes of 8 and 19 µg/m3, respectively. The MSAR-MSN-Burr model exhibits a 32.27% RMSE improvement in simulations and a 16.4% RMSE improvement in real air quality data over the normal-MSAR model. The proposed neo-normal MSAR MSN-Burr model is significantly enhancing the accuracy of air quality monitoring, providing a more efficient tool for detecting air quality abnormalities. [ABSTRACT FROM AUTHOR]

As the share of air conditioning electricity consumption within total grid electricity consumption grows, the decomposition of such consumption becomes increasingly crucial for assessing electricity usage patterns, devising consumption scheduling strategies, and maintaining the stability of the power grid. Although there is a strong correlation between apparent temperature and air conditioning electricity consumption, the literature currently available seldom explores the impact of apparent temperature on this consumption. Moreover, there is a scarcity of effective assessment indices to evaluate the efficacy of air conditioning electricity consumption breakdown. This study introduces a method for decomposing electricity consumption from air conditioning units, utilizing effective duration as a basis to tackle these issues. By employing an apparent temperature model as a constraint, this approach identifies the effective operating time of air conditioning and constructs a constrained convex optimization problem to estimate air conditioning power usage. Additionally, a novel evaluation index for the effectiveness of air conditioning electricity consumption decomposition is proposed, which includes penalties for negative decomposed consumption, alongside the traditional consistency index. Comparative experiments are conducted using real electricity consumption data from Fujian Province. Empirical results indicate that the methodology for air conditioning electricity consumption decomposition presented in this paper aligns more closely with actual conditions. Furthermore, the evaluation metrics introduced for the decomposition of air conditioning electricity consumption are adept at precisely gauging the quality of the air conditioning electricity consumption data. [ABSTRACT FROM AUTHOR]