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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]

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]

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]

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]

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 concentration series of PM2.5 (particulate matter ≤ 2.5 μm) is nonlinear, nonstationary, and noisy, making it difficult to predict accurately. This paper presents a new PM2.5 concentration prediction method based on a hybrid model of complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and bi-directional long short-term memory (BiLSTM). The new method was applied to predict the same kind of particulate pollutant PM10 and heterogeneous gas pollutant O3, proving that the prediction method has strong generalization ability. First, CEEMDAN was used to decompose PM2.5 concentrations at different frequencies. Then, the fuzzy entropy (FE) value of each decomposed wave was calculated, and the near waves were combined by K-means clustering to generate the input sequence. Finally, the combined sequences were put into the BiLSTM model with multiple hidden layers for training. We predicted the PM2.5 concentrations of Seoul Station 116 by the hour, with values of the root mean square error (RMSE), the mean absolute error (MAE), and the symmetric mean absolute percentage error (SMAPE) as low as 2.74, 1.90, and 13.59%, respectively, and an R2 value as high as 96.34%. The "CEEMDAN-FE" decomposition-merging technology proposed in this paper can effectively reduce the instability and high volatility of the original data, overcome data noise, and significantly improve the model's performance in predicting the real-time concentrations of PM2.5. [ABSTRACT FROM AUTHOR]

Ambient pollen proteins play key roles in the incidence of allergenic respiratory health, and numerous reports have focused on respiratory diseases caused by air pollutants. However, there is still a lack of understanding of the specific mechanisms underlying the involvement of microbiota in the respiratory tracts and effects induced by air pollutants. Therefore, an allergenic animal model was established to investigate the characterization of microbials in the lung induced by allergenic Platanus pollen protein (Pla a3) and ambient fine particulate matter. Our data showed that the mice exhibited strong immune and inflammatory responses after being exposed to PMs and Pla a3 protein. This included increased levels of immunoglobulins IgG and IgE, as well as elevated levels of cytokines TNF-α, IFN-γ, IL-4, and IL-13. Furthermore, the amounts of pathogenic bacteria, such as Desulfobacterota, Enterococcus, Ferruginibacter, and Pseudoxanthomonas, in the lung microbiota of the Pla a3 exposure group increased significantly. Correlation analysis revealed a strong association between specific lung bacteria and alterations in cytokines from the lung samples. Probiotic bacteria, Deferribacterota and Bifidobacterium, was associated with changes in the level of IgG and IgE. However, pathogenic bacteria, like Prevotella and Fusobacterium, were linked with the cytokines IL-4 and TNF-α. [ABSTRACT FROM AUTHOR]

Hybrid receptor models overestimate the contribution of background areas (no specific emission sources), like the Yellow Sea in Korea. This study aimed to improve model performances using Advanced Concentration Emission and Retention Time Weighted Trajectory (ACERWT). ACERWT was combined with a positive matrix factorization (PMF), back trajectory, and Regional Emission Inventory in Asia (REAS). The PMF receptor model used one year of data from Korea's Central Air Environment Research Center. In the PMF receptor model, eight sources (dust/soil, secondary nitrate, biomass burning, vehicles, secondary sulfate, industry, coal combustion and sea salt) influenced PM2.5 pollution at the receptor site (Daejeon, Korea). Secondary sulfate was the most dominant source, followed by secondary nitrate and vehicle sources. ACERWT results showed high contributions from China, Japan, and Korean regions, while the contribution from the Yellow Sea was significantly lower. Several regions, such as the eastern and south-eastern areas of China, the southern area of Taiwan, the western area of Tokyo, and the central area of Korea, showed high contributions due to large-scale emission facilities and industrial complexes. In this study, the ACERWT model significantly improved its performance regarding regional contributions to PM2.5 pollution at the receptor site. [ABSTRACT FROM AUTHOR]

