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Airborne particulate matter (PM) is of great concern in the modern-day atmosphere owing to its association with a variety of health impacts, such as respiratory and cardiovascular diseases. Of the various size fractions of PM, it is the finer fractions that are most harmful to health, in particular ultrafine particles (PM0.1; UFPs), with an aerodynamic diameter ≤ 100 nm. The smaller size fractions, of ≤2.5 µm (PM2.5; fine particles) and ≤0.1 µm (PM0.1; ultrafine particles), have been shown to have numerous linkages to negative health effects; however, their collection/sampling remains challenging. This review paper employed a comprehensive literature review methodology; 200 studies were evaluated based on the rigor of their methodologies, including the validity of experimental designs, data collection methods, and statistical analyses. Studies with robust methodologies were prioritised for inclusion. This review paper critically assesses the health risks associated with fine and ultrafine particles, highlighting vehicular emissions as the most significant source of particulate-related health effects. While coal combustion, diesel exhaust, household wood combustors' emissions, and Earth's crust dust also pose health risks, evidence suggests that exposure to particulates from vehicular emissions has the greatest impact on human health due to their widespread distribution and contribution to air pollution-related diseases. This article comprehensively examines current sampling technologies, specifically focusing on the collection and sampling of ultrafine particles (UFP) from ambient air to facilitate toxicological and physiochemical characterisation efforts. This article discusses diverse approaches to collect fine and ultrafine particulates, along with experimental endeavours to assess ultrafine particle concentrations across various microenvironments. Following meticulous evaluation of sampling techniques, high-volume air samplers such as the Chem Vol Model 2400 High Volume Cascade Impactor and low-volume samplers like the Personal Cascade Impactor Sampler (PCIS) emerge as effective methods. These techniques offer advantages in particle size fractionation, collection efficiency, and adaptability to different sampling environments, positioning them as valuable tools for precise characterisation of particulate matter in air quality research and environmental monitoring. [ABSTRACT FROM AUTHOR]

Fine particulate matter with an aerodynamic diameter less than 2.5 µm ( P M 2.5 ) profoundly affects environmental systems, human health and economic structures. Multi-source data and advanced machine or deep-learning methods have provided a new chance for estimating the P M 2.5 concentrations at a high spatiotemporal resolution. In this paper, the Random Forest (RF) algorithm was applied to estimate hourly P M 2.5 of the North China area (Beijing–Tianjin–Hebei, BTH) based on the next-generation geostationary meteorological satellite Himawari-8/AHI (Advanced Himawari Imager) aerosol optical depth (AOD) products. To improve the estimation of P M 2.5 concentration across large areas, we construct a method for co-weighting the environmental similarity and the geographical distances by using an attention mechanism so that it can efficiently characterize the influence of spatial–temporal information hidden in adjacent ground monitoring sites. In experiment results, the hourly P M 2.5 estimates are well correlated with ground measurements in BTH, with a coefficient of determination ( R 2 ) of 0.887, a root-mean-square error (RMSE) of 18.31 μ g/ m 3 , and a mean absolute error (MAE) of 11.17 µg/ m 3 , indicating good model performance. In addition, this paper makes a comprehensive analysis of the effectiveness of multi-source data in the estimation process, in this way, to simplify the model structure and improve the estimation efficiency of the model while ensuring its accuracy. [ABSTRACT FROM AUTHOR]

Air pollution is caused by the presence of polluting elements. Ozone (O3), carbon monoxide (CO), carbon dioxide (CO2), nitrogen dioxide (NO2), sulfur dioxide (SO2), and particulate matter (PM) are the most controlled gasses because they can be released into the atmosphere naturally or as a result of human activity, which affects air quality and causes disease and premature death in exposed people. Depending on the substance being measured, ambient air monitors have different types of air quality sensors. In recent years, there has been a growing interest in designing drones as mobile sensors for monitoring air pollution. Therefore, the objective of this paper is to provide a comprehensive patent review to gain insight into the proprietary technologies currently used in drones used to monitor outdoor air pollution. Patent searches were conducted using three different patent search engines: Google Patents, WIPO's Patentscope, and the United States Patent and Trademark Office (USPTO). The analysis of each patent consists of extracting data that supply information regarding the type of drone, sensor, or equipment for measuring PM, the lack or presence of a cyclone separator, and the ability to process the turbulence generated by the drone's propellers. A total of 1473 patent documents were retrieved using the search engine. However, only 13 met the inclusion criteria, including patent documents reporting drone designs for outdoor air pollution monitoring. Therefore, was found that most patents fall under class G01N (measurement; testing) according to the International Patents Classification, where the most common sensors and devices are infrared or visible light cameras, cleaning devices, and GPS tracking devices. The most common tasks performed by drones are air pollution monitoring, assessment, and control. These categories cover different aspects of the air pollution management cycle and are essential to effectively address this environmental problem. [ABSTRACT FROM AUTHOR]

Excessive particle concentrations during heating periods, which greatly affect people's physical and mental health and their normal lives, continue to be a concern. It is more practical to understand and analyze the relationship between the fractal dimension and particle size concentration distribution of atmospheric particulate matter before and after adjusting heating energy consumption types. The data discussed and analyzed in this paper were collected by monitoring stations and measured from 2016 to 2018 in Xi'an. The data include fractal dimension and particle size concentration changes in the atmospheric particulate matter before and after adjusting the heating energy consumption types. The results indicate that adjusting the heating energy consumption types has a significant impact on particulate matter. The average concentration of PM2.5 decreased by 26.4 μg/m3. The average concentration of PM10 decreased by 31.8 μg/m3. At the same time, the different particle sizes showed a downward trend. The particles ranging from 0.265 to 0.475 μm demonstrated the maximum decrease, which was 8.80%. The heating period in Xi'an mainly involves particles ranging from 0 to 0.475 μm. The fractal dimensions of the atmospheric particulate matter before and after adjusting the heating energy consumption types were 4.809 and 3.397, respectively. After adjusting the heating energy consumption types, the fractal dimension decreased by 1.412. At that time, the proportions of particle sizes that were less than 1.0 μm, 2.0 μm, and 2.5 μm decreased by 1.467%, 0.604%, and 0.424%, respectively. This paper provides new methods and a reference value for the distribution and effective control of atmospheric particulate matter by adjusting heating energy consumption types. [ABSTRACT FROM AUTHOR]

