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Policies limiting the number of days vehicles are permitted to circulate based on the last digit of their license plates have experienced a resurgence in popularity, particularly in Chinese cities. This paper provides a critical review of the literature on license-plate-based driving restrictions. Of the 235 papers reviewed, most (152) only briefly mention license-plate-based restriction programmes to describe contextual conditions or provide an example of a policy that influences driving or vehicle purchases. Reviewing forty empirical papers, we find a divided literature on whether and where license-plate-based driving restrictions reduce local pollution or congestion. Some differences in findings likely relate to differences in research design or outcome measurement. Variations in policy design, enforcement, and other local conditions also play an important role. We next review findings about the multiple legal and illegal strategies households employ in response to driving restrictions. The second- car hypothesis, which posits that restriction policies backfire and lead to increased local pollution due to households purchasing second cars with different final license-plate digits, has become particularly popular. Evidence for the hypothesis, however, is mixed. Households employ a range of other behavioural responses, such as shuffling driving trips to specific days and driving in lightly policed areas, that likely attenuate the effectiveness of license-plate-based driving restrictions. As a result, researchers and policymakers should not expect to find a 20% reduction in pollution or congestion from banning a fifth of vehicles from the road. Improving policy effectiveness will likely require policymakers to address intended and unintended behavioural responses through additional mechanisms. [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]

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]

The quantification of ammonia (NH3) emissions is essential to the more accurate quantification of atmospheric nitrogen deposition, improved air quality and the assessment of ammonia-related agricultural policy and climate mitigation strategies. The quantity, geographic distribution and historical trends of these emissions remain largely uncertain. In this paper, a new Chinese agricultural fertilizer NH3 (CAF NH3) emissions inventory has been compiled that exhibits the following improvements: (1) a 1 km × 1 km gridded map on the county level was developed for 2008, (2) a combined bottom-up and top-down method was used for the local correction of emission factors (EFs) and parameters, (3) the spatial and temporal patterns of historical time trends for 1978-2008 were estimated and the uncertainties were quantified for the inventories, and (4) a sensitivity test was performed in which a province-level disaggregated map was compared with CAF NH3 emissions for 2008. The total CAF NH3 emissions for 2008 were 8.4 Tg NH3 yr-1 (a 6.6-9.8 Tg interquartile range). From 1978 to 2008, annual NH3 emissions fluctuated with three peaks (1987, 1996 and 2005), and total emissions increased from 3.2 to 8.4 Tg at an annual rate of 3.0 %. During the study period, the contribution of livestock manure spreading increased from 37.0 to 45.5 % because of changing fertilization practices and the rapid increase in egg, milk and meat consumption. The average contribution of synthetic fertilizer, which has a positive effect on crop yields, was approximately 38.3 % (minimum: 33.4 %; maximum: 42.7 %). With rapid urbanization causing a decline in the rural population, the contribution of the rural excrement sector varied widely between 20.3 and 8.5 %. The average contributions of cake fertilizer and straw returning were approximately 3.8 and 4.5 %, respectively, thus small and stable. Collectively, the CAF-NH3 emissions reflect the nation's agricultural policy to a certain extent. An effective approach to decreasing PM2.5 concentrations in China would be to simultaneously decrease NOx, SO2 and NH3 emissions. [ABSTRACT FROM AUTHOR]

Air pollution is a risk factor for many diseases that can lead to death. Therefore, it is important to develop forecasting mechanisms that can be used by the authorities, so that they can anticipate measures when high concentrations of certain pollutants are expected in the near future. Machine Learning models, in particular, Deep Learning models, have been widely used to forecast air quality. In this paper we present a comprehensive review of the main contributions in the field during the period 2011–2021. We have searched the main scientific publications databases and, after a careful selection, we have considered a total of 155 papers. The papers are classified in terms of geographical distribution, predicted values, predictor variables, evaluation metrics and Machine Learning model. [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]