Brake wear particles are generated through frictional contact between the brake disc or brake drum and the brake pads. Some of these particles may be released into the atmosphere, contributing to airborne fine particulate matter (PM2.5). In this study, an onboard system was developed and tested to measure brake wear particles emitted under real-world driving conditions. Brake wear particles were extracted from a fixed volume enclosure surrounding the pad and disc installed on the front wheel of a light-duty vehicle. Real-time data on size distribution, number concentration, PM2.5 mass, and the contribution of semi-volatiles were obtained via a suite of instruments sub-sampling from the constant volume sampler (CVS) dilution tunnel. Repeat measurements of brake particles were obtained from a 42 min bespoke drive cycle on a chassis dynamometer, from on-road tests in an urban area, and from braking events on a test track. The results showed that particle emissions coincided with braking events, with mass emissions around 1 mg/km/brake during on-road driving. Particle number emissions of low volatility particles were between 2 and 5 × 109 particles/km/brake. The highest emissions were observed under more aggressive braking. The project successfully developed a proof-of-principle measurement system for brake wear emissions from transient vehicle operation. The system shows good repeatability for stable particle metrics, such as non-volatile particle number (PN) from the solid particle counting system (SPCS), and allows for progression to a second phase of work where emissions differences between commercially available brake system components will be assessed. [ABSTRACT FROM AUTHOR]

Accurate air quality modelling is an essential tool, both for strategic assessment (regulation development for emission controls) and for short-term forecasting (enabling warnings to be issued to protect vulnerable members of society when the pollution levels are predicted to be high). Model intercomparison studies are a valuable support to this work, being useful for identifying any issues with air quality models, and benchmarking their performance against international standards, thereby increasing confidence in their predictions. This paper presents the results of a comparison study of six chemical transport models which have been used to simulate short-term hourly to 24 hourly concentrations of fine particulate matter less than and equal to 2.5 µm in diameter (PM2.5) and ozone (O3) for Sydney, Australia. Model performance was evaluated by comparison to air quality measurements made at 16 locations for O3 and 5 locations for PM2.5, during three time periods that coincided with major atmospheric composition measurement campaigns in the region. These major campaigns included daytime measurements of PM2.5 composition, and so model performance for particulate sulfate (SO42−), nitrate (NO3−), ammonium (NH4+) and elemental carbon (EC) was evaluated at one site per modelling period. Domain-wide performance of the models for hourly O3 was good, with models meeting benchmark criteria and reproducing the observed O3 production regime (based on the O3/NOx indicator) at 80% or more of the sites. Nevertheless, model performance was worse at high (and low) O3 percentiles. Domain-wide model performance for 24 h average PM2.5 was more variable, with a general tendency for the models to under-predict PM2.5 concentrations during the summer and over-predict PM2.5 concentrations in the autumn. The modelling intercomparison exercise has led to improvements in the implementation of these models for Sydney and has increased confidence in their skill at reproducing observed atmospheric composition. [ABSTRACT FROM AUTHOR]

In the summer of 2018, Sweden experienced widespread wildfires, particularly in the region of Jämtland Härjedalen during the final weeks of July. We previously conducted an epidemiological study and investigated acute respiratory health effects in eight municipalities relation to the wildfire air pollution. In this study, we aimed to estimate the potential health impacts under less favorable conditions with different locations of the major fires. Our scenarios focused on the most intense plume from the 2018 wildfire episode affecting the largest municipality, which is the region's only city. Combining modeled PM2.5 concentrations, gridded population data, and exposure–response functions, we assessed the relative increase in acute health effects. The cumulative population-weighted 24 h PM2.5 exposure during the nine highest-level days reached 207 μg/m3 days for 63,227 inhabitants. We observed a small number of excess cases, particularly in emergency unit visits for asthma, with 13 additional cases compared to the normal 12. Overall, our scenario-based health impact assessment indicates minor effects on the studied endpoints due to factors such as the relatively small population, limited exposure period, and moderate increase in exposure compared to similar assessments. Nonetheless, considering the expected rise in fire potential due to global warming and the long-range transport of wildfire smoke, raising awareness of the potential health risks in this region is important. [ABSTRACT FROM AUTHOR]

Air pollution in the Metropolitan Area of São Paulo (MASP), Brazil, is a serious problem and is strongly affected by local sources. However, atmosphere column composition in MASP is also affected by biomass burning aerosol (BB). Understanding the impacts of aerosol particles, from both vehicles and BB, on the air quality and climate depends on in-depth research with knowledge of some parameters such as the optical properties of particles and their chemical composition. This study characterized fine particulate matter (PM2.5) from July 2019 to August 2020 in the eastern part of the MASP, relating the chemical composition data obtained at the surface and columnar optical parameters, such as aerosol optical depth (AOD), Ångström Exponent (AE), and single-scattering albedo (SSA). According to the analyzed data, the mean PM2.5 concentration was 18.0 ± 12.5 µg/m3; however, daily events exceeded 75 times the air quality standard of the World Health Organization (15 µg/m3). The mean black carbon concentration was 1.8 ± 1.5 µg/m3 in the sampling period. Positive matrix factorization (PMF) identified four main sources of aerosol: heavy vehicles (42%), followed by soil dust plus local sources (38.7%), light vehicles (9.9%), and local sources (8.6%). AOD and AE presented the highest values in the dry period, during which biomass burning events are more frequent, suggesting smaller particles in the atmosphere. SSA values at 440 nm were between 0.86 and 0.94, with lower values in the winter months, indicating the presence of more absorbing aerosol. [ABSTRACT FROM AUTHOR]