Air quality in school environments is of particular interest due to the significant amount of time children spend in these settings. Children, being a particularly sensitive demographic, are exposed to various pollutants at school or kindergarten. In this regard, our studies have focused on monitoring the concentrations of three main categories of pollutants: VOCs (volatile organic compounds), VICs and PM (particulate matter). We conducted two experimental campaigns in seven classrooms within public educational institutions. The average concentration values of TVOC (total volatile organic compounds) ranged from 554 µg/m3 to 2518 µg/m3, of CO2 from 1055 ppm to 2050 ppm, of NH₃ (Ammonia) from 843.2 µg/m3 to 1403.4 µg/m3, of PM2.5 from 25.1 µg/m3 to 89.9 µg/m3, and of PM10 from 63.7 µg/m3 to 307.4 µg/m3. In most instances, the registered values exceeded the limit values set by national or international regulations. Furthermore, this study highlights the significant impact of a heat recovery ventilation system in improving indoor air quality by substantially reducing the levels of CO2 and PM. However, it also underscores the need for further measures to more efficiently reduce TVOC concentrations. The aim of our paper was to enhance the understanding of pollution levels in school environments, increase awareness of the importance of indoor air quality, and highlight the adverse effects of polluted air on the health of occupants. [ABSTRACT FROM AUTHOR]

During rapid urbanization, microclimate environment deterioration through events such as haze pollution and heat waves has continuously occurred in cities, which greatly affects the living environment, production activities, and health of urban residents. Therefore, it is particularly necessary to explore methods for controlling and optimizing the urban microclimate environment. In this paper, based on the mechanism of the effect of urban spatial structure at street-level on the distribution of atmospheric particulate matter, an indicator system that can be employed to comprehensively describe and quantify urban morphological structure at street-level was constructed from eight aspects: the spatial morphology of street-valleys, intensity of land use and development, geometric structure of buildings, inhomogeneity of buildings, roughness of the underlying surface, distribution of ecological landscapes, 3D architectural landscape morphology, and ventilation potential. Furthermore, using satellite remote sensing images and vector thematic maps of Shanghai, indicator factors were quantified by applying GIS technique. The intrinsic mechanism of the influence of the urban morphology on the diffusion and transport of atmospheric particulate matter was comprehensively analyzed by combining statistical methods and data mining algorithm, and eight key dominant factors were identified that can be considered to improve the urban ventilation conditions and help control urban air pollution, namely, the land use intensity, urban canopy resistance, vegetation cover, spatial congestion rate, comprehensive porosity, height-to-gross floor area ratio, building density, and average building volume ratio. As such, according to the quantitative analysis results for various combinations of the dominant factors, a spatial optimization strategy at street-level that can help improve the urban air quality was proposed in terms of identifying the pathways through which urban spatial elements affect the distribution of particulate matter, i.e., controlling the source–flow diversion–flow convergence process. [ABSTRACT FROM AUTHOR]

The low-low-temperature electrostatic precipitator (LLT-ESP) is considered one of the mainstream technological approaches for achieving ultra-low ash emissions and has already been applied in many coal-fired power plants. Particulate matter and SO3 can both be removed by LLT-ESP. However, the removal performance of SO3 is relatively lower than that of particulate matter, which is caused by the condensation characteristics of SO3. In this paper, the condensation characteristics of SO3 were investigated on a simulated experimental system, and several measurement and characteristic methods were used to investigate mechanisms. After reducing the flue gas temperature with a heat exchanger, the size distribution of particulate matter, the mass concentration of SO3 on different sizes of particulate matter, as well as the microscopic morphology and elemental composition of particulate matter, were all experimentally studied. The results indicate that gaseous SO3 transformed into a liquid phase by heterogeneous or homogeneous condensation and then adhered to the surface of particulate matter through nucleation–condensation, collision–coalescence, and adsorption reactions. Furthermore, the removal efficiency of SO3 in LLT-ESP was also investigated under various conditions, such as ash concentration and flue gas temperature drop, suggesting that a higher ash concentration and a more significant temperature drop were beneficial for improving SO3 removal efficiency. Nevertheless, it is worth noting that the impact was limited by a further increase in ash concentration and a drop in flue gas temperature. [ABSTRACT FROM AUTHOR]

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

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

Using an aerosol–climate coupled model, this paper has investigated the changes in distributions of anthropogenic and natural particles due to 4 × CO2-induced global warming, under the low emission scenario of Representative Concentration Pathway 4.5 (RCP4.5). Special attention is paid to the seasonal variations of aerosol size modes. With rising CO2 levels, surface warming, and changes in atmospheric circulations and hydrologic cycles are found during both summer (JJA) and winter (DJF). For anthropogenic particles, changes in fine anthropogenic particulate matter (PM2.5, particles with diameters smaller than 2.5 μm) decrease over high-latitude regions and increase over the tropics in both DJF and JJA. Global mean column concentrations of PM2.5 decrease by approximately 0.19 mg m−2, and concentrations of coarse anthropogenic particles (CPM, particles with diameters larger than 2.5 μm) increase by 0.005 mg m−2 in JJA. Changes in anthropogenic particles in DJF are similar to those in JJA, but the magnitudes of maximum regional changes are much smaller than those in JJA. The coarse anthropogenic particles (CPM, particles with diameters larger than 2.5 μm) increase over northern Africa and the Arabian Peninsula during JJA, whereas changes in anthropogenic CPM during DJF are minimal. During both JJA and DJF, changes in anthropogenic CPM are about two orders of magnitude smaller than those of anthropogenic PM2.5. Enhanced wet deposition by large-scale precipitation under rising CO2-induced surface warming is the critical factor affecting changes in anthropogenic particles. For natural particles, the distribution of change in the natural PM2.5 burden is similar to that of natural CPM, but much larger than natural CPM during each season. Both natural PM2.5 and CPM burdens increase over northern Africa and the Arabian Peninsula during JJA, but decrease over most of the continental regions during DJF. Changes in surface wind speed, divergence/convergence of surface wind, and precipitation are primary reasons for the variation of natural particles. [ABSTRACT FROM AUTHOR]