Air quality has a significant impact on human health, in order to alleviate the air pollution and improve the ability to predict the air quality. In this paper, a prediction model of air quality index composed of variational mode decomposition and temporal convolutional network was proposed. First, in order to reduce the non-stationarity and randomness of the time series, the original air quality index sequence was decomposed by variational mode decomposition, and the decomposition number was determined by the central frequency method to decompose into multiple relatively stable sub-sequences with different frequency scales. Then, the decomposed sub-stable sequence was predicted by the time convolutional network. Finally, the prediction data were integrated and reconstructed to obtain the final prediction results. Comparing the results of other forecasting models by performance evaluation metrics, the combined forecasting model proposed in this paper reduced RMSE by 20.9%, 19.2%, 5.1%, 29.9%, 23.7% on the Beijing dataset. MAPE reduced by 26.6%, 22.3%, 19.5%, 28.9%, 15.0%, respectively. MAE decreased by 19.1%, 20.6%, 9.6%, 29.5%, 23.5%. R2 increased by 4.6%, 4.0%, 0.8%, 14.9%, 5.5% respectively. This proves the accuracy and reliability of the proposed model. [ABSTRACT FROM AUTHOR]

This study reviewed the status quo of research on machine learning (ML), including deep learning, in the rail industry. This study conducted a scientometric analysis and critical review of 640 papers selected from 12,675 web-crawled papers. The extensive and complex networks of topics, researchers, and countries were analyzed using the Louvain method, a co-occurrence keyword analysis, a degree centrality analysis, and other network analysis methods. The results indicate that the majority of studies of ML in the rail industry focused on maintenance activities and traffic management, and mainly targeted rolling stock, rails, and passengers. Overhead contact systems, including catenaries, are a high-demand objective for ML-based maintenance. Although analyses of tunnels and stations remain rare, passenger flow prediction, station air quality estimation, shield tunneling performance improvement, and ground settlement are areas of high importance. Geographically, China, the US, and the United Kingdom lead ML studies in the rail industry, and the level of collaboration is higher among European countries than among countries on other continents. Future challenges include ensuring the security and stability of ML, along with considering novel mindsets, the black-box effect, improvements in ML techniques, and resource overload when introducing ML technologies. [ABSTRACT FROM AUTHOR]

Air toxics are airborne pollutants known or suspected to cause cancer or other serious health effects, including certain volatile organic compounds (VOCs), prioritized by the US Environmental Protection Agency (EPA). While several EPA-designated air toxics are monitored at a subset of Canadian National Air Pollution Surveillance (NAPS) sites, Canada has no specific 'air toxics' control priorities. Although pulp and paper (P&P) mills are major industrial emitters of air pollutants, few studies quantified the spectrum of air quality exposures. Moreover, most NAPS monitoring sites are in urban centers; in contrast, rural NAPS sites are sparse with few exposure risk records. The objective of this pilot study was to investigate prioritized air toxic ambient VOC concentrations using NAPS hourly emissions data from a rural Pictou, Nova Scotia Kraft P&P town to document concentration levels, and to determine whether these concentrations correlated with wind direction at the NAPS site (located southwest of the mill). Publicly accessible Environment and Climate Change Canada data (VOC concentrations [Granton NAPS ID: 31201] and local meteorological conditions [Caribou Point]) were examined using temporal (2006-2013) and spatial analytic methods. Results revealed several VOCs (1,3-butadiene, benzene, and carbon tetrachloride) routinely exceeded EPA air toxics-associated cancer risk thresholds. 1,3-Butadiene and tetrachloroethylene were significantly higher ( p < 0.05) when prevailing wind direction blew from the northeast and the mill towards the NAPS site. Conversely, when prevailing winds originated from the southwest towards the mill, higher median VOC air toxics concentrations at the NAPS site, except carbon tetrachloride, were not observed. Despite study limitations, this is one of few investigations documenting elevated concentrations of certain VOCs air toxics to be associated with P&P emissions in a community. Findings support the need for more research on the extent to which air toxics emissions exist in P&P towns and contribute to poor health in nearby communities. [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]