This study investigated the sensitivity of the positive matrix factorization (PMF) model using concentrations of PM2.5-bound elements in Windsor, Ontario, Canada. Five scenarios were devised to assess impacts of input data on source identification, source contributions, and model performance. The study found that the model outcomes and performance were not sensitive to data below method detection limits (MDLs) being replaced with ½ MDLs, nor whether brown carbons (BrCs) data were excluded. By analyzing two episodic events individually, unique factors of fireworks and mineral dust were identified for each of the two episodes. Moreover, PMF model performance was improved greatly for event markers of the episodes and elements with less variability in concentration when compared with the base case scenario. Excluding the two episodes from the entire dataset had little impact on factor identification and source contributions but improved the model performance for three out of twelve elements unique to the two episodes. Overall, the PMF model outcomes and performance were sensitive to percentages of concentrations below MDLs and element concentrations with large variability due to high concentrations observed in episodes. Our findings are useful for dealing with data below MDLs and episodic events in conducting future PMF source apportionment of PM2.5-bound elements. [ABSTRACT FROM AUTHOR]

Particulate matter (PM) pollution is a crucial environmental issue. Considering its adverse health impacts, especially on children's immune systems, Korean regulations require annual PM2.5 measurements in daycare centers. Therefore, we developed a low-cost PM2.5 sensor calibration model for measuring the indoor PM concentrations in daycare centers using long short-term memory (LSTM) algorithms. Moreover, we trained the model to predict the PM2.5 based on temperature and humidity, and optimized its hyperparameters. The model achieved a high accuracy and outperformed traditional calibration methods. The optimal lookback period was 76, which led to a high calibration performance with root mean and mean squared errors, a coefficient of determination, and mean absolute errors of 3.57 and 12.745, 0.962, and 2.7, respectively. The LSTM model demonstrated a better calibration performance than those of the linear (r2 = 0.57) and multiple (r2 = 0.75) linear regression models. The developed calibration model provided precise short-term measurement values for the optimal management of indoor PM concentrations. This methodology can be applied to similar environments to obtain new learning and hyper-parameters. Our results will aid in improving the accuracy of low-cost sensors for measuring indoor PM concentrations, thereby providing cost-effective solutions for enhancing children's health and well-being in daycare centers and other multiuse facilities. [ABSTRACT FROM AUTHOR]

Air pollution has a strong impact on human health, from respiratory and severe pulmonary diseases to heart attack and cancer. During the 3 years of the COVID-19 pandemic, several peaks of mortality occurred, which could be related to particulate matter (PM) pollution events. The possible effects of PM (PM10 and PM2.5, with diameters less than 10 and 2.5 µm, respectively) on COVID-19 mortality have now been established. To better understand this relationship at the European level for the period 2020–2022, data from 16 representative locations in Europe (81 million people) with PM2.5 levels (µg·m−3) ranging from low to high values were analyzed using statistical methods. The analysis confirms a temporal relation between the peaks of PM2.5 exposure and COVID-19 mortality. The best correlation was obtained considering the history of exposure to PM2.5 pollution during a 2-month integration time coupled with a one-week delay for the COVID-19 mortality. Although the trend of COVID-19 mortality vs. PM2.5 levels varies among locations, the global trend was similar, giving an estimated mean value of a 40 ± 20% mortality increase per µg·m−3 PM2.5 increase. The stronger the positive (negative) gradient of the PM peak, the stronger the positive (negative) gradient of the COVID-19 mortality. These results indicate that a succession of PM pollution peaks could be more dangerous than permanent exposure to moderate pollution levels. Finally, PM number concentrations should be used in the future rather than the PM2.5 mass concentrations (µg·m−3), with the consideration of PM composition to better evaluate the effect of submicron particles on human health, particularly for other respiratory diseases. These results must be considered in the management of future pandemics. [ABSTRACT FROM AUTHOR]