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

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

Time-series forecasting has a wide range of application scenarios. Predicting particulate matter with a diameter of 2.5 μm or less (PM2.5) in the future is a vital type of time-series forecasting task where valid forecasting would provide an important reference for public decisions. The current state-of-the-art general time-series model, TimesNet, has achieved a level of performance well above the mainstream level on most benchmarks. Attributing this success to an ability to disentangle intraperiod and interperiod temporal variations, we propose TimesNet-PM2.5. To make this model more powerful for concrete PM2.5 prediction tasks, task-oriented improvements to its structure have been added to enhance its ability to predict specific time spots through better interpretability and meaningful visualizations. On the one hand, this paper rigorously investigates the impact of various meteorological indicators on PM2.5 levels, examining their primary influencing factors from both local and global perspectives. On the other hand, using visualization techniques, we validate the capability of representation learning in time-series forecasting and performance on the forecasting task of the TimesNet-PM2.5. Experimentally, TimesNet-PM2.5 demonstrates an improvement over the original TimesNet. Specifically, the Mean Squared Error (MSE) improved by 8.8% for 1-h forecasting and by 22.5% for 24-h forecasting. [ABSTRACT FROM AUTHOR]

Radon (Rn) is a radioactive, colourless, odourless, noble gas that decays rapidly. It's most stable isotope, 222Rn, has a half-life of around 3.8 days. Atmospheric radon measurements play an important role in understanding our atmospheric environments. Naturally occurring radon can be used as an atmospheric tracer for airmass tracking, to assist in modelling boundary layer development, and is important for understanding background radiation levels and personal exposure to natural radiation. The daughter products from radon decay also play an important role when measuring fine particle pollution using beta-attenuation monitors (BAM). Beta radiation from the 222Rn decay chain interferes with BAM measurements of fine particles; thus, some BAMs incorporate radon measurements into their sampling systems. BAMs are ubiquitous in air quality monitoring networks globally and present a hitherto unexplored source of dense, continuous radon measurements. In this paper, we compare in situ real world 222Rn measurements from a high quality ANSTO dual flow loop, dual filter radon detector, and the radon measurements made by a commercial BAM instrument (Thermo 5014i). We find strong correlations between systems for hourly measurements (R2 = 0.91), daily means (R2 = 0.95), hour of day (R2 = 0.72–0.94), and by month (R2 = 0.83–0.94). The BAM underestimates radon by 22–39%; however, the linear response of the BAM measurements implies that they could be corrected to reflect the ANSTO standard measurements. Regardless, the radon measurements from BAMs could be used with correction to estimate local mixed layer development. Though only a 12-month study at a single location, our results suggest that radon measurements from BAMs can complement more robust measurements from standard monitors, augment radon measurements across broad regions of the world, and provide useful information for studies using radon as a tracer, particularly for boundary layer development and airmass identification. [ABSTRACT FROM AUTHOR]

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

This report provides a review of several measurement studies and campaigns focusing on road dust resuspension (RDS), which is becoming an important source of ambient particulate matter (PM) concentrations. There is a growing requirement from stakeholders, policy makers and scientists for gathering additional data around RDS, since there is a lack of well-established methodologies or those developed for measuring or modelling this source of emissions are not very recent. Moreover, conventional inventory methodologies are not available for this source of emissions, and it is not yet covered in the UK's National Atmospheric Emissions Inventory (NAEI) neither in the inventory guidebooks at European level. Based on the available literature and models, this paper also seeks to provide some preliminary considerations whether an approach can be tailored for application in air quality models used for modelling ambient PM concentrations in the UK in a consistent manner and demonstrated using a state-of-art air quality modelling tool, namely RapidAir®. The impact of including resuspension emissions on roadside concentrations was modelled in a UK case study with the greatest increases in modelled road PM10 concentrations observed on or near the road surface. The paper concludes with recommendations for longer-term implementation of the proposed improvements such as investigation of the temporal effects of RDS on PM levels; of the influence of different type of tyre materials and mixture (e.g., composition) and designs (e.g., shape, dimensions and tread); and influence of different existing pavements or of new road surface types. [ABSTRACT FROM AUTHOR]

This paper presents a method for reconstructing the historical hourly concentrations of Particulate Matter 2.5 (PM2.5) over the Kathmandu Valley from 1980 to the present. The method uses a machine learning model that is trained using PM2.5 readings from the US Embassy (Phora Durbar) as a ground truth, and the meteorological data from the Modern-Era Retrospective Analysis for Research and Applications v2 (MERRA2) as input. The Extreme Gradient Boosting (XGBoost) model acquires a credible 10-fold cross-validation (CV) score of ~83.4%, an r2-score of ~84%, a Root Mean Square Error (RMSE) of ~15.82 µg/m3, and a Mean Absolute Error (MAE) of ~10.27 µg/m3. Further demonstrating the model's applicability to years other than those for which truth values are unavailable, the multiple cross-tests with an unseen data set offered r2-scores for 2018, 2019, and 2020 ranging from 56% to 67%. The model-predicted data agree with true values and indicate that MERRA2 underestimates PM2.5 over the region. It strongly agrees with ground-based evidence showing substantially higher mass concentrations in the dry pre- and post-monsoon seasons than in the monsoon months. It also shows a strong anti-correlation between PM2.5 concentration and humidity. The results also demonstrate that none of the years fulfilled the annual mean air quality index (AQI) standards set by the World Health Organization (WHO). [ABSTRACT FROM AUTHOR]