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 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]

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]

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 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]

The overall ecological benefits of green space landscape mainly depend on the internal structure and spatial layout of green space. The study area is six urban districts in Xi'an, with the support of remote sensing technology and ArcGIS, which explored the scale effect and spatial differentiation of the green space landscape patterns and their synergistic influence on PM2.5 and PM10. In this paper, the principal component analysis was used to screen out the indices that can represent the green space landscape pattern in the study area, and on this basis, the landscape metrics weighted change model based on the entropy method was established for the spatial scale analysis of green space landscape. Then, the spatial differentiation characteristics of green space landscape patterns and their impacts on air quality were studied comprehensively using statistical analysis methods. The results show obvious gradient changes in the green space landscape pattern in Xi'an, and the constructed green space landscape pattern metrics can better explain the variation of atmospheric PM concentration. Moreover, we further found that increasing the number and area of green space patches and reducing their overall dispersion can effectively reduce PM concentration. This paper is the first to reveal the characteristic scales of green space landscape patterns in the main urban area of Xi'an. It provides a scientific basis for creating a reasonable spatial distribution pattern of urban green space, which plays an active role in improving the ecological benefits of urban green space. [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]

Affected by the epidemic and other factors, the global economy is in a downturn, and countries around the world are under unprecedented debt pressure. How will this affect environmental protection? Taking China as an example, this paper empirically studies the impact of changes in local government behavior on urban air quality under fiscal pressure. This paper uses the generalized method of moments (GMM) to find that fiscal pressure has significantly reduced PM2.5 emissions, with a unit increase in fiscal pressure will increase PM2.5 by about 2%. The mechanism verification shows that three channels affect PM2.5 emissions: (1) fiscal pressure has prompted local governments to relax the supervision of existing pollution-intensive enterprises. (2) Local governments reduce environmental regulations for attracting more pollution-intensive enterprises. (3) Local governments tend to reduce environmental protection investment to save fiscal expenses. The paper's conclusions provide new policy ideas for promoting environmental protection in China, as well as served as a case for analyzing current changes in environmental protection in other countries. [ABSTRACT FROM AUTHOR]

The article presents a study which aims to describe the influence of different characteristics of assimilation system on the surface ozone in Europe. It states that the evaluation of the background error covariance matrix (BECM) was emphasized. The result of the study shows that the data assimilation system was efficient in bring the model assimilations closer to observations.

To improve short-term particulate matter (PM) forecasts in South Korea, the initial distribution of PM composition, particularly over the upwind regions, is primarily important. To prepare the initial PM composition, the aerosol optical depth (AOD) data retrieved from a geostationary equatorial orbit (GEO) satellite sensor, GOCI (Geostationary Ocean Color Imager) which covers Northeast Asia (113-146° E; 25-47° N), were used. Although GOCI can provide a higher number of AOD data in a semi-continuous manner than low Earth orbit (LEO) satellite sensors, it still has a serious limitation in that the AOD data are not available at cloud pixels and over high-reflectance areas, such as desert and snow-covered regions. To overcome this limitation, a spatio-temporal (ST) kriging method was used to better prepare the initial AOD distributions that were converted into the PM composition over Northeast Asia. One of the largest advantages to using the ST-kriging method in this study is that more observed AOD data can be used to prepare the best initial AOD fields. It is demonstrated in this study that the short-term PM forecast system developed with the application of the ST-kriging method can greatly improve PM10 predictions in Seoul Metropolitan Area (SMA), when evaluated with ground-based observations. For example, errors and biases of PM10 predictions decreased by ∼ 60 and ∼ 70 %, respectively, during the first 6 h of short-term PM forecasting, compared with those without the initial PM composition. In addition, the influences of several factors (such as choices of observation operators and control variables) on the performances of the short-term PM forecast were explored in this study. The influences of the choices of the control variables on the PM chemical composition were also investigated with the composition data measured via PILS-IC and low air-volume sample instruments at a site near Seoul. To improve the overall performances of the short-term PM forecast system, several future research directions were also discussed and suggested. [ABSTRACT FROM AUTHOR]