Monitoring particulate matter with aerodynamic diameters of less than 2.5 μm (PM2.5) is of great importance to assess its adverse effects on human health, especially densely populated regions. In this paper, an improved linear mixed effect model (LMEM) was developed. The model introduced meteorological variable, column water vapor (CWV), which has as the same resolution as satellite-derived aerosol optical thickness (AOT), to enhance PM2.5 estimation accuracy by considering spatiotemporal consistency of CWV and AOT. The model was implemented to urban agglomeration of Chengdu Plain during 2015. The results show that model accuracy has been improved significantly compared to linear regression model (R2 = 0.49), with R2 of 0.81 and root mean squared prediction error (RMSPE) of 15.47 μg/m3, mean prediction error (MPE) of 11.09 μg/m3, and effectively revealed the characteristics of spatiotemporal variations PM2.5 level across the study area: The PM2.5 level is higher in the central and southern areas with dense population, while it is lower in the northwest and southwest mountain areas; and the PM2.5 level is higher during autumn and winter, while it is lower during spring and summer. The product data in this paper are valuable for local government pollution monitoring, public health research, and urban air quality control. [ABSTRACT FROM AUTHOR]

While the district of Mae Moh, Thailand has been well known for its atmospheric pollution associated with coal power production, economic assessment of demand for improved air quality has not been conducted to date. This study estimated local residents' individual and aggregate willingness to pay (WTP) for mitigation of atmospheric pollution in Mae Moh using the contingent valuation method (CVM), and analyzed the factors associated with the individual WTP using the bivariate tobit and double-hurdle regression techniques. Primary data were collected through face-to-face interviews with a stratified sample of 200 residents. The hypothetical scenarios used in the CVM module were 50% and 80% mitigation of atmospheric concentrations of major pollutants. The weighted average WTP was found to be THB 251.3 and 307.9 per annum (USD 8.4 and 10.3) for the 50% and 80% reduction scenarios, respectively. The aggregate WTP for the entire population of Mae Moh was THB 10,008,733 and 12,264,761 per annum (USD 336,294 and 412,096), respectively. Education, occupation type, income, expenses, satisfaction with ambient quality, and perceived sources of pollution had significant associations with the individual WTP. The paper concludes by discussing policy implications for atmospheric pollution management and avenues for future research. [ABSTRACT FROM AUTHOR]

A reduction in gaseous pollutants is an important method for mitigating PM2.5 concentration in the atmosphere, and the reduction in SO2/NH3/NOx is beneficial to control secondary inorganic aerosols in PM2.5. In this study, the Weather Research and Forecasting model with Chemistry model (WRF-Chem) was applied to study the impact on the PM2.5 and its secondary inorganic aerosols using the scenario simulation method in the Beijing–Tianjin–Hebei (BTH) region. The results showed that the BTH region is characterized by being NH3-rich and having a higher [NH4+]/[SO42−] ratio in southern BTH, with a ratio of more than 6.0. Source contribution to PM2.5 was highest in the 30%_SO2_40%_NH3_40%_NOx scenario, with a contribution ratio of 6.8%, followed by 3.8% contribution in the 30%_SO2_40%_NH3 scenario, and a 3.4% contribution in the 30%_SO2_60%_NH3_60%_NOx scenario. These results indicate that synergistic reduction measures may be suitable for controlling PM2.5 concentrations. A lower sensitivity factor, β value between PM2.5 and NH3 suggests that solely reducing NH3 emissions is not beneficial for the BTH region. However, this study indicates that the sensitivity of NO3− would improve significantly if NH3 emissions are reduced sharply. A slight reduction in NH3 was found to be beneficial for controlling NO3− in medium and small cities, while a significant decrease in NH3 would be more suitable for mega-cities. The study also observed that SO42− and its constituents continued to decrease with a consistent β value of approximately 0.14 in the 30%_SO2_%_NH3 scenario and between 10.5 and 12.8 in the 30%_SO2_%_NH3_%_NOx scenario. These findings suggest that a synergistic reduction in SO2-NH3-NOx emissions may be more effective in reducing PM2.5 concentrations and its secondary inorganic aerosols (SIAs). However, it is important to ensure that the reduction in NH3 and NOx exceeds 60% in low SO2 concentration conditions. [ABSTRACT FROM AUTHOR]