Atmospheric visibility is an important indicator that reflects the transparency of the atmosphere and characterizes the air quality, so it is of great significance to study the long-term change in visibility. This paper is based on the global surface summary of day data (GSOD) site dataset and other relevant data, using the Mann–Kendall (MK) mutation point test, wavelet transform, and seasonal autoregressive integrated moving average (SARIMA) model forecasting. The time-frequency domain variation characteristics and related influencing factors of regional visibility in China were studied in detail, and the visibility was predicted; the results of the study showed the following: (1) the overall interannual variation of regional visibility in China has a decreasing trend, and the four-season variation has a decreasing trend, except for the rising trend in summer, with abrupt change points in both the overall interannual variation and the four-season variation. (2) There are main cycles of visibility in the Chinese region with time scales of 180 months and 18 months. Under the time scale of 180 months for the main cycle, the variation period of visibility is about 123 months, experiencing two high to low variations; under the time scale of 18 months for the main cycle, the variation period of visibility is 12 months, experiencing 21 high to low variations. (3) The development of the economy indirectly affects changes in visibility. Cities with high economies are densely populated, with concentrations of various particulate emissions and high concentrations of particulate matter, which can directly reduce visibility. (4) Two prediction models, SARIMA and long and the short-term memory (LSTM) neural network, were used to predict the visibility in China, both of which achieved good evaluation indexes, and the visibility in China may show an increasing trend in the future. [ABSTRACT FROM AUTHOR]

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

This paper investigated the physical and chemical properties of gasoline direct injection (GDI) engine particulate matter (PM). The physical properties mainly included the particulate aggregate morphology, primary particle size, and internal nanostructure. High-resolution transmission electron microscopy (HRTEM) and scanning electron microscopy (SEM) were used to obtain particle morphology information and to conduct image processing and analysis. The chemical characterization tests included X-ray photoelectron spectroscopy (XPS), energy dispersive scanning (EDS), Raman spectroscopy, Fourier-transform infrared spectroscopy (FTIR), and thermal gravimetric analysis (TGA). XPS can be used to observe the content of carbon and oxygen components and the surface carbon chemistry status, EDS can be used to obtain the elemental composition of particles, and TGA is used to analyze the oxidative kinetics of particles. Samples were collected from the exhaust emissions of a passenger vehicle compliant with China's VI emission standards under China Light-Duty Vehicle Test Cycle (CLTC) test conditions. The study found that the particle morphology mainly comprised primary particles stacked on top of each other to form agglomerate structures, and the primary particles exhibited a core–shell structure. Analysis showed that carbon and oxygen were the predominant components of the particles, with other metallic elements also present. The XPS observations agreed with the FTIR results, indicating a small amount of oxygen was present on the particle surface and that the carbon components consisted mainly of sp2 hybridized graphite and sp3 hybridized organic carbon. The TGA results indicated high characteristic temperatures and low oxidation activity. [ABSTRACT FROM AUTHOR]

In this paper, individual particle analysis by automated scanning electron microscopy (SEM) coupled with energy-dispersive X-ray (EDX) was used to assess the size-resolved information of composition, size distribution, complex refractive index, and mixing state of mineral dust aerosol particles collected using different passive and active samplers. In the study, over 120,000 particles from 53 samples were analyzed. Results show that dust particles are the dominating mineral particle type during this campaign, comprising different classes of silicates, Si-rich (quartz-like), Ca-rich (calcite-like), CaMg-rich (dolomite-like), and CaS-rich (gypsum-like). The results also show that there is no significant difference in composition between suspended and deposited dust particles. By using the particle composition, the size-resolved complex refractive index of dust particles was calculated. The real part of the refractive index varied between 1.71 and 1.53 for wavelengths in the range of 370 to 950 nm. The imaginary part of the refractive index, determined mostly by iron oxide, varied between 3.28 × 10 − 4 and 7.11 × 10 − 5 for wavelengths ranging from 250 nm to 1640 nm. In addition, the refractive index values showed a slight decrease with increasing particle size. We also analyzed the potential for buffering of the acid mobilization of iron by other dust compounds. For particles which contain both iron (Fe) and (unprocessed) calcium (Ca), acids that are able to dissolve insoluble Fe particles can react with the Ca particles before reacting with Fe, but eventually, with longer processing time, the Fe particles could be processed. By analyzing the ratio of sulfate mass to the total aerosol mass of individual particles, the mixing state of sulfate particles to the total dust particles was investigated. The analysis showed that the finer dust particles were associated with higher content of sulfate, while the coarse dust particles correspond to lower sulfate contents, revealing that only fine mode sulfate is more internally mixed with mineral dust aerosol particles. [ABSTRACT FROM AUTHOR]

Currently, air pollution is a critical public health problem, which means that the daily measurement of urban air quality can be enriched if measured in a personalized way. Personal environmental monitoring devices can guide the population to take action. They can track their daily activities, avoiding situations that could affect their health, allowing them to precisely know the air quality they breathe in real-time in various microenvironments. In this work, we present a review of cyclonic separation technology patents, such as pre-separators in monitoring devices. We focused on the state-of-the-art commercially available personal monitoring devices, the classification of kinds of patents, and a review of cyclone patents and gas–particle separation behaviors. The World Intellectual Property Organization IP's portal and Google Patents search engine were used, using international patent classification plus mesh terms involving a cyclone in an air particulate monitor after predefining inclusion and exclusion criteria such as gas–air cyclones, high efficiency, and fine particle separation. Twenty-nine patents were analyzed according to the main characteristics (e.g., cut point, flow rate, and cyclone improvement) available in the patent document. The wide range of cyclones indicates a maximum flow rate of between 0.5 and 4.5 Lpm and a lower cyclone cut point of 0.8 μm. This review includes a discussion of the most relevant features of the patent documents (flow rate, particle cut point, some cyclone improvements, and technology detection). This paper aims to give an overview of the use of cyclones as pre-separators for personal air monitoring devices and to acknowledge the patented improvements of new inventors or developers. [ABSTRACT FROM AUTHOR]

The treatment of polluted industrial flow remains a relevant topic for the purpose of sustainable development and improvement of the general state of the environment. The removal of particulate matter, and especially their fine and ultra-fine fractions, from the gas flow, is an urgent task, but it poses many challenges and demands for purification technology. This paper presents the results of the first stage of the research using a newly developed cleaning device operating by a complex principle, which consists of a new generation two-stage centrifugal filtration device and an electro-filter. The rate of air flow was varied from 0.3 to 1.16 m/s at the inlet and corresponds to an air flow yield of 53 m3/h to 205 m3/h. The maximum pressure drop at an air flow of 255 m3/h is 26 Pa. Research has shown that the efficiency of removing ultra-fine particulate matter is up to 99.7% for particles 0.3–0.5 µm in size at 200 m3/h of the air flow rate. [ABSTRACT FROM AUTHOR]