A new anisotropic hr-adaptive mesh technique has been applied to modelling of multiscale transport phenomena, which is based on a discontinuous Galerkin/control volume discretization on unstructured meshes. Over existing air quality models typically based on static-structured grids using a locally nesting technique, the advantage of the anisotropic hr-adaptive model has the ability to adapt the mesh according to the evolving pollutant distribution and flow features. That is, the mesh resolution can be adjusted dynamically to simulate the pollutant transport process accurately and effectively. To illustrate the capability of the anisotropic adaptive unstructured mesh model, three benchmark numerical experiments have been setup for two-dimensional (2-D) transport phenomena. Comparisons have been made between the results obtained using uniform resolution meshes and anisotropic adaptive resolution meshes. [ABSTRACT FROM AUTHOR]

The Emission Scenario Projection (ESP) method produces future-year air pollutant emissions for mesoscale air quality modeling applications. We present ESP v2.0, which expands upon ESP v1.0 by spatially allocating future-year emissions to account for projected population and land use changes. In ESP v2.0, US Census Division-level emission growth factors are developed using an energy system model. Regional factors for population-related emissions are spatially disaggregated to the county level using population growth and migration projections. The county-level growth factors are then applied to grow a base-year emission inventory to the future. Spatial surrogates are updated to account for future population and land use changes, and these surrogates are used to map projected county-level emissions to a modeling grid for use within an air quality model. We evaluate ESP v2.0 by comparing US 12 km emissions for 2005 with projections for 2050. We also evaluate the individual and combined effects of county-level disaggregation and of updating spatial surrogates. Results suggest that the common practice of modeling future emissions without considering spatial redistribution over-predicts emissions in the urban core and under-predicts emissions in suburban and exurban areas. In addition to improving multi-decadal emission projections, a strength of ESP v2.0 is that it can be applied to assess the emissions and air quality implications of alternative energy, population and land use scenarios. [ABSTRACT FROM AUTHOR]

This article discusses the use of machine learning to analyze air quality in the Hawke's Bay region of New Zealand. The Hawke's Bay Regional Council is responsible for managing air quality in the region and aims to meet national and international air quality standards. The article focuses on the Awatoto airshed, where monitoring of particulate matter (PM10 and PM2.5) has been conducted. The study found that wind direction, average wave height, mean wave direction, and air temperature were the most influential factors in predicting PM10 concentrations. The machine learning approach provided better predictions compared to a linear model, but further research is needed to fully explain the variance in PM10 measurements. [Extracted from the article]

Papers on population aging and the effects of environmental quality on health expenditure have critical policy consequences. However, findings in the relevant literature are mixed, and papers generally focus on developed countries. To provide new information to the literature, this paper examines the impact of globalization, economic growth, greenhouse gas emissions, and population aging on health expenditures in emerging market economies with annual data for the period 2000 to 2018. The paper follows a second-generation advanced panel data method that considers cross-sectional dependency. The estimation results reveal that population aging, economic growth, and greenhouse gas emissions have an increasing effect on health expenditures, while globalization has a decreasing effect. Furthermore, one-way causality running from population aging to health expenditures is confirmed, while a feedback causality relationship is observed between health expenditures and other indicators (globalization, economic growth, and greenhouse gas emissions). After all, the outputs of this paper can provide critical policy implications about the relationships between aging, globalization, air quality, and health expenditures in developing countries. [ABSTRACT FROM AUTHOR]