Ambient air pollution remains one of the most important risk factors for health outcomes. In recent years, there has been a growing number of research linking particulate matter (PM) exposure with adverse health effects, especially on cardiovascular and respiratory systems. The objective of this review is to examine the range and nature of studies on time series analysis of health outcomes affected by PM2.5 across a broad research area. A literature search was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) extension for scoping review framework through a strategic search of PubMed and ScienceDirect online databases for articles from January 2016 to January 2021. Articles were first screened by their titles and abstracts. Then two reviewers independently reviewed and evaluated the full text of the remaining articles for eligibility. Of the 407 potentially relevant studies, 138 articles were included for final analysis. There was an increasing trend in publications from 2016 to 2019 but a decreasing trend in the year 2020. Most studies were conducted in Eastern and South-Eastern Asia (69.6%), Europe and Northern America (14.5%) and Latin America and the Caribbean (8.7%), with the majority coming from high- and upper-middle-income countries (95.6%). The main methodology used was Generalized Additive Model (GAM) with Poisson distribution (74.6%). Morbidity was the most common health outcome studied (60.1%), with vulnerable groups (64.5%) often included. The association between PM2.5 and health effects was stronger for respiratory diseases compared to cardiovascular diseases. In short-term studies (less than 7 years), respiratory diseases showed higher risks compared to cardiovascular. However, in long-term studies (7 years and more), cardiovascular showed higher risks. [ABSTRACT FROM AUTHOR]

Air quality in Europe continues to remain poor in many areas, with regulation limits often exceeded by many countries. The EU Life-IP PREPAIR Project, involving administrations and environmental protection agencies of eight regions and three municipalities in Northern Italy and Slovenia, was designed to support the implementation of the regional air quality plans in the Po Valley, one of the most critical areas in Europe in terms of pollution levels. In this study, four air quality modelling systems, based on three chemical transport models (CHIMERE, FARM and CAMx) were applied over the Po Valley to assess the sensitivity of PM2.5 concentrations to NOx and NH3 emission reductions. These two precursors were reduced (individually and simultaneously) from 25% up to 75% for a total of 10 scenarios, aimed at identifying the most efficient emission reduction strategies and to assess the non-linear response of PM2.5 concentrations to precursor changes. The multi-model analysis shows that reductions across multiple emission sectors are necessary to achieve optimal results. In addition, the analysis of non-linearities revealed that during the cold season, the efficiency of PM2.5 abatement tends to increase by increasing the emission reductions, while during summertime, the same efficiency remains almost constant, or slightly decreases towards higher reduction strengths. Since the concentrations of PM2.5 are greater in winter than in summer, it is reasonable to infer that significant emission reductions should be planned to maximise reduction effectiveness. [ABSTRACT FROM AUTHOR]

In 2020, when the novel coronavirus disease 2019 (COVID-19) broke out as a global pandemic, cities in Hubei Province first went into lockdown on 23 January and resumed work and production on 20 March. From February to March 2020, human activities in Hubei decreased significantly, with the average particulate matter smaller than 2.5 μm (PM2.5) concentration standing at 40 μg/m3, which is 21% lower than the expected based on a linear fitting trend in thePM2.5 concentration in Hubei. By using the empirical orthogonal function (EOF) method, this paper comparatively analyzes the spatial-temporal variations of Hubei's PM2.5 concentration anomaly in February and March 2020 and the same periods of 2016–2019. The results show that the daytime peak of the PM2.5 daily variation in Hubei in a low-emission scenario during COVID-19 declined significantly, to which human activities contributed the most. However, during nighttime, the PM2.5 peak became more prominent, and the meteorological conditions had a more noticeable effect on the PM2.5 concentration. In addition, during COVID-19, there was a great drop in PM2.5 pollution accumulated from local sources within the urban circle of Wuhan City, while an increase was seen in central-western Hubei due to the inter-regional pollutant transport. Thus, the high PM2.5 concentration center in the urban circle of Wuhan disappeared, but the pollution transport channel cities in central-western Hubei remained as high-PM2.5-concentration centers. [ABSTRACT FROM AUTHOR]