Vehicles' exhaust particulate matter (PM) emissions have significantly decreased over the years. On the other hand, non-exhaust emissions, i.e., particle emissions from brakes and tires, have increased due to the increase in the vehicle fleet, traffic congestion, and the distance traveled. As a result, regulatory bodies are investigating the possibility of mitigating non-exhaust emissions. The Euro 7 proposal introduces specific emission limits for both brakes and tires for the first time in a regulation worldwide. The methodology for brake particle emissions sampling and measurement builds on the work of the Particle Measurement Programme (PMP) informal working group of the United Nations Economic Commission for Europe (UNECE). The recently adopted Global Technical Regulation (GTR) on brakes from light-duty vehicles up to 3.5 t prescribes the technical details. In this paper, we present the technical specifications for the measurements of PM. We also evaluate the penetrations for two cases with two setups for minimum and maximum particle losses. This study, using aerosol engineering calculations, estimates the maximum expected differences between the two setups, both of which are compliant with the GTR. This study also discusses the mass ratios of PM2.5 and PM10 as a function of the mass median diameters. [ABSTRACT FROM AUTHOR]

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

An optimum operation of a gasoline particulate filter (GPF) for a gasoline direct injection vehicle in terms of its performance of pressure drop, soot loading, and filtration efficiency becomes inevitable to fulfill upcoming emission regulations. This paper proposes a methodology to characterize the GPF performance for validation of simulation models for more precise operation strategies along with future legislative requirements. The feasibility was examined through experiments of miniature GPF samples using a synthetic particle generator. Firstly, permeability of the GPF walls was estimated to be 6.9 ± 1.5 × 10−13 m2 by a flow resistance descriptor model, which was in good agreement with its pore structure. Secondly, photoacoustic sensor systems indicated soot accumulation inside the GPFs in real time thanks to linear correlations between sensor signals and soot mass concentrations in exhaust. Thirdly, particle number counting systems compliant with respective regulatory technical requirements exhibited time-resolved filtration efficiencies of the GPFs in conjunction with solid particle number emissions whose diameter was larger than 10 nm and 23 nm. The filtration efficiencies at a clean state of the GPF were 0.78 and 0.77, respectively. The slight difference could be explained by Brownian diffusion and interception for particle filtration. [ABSTRACT FROM AUTHOR]

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

The paper deals with the measurement of individual size components of particulate matter focusing on typical particle size, mass, and number concentrations primarily in the city of Košice (Slovak republic) and the surrounding countryside. The deployment of the sensor IPS-7100 in the 2nd half of December 2022 allowed us to measure the particles smaller than the detection limit of other low-cost optical sensors—namely SPS30 and SEN54. The results show that although the mass concentration of ultrafine particles is negligible in comparison to fine and coarse particles, in terms of number concentration ultrafine particles make up the dominant component of particulate matter, which stands as a warning from the health point of view. [ABSTRACT FROM AUTHOR]

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

Cooking food in water or soup, such as hot pot, is a widely used cooking method in China. This type of cooking requires no oil and cooks at a lower temperature, but that does not mean it produces fewer pollutants or is less harmful. There are few research studies on the emission characteristics and mechanisms of particulate matter emissions when eating hot pot (the boiling process), which leads to the unreasonable design of ventilation systems for this kind of catering. In this paper, the effects of boiling different ingredients (including noodles, potatoes, fish, tofu, meatballs, and pork) on particle number concentration emissions were studied. The particle number concentration and particle size distribution of PM with diameters of 0.3 μm and less, 0.3–0.5 μm and 0.5–1.0 μm (PM0.3, PM0.3–0.5 and PM0.5–1.0, respectively) were measured in an experimental chamber. The food type and shape showed very little change in the PM emission characteristics of boiling. When the boiling state was reached, the number concentration, particle size distribution, and arithmetic mean diameter of particles all fluctuated within 60 s. The emission characteristics of particles produced by boiling water and heating oil were compared. Heating oil produced more small particles, and boiling water released more large particles. Transient and steady methods were used to calculate the emission rate of particles, and the steady-state calculation has a high estimation of the emission rate. [ABSTRACT FROM AUTHOR]

Recently, precursor gases such as ammonia have sparked a growing interest in the secondary formation of particulate matter (PM). Most studies focus on urban areas and scientific data. Studies on precursor gases and PM emitted from agricultural sources are insufficient; thus, this paper presents a field monitoring study conducted from agricultural sources. To estimate the effect of precursor gases for PM2.5 from naturally ventilated dairy barns, correlation analyses were conducted using real-time monitoring data on the mass concentrations of PM2.5, NH3, SO2, NO2, and H2S and meteorological data. In addition to mass concentration, the emission and loading rates were used to closely analyze pollution status. The mass concentrations of PM2.5 and gaseous compounds did not correlate well, whereas the mass emission rates for PM2.5 and gaseous compounds (SO2, NH3, and NO2) correlated well because the unit of the emission rate reflected the ventilation factor. The correlation coefficients between PM2.5 and precursor gas emission rates ranged from 0.72 to 0.89 (R), with the SO2 emission rate exhibiting the highest correlation coefficient (R = 0.89). This correlation implies that SO2 from dairy farms is a dominant species among the gaseous precursors influencing the secondary formation of PM2.5; alternatively, SO2 and PM2.5 are produced from the same sources. The ambient PM2.5 loading rate and barn PM2.5 emission rate—estimated by multivariate linear regression using the gaseous independent variables NH3, SO2, and NO2—revealed high-correlation coefficients (0.60 and 0.92, respectively) with the measured data. At present, most studies investigating the precursor gases of PM in agricultural fields have focused on NH3; however, this study suggests that SO2 is a key factor in PM2.5 pollution. To elucidate the secondary formation of PM from precursor gases in agricultural sources, particulate ammonium, sulfate, nitrate, and chloride, which were not measured in this study, as well as oxidants and intermediates, should be considered in future research. [ABSTRACT FROM AUTHOR]