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Based on a review of the relevant literature, this paper uses data from 70 large and medium-sized cities to test whether the purchasing behaviours of Chinese urban residents are affected by air quality. Despite the indirectness of perceptions of air quality and the non-immediacy of the impact of air quality, buyers' awareness of environmental factors plays an important role in their willingness to pay. This paper proposes that residents' awareness strengthens the influence of air quality on commercial housing prices and conducts empirical tests to examine this proposition. To overcome the deviation caused by the heterogeneity of the samples, the paper also discusses whether the above relationship varies by region, development level and political level. The empirical results show that the average annual sales price of commercial housing decreases by 0.881 units for each unit of air pollution improvement. Comprehensive analysis finds that Chinese urban residents concern the air quality near commercial housing, but the degree of influence is weak. Residents' level of awareness strengthens the influence of air quality on commercial housing prices, but the relationship varies by region, development level and political level. [ABSTRACT FROM AUTHOR]

Using panel data for 277 Chinese cities, this paper applies a time-varying difference-in-differences (DID) model to empirically test the impact of China's carbon emissions trading scheme (ETS) on urban air quality, and further explores its heterogeneity and the mechanisms involved. The results show that ETS can improve urban air quality. This conclusion remains robust through a series of robustness tests, including PSM-DID estimation, varying window periods, exclusion of significant events, lag phase, and placebo tests. The dynamic effect test indicates that ETS has a continuous and effective effect on improving urban air quality. Mechanism analysis reveals that the degree of marketization can enhance the improvement effect that ETS has on urban air quality. Meanwhile, industrial structure upgrading and green technology innovation are important mechanisms by which pilot ETS policy improves urban air quality. Regional heterogeneity analysis finds that ETS only improves urban air quality in eastern and central regions. [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]

Based on panel data of 282 prefecture-level and above cities in China from 2013 to 2020, this paper investigates the impact and transmission paths of the "LFD" land disposal model on urban air quality at the theoretical and empirical levels using dynamic fixed-effects and dynamic spatial Durbin models. The results show that the way land is allocated in a city has a lagging and long-term impact on air quality not only locally but also in neighboring cities. The type of land supply by local governments to different sectors is an important pathway to influence urban air quality. Extended analysis shows that land market reforms in China can significantly reduce urban air quality index (AQI) and effectively mitigate urban air quality, with long-term effects. This paper provides a theoretical and scientific basis for correcting the mismatch of land resources and promoting urban ecological environment in China. [ABSTRACT FROM AUTHOR]

We conducted simulations using the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) version 3.5 to study air quality in East and South Asia at a spatial resolution of 20 km x 20 km. We find large discrepancies between two existing emissions inventories: the Regional Emission Inventory in Asia version 2 (REAS) and the Emissions Database for Global Atmospheric Research version 4.2 (EDGAR) at the provincial level in China, with maximum differences up to 500% for CO emissions, 190% for NO, and 160% for primary PM10. Such differences in the magnitude and the spatial distribution of emissions for various species lead to 40-70% difference in surface PM10 concentrations, 16-20% in surface O3 mixing ratios, and over 100% in SO2 and NO2 mixing ratios in the polluted areas of China. Our sensitivity run shows WRF-Chem is sensitive to emissions, with the REAS-based simulation reproducing observed concentrations and mixing ratios better than the EDGAR-based simulation for July 2007. We conduct further model simulations using REAS emissions for January, April, July, and October in 2007 and evaluate simulations with available ground-level observations. The model results show clear regional variations in the seasonal cycle of surface PM10 and O3 over East and South Asia. The model meets the air quality model performance criteria for both PM10 (mean fractional bias, MFB≤±60%) and O3 (MFB≤±15%) in most of the observation sites, although the model underestimates PM10 over Northeast China in January. The model predicts the observed SO2 well at sites in Japan, while it tends to overestimate SO2 in China in July and October. The model underestimates most observed NO2 in all four months. These findings suggest that future model development and evaluation of emission inventories and models are needed for particulate matter and gaseous pollutants in East and South Asia. [ABSTRACT FROM AUTHOR]