In this paper, we explore the computational capabilities of advanced modeling tools to reveal the factors that shape the observed benzene levels and behavior under different environmental conditions. The research was based on two-year hourly data concentrations of inorganic gaseous pollutants, particulate matter, benzene, toluene, m, p-xylenes, total nonmethane hydrocarbons, and meteorological parameters obtained from the Global Data Assimilation System. In order to determine the model that will be capable of achieving a superior level of performance, eight metaheuristics algorithms were tested for eXtreme Gradient Boosting optimization, while the relative SHapley Additive exPlanations values were used to estimate the relative importance of each pollutant level and meteorological parameter for the prediction of benzene concentrations. According to the results, benzene levels are mostly shaped by toluene and the finest aerosol fraction concentrations, in the environment governed by temperature, volumetric soil moisture content, and momentum flux direction, as well as by levels of total nonmethane hydrocarbons and total nitrogen oxide. The types of conditions which provided the environment for the impact of toluene, the finest aerosol, and temperature on benzene dynamics are distinguished and described. [ABSTRACT FROM AUTHOR]

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

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

High urbanization and a consequent change in land cover can lead to a deterioration in air quality and generate impacts on public health. The objective of this paper is to provide a multitemporal analysis of the influence of particulate matter ≤ 10 μm (PM10) on human mortality from the land cover variation in a Latin American megacity. Six monitoring stations (monitoring daily PM10 concentration, increases in daily mortality (IDM), and land cover) were established throughout the megacity. The results suggest that for every 10% increase in vegetation cover, the daily PM10 concentration and IDM decreases by 7.5 μg/m3 and 0.34%, respectively. Moreover, it is evident that the monitoring station with the lowest vegetation cover (8.96 times) shows an increase of 1.56 times and 4.8 times in the daily PM10 concentration and IDM, respectively, compared with the monitoring station with the highest vegetation cover (46.7%). It is also suggested that for each increase of 100 inhabitants/hectare in population density, the daily PM10 concentration and IDM increases by 9.99 µg/m3 and 0.45%, respectively. Finally, the population densification of the megacity possibly implies a loss of vegetation cover and contributes to the increase in PM10 and IDM. [ABSTRACT FROM AUTHOR]

Prior studies conducted within the realm of environmental psychology and public health have shown that air pollution exposure exerts significant effects on both physical and psychological well-being, especially for children. The roles of air pollution exposure are being increasingly recognized as essential factors influencing children's restoration. This systematic review provides an overview of existing knowledge of the impact of air pollution on children's mental health and restorative experience in both outdoor and indoor environments. A list of keywords for paper selection was derived from a systematic investigation of the literature on children's restorative environments. A total of 228 studies were initially identified, 18 of which met the eligibility criteria. This systematic review summarizes distinctive categories of air pollutants and discusses the assessments for both air pollution exposure and children's restorative outcomes. Additionally, 16 barriers in air pollution exposure to children's restorative experience were identified. The findings of this systematic review were concluded in an integrated framework, which have important implications for developing evidence-based and cross-disciplinary research on the air quality and children's restoration. [ABSTRACT FROM AUTHOR]

Cement is one of the most produced materials globally. Population growth and urbanization cause an increased demand for the cement needed for expanding infrastructures. As a result of this circumstance, the cement industry must find the optimum compromise between increasing cement production and reducing the negative environmental impact of that production. Since cement production uses a lot of energy, resources and raw materials, it is essential to assess its environmental impact and determine methods for the sector to move forward in sustainable ways. This paper uses an integrated life cycle assessment (LCA) and a system dynamics (SDs) model to predict the long-term environmental impact and future dynamics of cement production in South Africa. The first step used the LCA midpoint method to investigate the environmental impact of 1 kg of Portland cement produced in South Africa. In the cement production process, carbon dioxide (CO2), nitrogen oxides (NOx), sulphur dioxide (SO2), methane (CH4) and particulate matter (PM) were the major gases emitted. Therefore, the LCA concentrated on the impact of these pollutants on global warming potential (GWP), ozone formation, human health, fine particulate matter formation and terrestrial acidification. The system dynamics model is used to predict the dynamics of cement production in South Africa. The LCA translates its results into input variables into a system dynamics model to predict the long-term environmental impact of cement production in South Africa. From our projections, the pollutant outputs of cement production in South Africa will each approximately double by the year 2040 with the associated long-term impact of an increase in global warming. These results are an important guide for South Africa's future cement production and environmental impact because it is essential that regulations for cement production are maintained to achieve long-term environmental impact goals. The proposed LCA–SD model methodology used here enables us to predict the future dynamics of cement production and its long-term environmental impact, which is the primary research objective. Using these results, a number of policy changes are suggested for reducing emissions, such as introducing more eco-blended cement productions, carbon budgets and carbon tax. [ABSTRACT FROM AUTHOR]

There is a global trend toward intensive livestock breeding, which tends to increase the microbial load in the environment as well as the presence of volatile compounds and dust that can cause health issues. Cattle is the major producer of Escherichiacoli (E. coli), a group of foodborne bacteria associated with severe human diseases, and Neuquén province in Argentina has one of the highest rates of uremic hemolytic syndrome incidence in the world. This paper presents the results of two sampling events of E. coli bacteria at 39 sites in La Paisana ranch (LPR), in Añelo (Neuquén), considering locations inside the pens, upwind, and downwind of the feedlot with different time steps, using a Microflow α equipment. The ranch has approximately 600 heads and clean and controlled installations. The field experiment included sampling airborne aerosol deposition and concentration using passive and active methods. Concentrations were also estimated using an atmospheric dispersion model. During the field experiment, counts of up to 2970 CFU/m3 were obtained in the cattle stockyards and up to 111 CFU/m3 at a distance of 100 m. [ABSTRACT FROM AUTHOR]

Particulate matter PM2.5 pollution affects the Chinese population, particularly in cities such as Shenyang in northeastern China, which occupies a number of traditional heavy industries. This paper proposes a semi-supervised learning model used for predicting PM2.5 concentrations. The model incorporates rich data from the real world, including 11 air quality monitoring stations in Shenyang and nearby cities. There are three types of data: air monitoring, meteorological data, and spatiotemporal information (such as the spatiotemporal effects of PM2.5 emissions and diffusion across different geographical regions). The model consists of two classifiers: genetic programming (GP) to forecast PM2.5 concentrations and support vector classification (SVC) to predict trends. The experimental results show that the proposed model performs better than baseline models in accuracy, including 3% to 18% over a classic multivariate linear regression (MLR), 1% to 11% over a multi-layer perceptron neural network (MLP-ANN), and 21% to 68% over a support vector regression (SVR). Furthermore, the proposed GP approach provides an intuitive contribution analysis of factors for PM2.5 concentrations. The data of backtracking points adjacent to other monitoring stations are critical in forecasting shorter time intervals (1 h). Wind speeds are more important in longer intervals (6 and 24 h). [ABSTRACT FROM AUTHOR]

The melting behavior of glaciers on and around the Tibetan Plateau is strongly influenced by their albedo. In this paper, we report continuous observations made on the Qiangtang (QT) No. 1 Glacier, located in the central Tibetan Plateau, during its 2013–2015 melting seasons. Surface snow on the QT No. 1 Glacier mainly had a dust content less than 600 ppm and a black carbon (BC) content less than 10 ppb. A strong negative correlation was observed between albedo and dust content up to a threshold concentration of 1000 ppm, although albedo remained constant when dust concentrations increased above this value. The radii of snow particles showed a log-normal distribution that had a mean value of ~500 μm, but maximum and minimum values of 2539 μm and 40 μm, respectively. Snow density showed a normal distribution with a total range of 193–555 kg/m3, although most snow had a density of 400 kg/m3. Snow, ice, and aerosol radiative (SNICAR) simulations showed that dust and BC in the surface snow of the QT No. 1 Glacier reduced the snow and ice albedo by 5.9% and 0.06%, respectively, during the ablation season in 2015; however, the simulated particle impact was greater than the albedo reduction measured from field data. We interpret that dust has played a significantly more important role in melting of the QT No. 1 Glacier than BC over the study period, which is mainly due to the scarcity of human activities in the region and the low concentration of BC being produced. [ABSTRACT FROM AUTHOR]

In the present work, the data from the monitoring of the biologically active ultraviolet (UV) radiation of the Sun at the National Institute of Geophysics, Geodesy and Geography, Bulgarian Academy of Sciences for the period 2007–2021 were used. Based on the data analysis, a statistical method is proposed for determining the UV radiation index values in clear weather. As a result, estimated values of the UV-Index for each day of the year and for each hour are obtained, which can be used for predicting when reporting the cloud forecast. In the present paper, the applicability of the theoretical dependence of the UV-Index on the square of the cosine of the zenith angle of the Sun is investigated. The seasonal dependence of UV-Index values at fixed zenith angles of the Sun is discussed. Through regression analysis, the influence of the Total Ozone Column (TOC) and the concentration of particulate matter with d < 10 μm (PM10) on the UV-Index in the conditions of the city of Sofia was investigated. Explanations of the obtained results are offered. [ABSTRACT FROM AUTHOR]

Low-carbon cities have become a new trend in regional development around the world. Whether they can improve the environment in China, especially the air quality, remains to be tested. In this paper we take low-carbon city construction as a quasi-natural experiment and empirically test the net effects, influencing factors, and dynamic effects of low-carbon city construction on air quality by constructing a multistage propensity score matching and Difference-in-Differences model. After a series of robustness tests, the following conclusions are drawn: first, low-carbon city construction reduces the regional Air Quality Index, inhalable particulate matter, fine particulate matter, and NO2 concentrations. Among them, the construction effect in 2017 was the most significant. Therefore, it is necessary to continue to promote low-carbon city policies and accurately identify different types of air pollutants to improve the overall effectiveness of low-carbon city policies. Second, temperature, humidity, wind level, and other meteorological factors, as well as gross domestic product for the proportion of secondary industry, will affect air quality. Therefore, it is necessary to comprehensively consider meteorological, economic, social, and other influencing factors in an early stage of the construction of the next batch of low-carbon cities, so as to avoid falling into the trap of "building first and managing later". Third, the impact of secondary industry on air quality is significantly greater than that of tertiary industry. Therefore, the upgrading of industrial structure promoted by low-carbon city policy is effective in improving air quality. Fourth, the construction of low-carbon cities in western China has the most significant impact on air quality improvement. Therefore, the joint prevention and control mechanism of air pollution control in urban agglomeration should be established. [ABSTRACT FROM AUTHOR]

Haze is the phenomenon of visibility degradation caused by extinction effects related to the physicochemical properties of atmospheric particulate matter (APM). Atmosphere heterogeneous reactions can alter the physicochemical properties of APM. Therefore, it is important to understand the atmospheric heterogeneous reactions of APM in order to reveal the cause of haze. Herein, the current situation, developmental trend, source, and composition of APM pollution in China are reviewed. Additionally, we introduce the reaction characteristics and key chemical processes of common inorganic, organic, and mixed pollutant gases on the surface of mineral particles. The effects of mineral particulate matter on aggregation, regulation, and catalysis in the formation of atmospheric aerosols and the synergistic reaction mechanism of SO2, NO2, O3, and VOCs on the surfaces of different mineral particles are summarized. The problems existing in the current research on heterogeneous reactions on the surfaces of mineral particles are also evaluated. This paper aims to gain a deep understanding of the mechanism of mineral particulate matter promoting the formation of secondary aerosols and attempts to provide theoretical support for effective haze control. [ABSTRACT FROM AUTHOR]

This paper presents the results from field measurements and household surveys on the severity of indoor mold risk and its impact on respiratory health in a typical unplanned neighborhood of kampungs in Bandung, Indonesia. Mold risk was investigated using fungal risk detectors (n = 102), while air pollution levels were established with total suspended particulate (TSP) and particulate matter (PM2.5) (n = 38). The self-reported prevalence of respiratory diseases was obtained using a questionnaire form (ATS-DLD-78) (n = 599). The results showed that respiratory health problems were higher in the rainy season, particularly among children. Most houses suffered from severe mold risk, primarily due to extreme humid weather conditions, especially during rainy season (97%) where water leakage was prevalent (60%). In addition, the TSP and PM2.5 concentrations exceeded the WHO standards in most kampung houses, where around 58% of the houses recorded higher outdoor mean PM2.5 concentrations than indoors. Further, the path analysis showed that allergies followed by humidity rate and smell, which were affected by window-opening duration, directly impacted children's respiratory health. Smoking behavior and building-related health problems, due to exposure to outdoor air pollution, affected the respiratory health of those aged 15 years old and over. [ABSTRACT FROM AUTHOR]

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

The ventilation performance of air shafts is important to the air quality of subway tunnels, but there is no unified evaluation index of ventilation performance. In this paper, the air shafts at different locations in subway tunnels were taken as research objects, and the wind speed as well as the particulate matter concentration of each air shaft was tested. The effective ventilation volume and PM2.5 discharge efficiency of the air shafts were defined to evaluate the ventilation performance. It was found that on average, during the subway train service, the station air shaft on the train-arriving side can discharge 2050 m3 of dirty air in the tunnels and inhale 218 m3 of fresh air from the outside environment, while the station air shaft on the train-leaving side can absorb 2430 m3 of fresh air but can hardly effectively discharge dirty air; meanwhile, the middle air shaft can not only effectively exhaust 1519 m3 of dirty air but can also absorb 7572 m3 of fresh air. In addition, the middle air shaft has better ventilation performance if its inner opening is set on the top rather than on the side of the tunnel. The PM2.5 discharge efficiency of the station air shaft on the train-arriving side is 52.0~62.8%, higher than that of the middle air shaft of which the value is 26.8~40.7%. This research can provide guidance for ventilation performance evaluation of subway air shafts and provide a reference for subway tunnel air shaft location design. [ABSTRACT FROM AUTHOR]

Turkey is located in the heart of complex transition geography between Eurasia and the Middle East. In the grand scheme, the so-called eastern Mediterranean Basin is located almost in the middle of the dusty belt, and is a hot spot of climate change. The downstream location of dust-carrying winds from close desert sources reveals Turkey as an open plane to particulate matter exposure throughout the year. In order to clarify this phenomenon, this paper aims to determine the desert dust climatology of Turkey via CALIPSO onboard Lidar. This prominent instrument enables us to understand clouds, aerosols and their types, and related climatic systems, with its valuable products. In this study, a 9-year CALIPSO-derived pure dust product dataset was formed to explain horizontal and vertical distributions, transport heights and case incidences. The results indicated that the pure dust extinction coefficient increased as the location shifted from west to east. Moreover, in the same direction of west to east, the dominant spring months changed to summer and autumn. Mountain range systems surrounding Anatolia were the main obstacles against lofted and buoyant dust particles travelling to northern latitudes. Even if high ridges accumulated mass load on the southern slopes, they also enabled elevated particles to reach the ground level of the inner cities. [ABSTRACT FROM AUTHOR]

The fast development of large-scale intensive animal husbandry has led to an increased proportion of atmospheric pollution arising from livestock and poultry housing. Atmospheric pollutants, including particulate matter (PM), ammonia (NH3), hydrogen sulfide (H2S), and greenhouse gases (GHG), as well as other hazardous materials (e.g., gases, bacteria, fungi and viruses), have significant influences upon the local atmospheric environment and the health of animals and nearby residents. Therefore, it is imperative to develop livestock and poultry housing mitigation strategies targeting atmospheric pollution, to reduce its negative effects on the ambient atmosphere and to promote sustainable agricultural production. In this paper, we summarize the various strategies applied for reducing outlet air pollutants and purifying inlet air from mechanical ventilated livestock and poultry housing. This review highlights the current state of knowledge on the removal of various atmospheric pollutants and their relative performance. The potential optimization of processes and operational design, material selection, and other technologies, such as electrostatic spinning, are discussed in detail. The study provides a timely critical analysis to fill the main research gaps or needs in this domain by using practical and stakeholder-oriented evaluation criteria. [ABSTRACT FROM AUTHOR]

Human health is strongly affected by the concentration of fine particulate matter (PM2.5). The need to forecast unhealthy conditions has driven the development of Chemical Transport Models such as Community Multi-Scale Air Quality (CMAQ). These models attempt to simulate the complex dynamics of chemical transport by combined meteorology, emission inventories (EI's), and gas/particle chemistry and dynamics. Ultimately, the goal is to establish useful forecasts that could provide vulnerable members of the population with warnings. In the simplest utilization, any forecast should focus on next day pollution levels, and should be provided by the end of the business day (5 p.m. local). This paper explores the potential of different approaches in providing these forecasts. First, we assess the potential of CMAQ forecasts at the single grid cell level (12 km), and show that significant variability not encountered in the field measurements occurs. This observation motivates the exploration of other data driven approaches, in particular, a neural network (NN) approach. This approach makes use of meteorology and PM2.5 observations as model predictors. We find that this approach generally results in a more accurate prediction of future pollution levels at the 12 km spatial resolution scale of CMAQ. Furthermore, we find that the NN is able to adjust to the sharp transitions encountered in pollution transported events, such as smoke plumes from forest fires, more accurately than CMAQ. [ABSTRACT FROM AUTHOR]

In recent years, main cities in China have been suffering from hazy weather, which is gaining great attention among the public, government managers and researchers in different areas. Many studies have been conducted on the topic of urban air quality to reveal different aspects of the air quality problem in China. This paper focuses on the visualization problem of the big air quality monitoring data of all main cities on a nationwide scale. To achieve the intuitive visualization of this dataset, this study develops two novel visualization tools for multi-granularity time series visualization (timezoom.js) and a dynamic symbol declutter map mashup layer for thematic mapping (symadpative.js). With the two invented tools, we develops an interactive web map visualization application of urban air quality data of all main cities in China. This application shows us significant air pollution findings at the nationwide scale. These results give us clues for further studies on air pollutant characteristics, forecasting and control in China. As the tools are invented for general visualization purposes of geo-referenced time series data, they can be applied to other environmental monitoring data (temperature, precipitation, etc.) through some configurations. [ABSTRACT FROM AUTHOR]