Exhaust regulations and improved exhaust gas treatment systems have already initiated the trend that brings emissions from brakes and tires to the forefront of traffic-induced particulate matter. The health and environmental relevance of particulate matter has resulted in regulators, industry, and research institutions prioritising the mitigation of non-exhaust particle emissions. To this end, under the umbrella of the United Nations Economic Commission for Europe World Forum for Harmonisation of Vehicle Regulations (UNECE WP.29), the Working Party on Pollution and Energy (GRPE) mandated the Particle Measurement Programme Informal Working Group (PMP-IWG) to develop a Global Technical Regulation (GTR) for measuring brake dust. The standards and procedures defined within the GTR should eventually form the basis for the introduction of a Euro 7 limit value for brake emissions. The purpose of this measurement campaign is to provide an exemplary overview of the emission behaviour of wheel brakes and friction pairings currently available on the market and to identify possible reduction potential with regard to particulate emissions. All measurements were carried out taking into account the draft GTR valid at the time of execution. For the investigations, brakes were selected using the example of different vehicle classes, brake concepts (disc and drum brake), vehicle axles (front and rear axle), and alternative friction materials (brake disc and pads/shoes). Thus, the use of wear-resistant discs and improved brake pad compositions are able to achieve significantly lower emissions. In addition, the measurement of brake dust emissions from vehicles with different levels of electrification was considered. Electrical braking was modelled and applied to the Worldwide Harmonised Light-Duty Vehicles Test Procedure (WLTP) Brake Cycle, which has demonstrated high emission reduction potentials depending on the electrification level. [ABSTRACT FROM AUTHOR]

Handan is a typical city affected by regional particulate pollution. In order to investigate particulate matter (PM) characterization, source contributions and health risks for the general populations, we collected PM samples at two sites affected by a pollution event (12–18 May 2020) during the COVID-19 pandemic and analyzed the major components (SNA, OCEC, WSIIs, and metal elements). A PCA-MLR model was used for source apportionment. The carcinogenic and non-carcinogenic risks caused by metal elements in the PM were assessed. The results show that the renewal of old neighborhoods significantly influences local PM, and primarily the PM10; the average contribution to PM10 was 27 μg/m3. The source apportionment has indicated that all other elements came from dust, except Cd, Pb and Zn, and the contribution of the dust source to PM was 60.4%. As PM2.5 grew to PM10, the PM changed from basic to acidic, resulting in a lower NH4+ concentration in PM10 than PM2.5. The carcinogenic risk of PM10 was more than 1 × 10−6 for both children and adults, and the excess mortality caused by the renewal of the community increased by 23%. Authorities should pay more attention to the impact of renewal on air quality. The backward trajectory and PSCF calculations show that both local sources and short-distance transport contribute to PM—local sources for PM10, and short-distance transport in southern Hebei, northern Henan and northern Anhui for PM2.5, SO2 and NO2. [ABSTRACT FROM AUTHOR]

Several studies have identified a relationship between air pollution and depression, particularly in relation to fine particulate matter (PM2.5) exposure. However, the strength of this association appears to be moderated by variables such as age, gender, genetic vulnerability, physical activity, and climatic conditions, and has not been assessed at a cross-national level to date. Moreover, certain studies in this field have yielded negative results, and there are discrepancies between the results obtained in high-income countries and those from low- and middle-income countries. The current study examines cross-sectional and longitudinal associations between the incidence of depression in each country, based on Global Burden of Disease Study data, and the average national level of PM2.5 based on the World Health Organization's database, over the past decade (2010–2019). The observed associations were adjusted for age, gender, level of physical activity, income, education, population density, climate, and type of depression. It was observed that while PM2.5 levels showed significant cross-sectional associations with the incidence of depression, longitudinal analyses were not suggestive of a direct causal relationship. These findings are discussed in the light of recent contradictory results in this field, and the need to consider the intermediate roles of a number of individual and environmental factors. [ABSTRACT FROM AUTHOR]

This study aims to determine emission source areas of fine particulate matter (PM2.5) in Ho Chi Minh (HCM) City, Vietnam, using a conditional bivariate probability function (CBPF) and hybrid receptor models, including three-dimensional potential source contribution function (3D-PSCF) and concentration-weighted trajectory (3D-CWT), considering latitudes, longitudes, and height of trajectory segments. Uncertainties of the CBPF and 3D-PSCF/3D-CWT were evaluated based on the 95th confidence intervals and 95% confidence levels, respectively. For the local scale, PM2.5 in HCM City was primarily emitted from shallow or common ground sources (e.g., vehicle emissions) throughout the year. Regarding non-local source areas, PM2.5 in HCM City is contributed by those originated from the East Sea (e.g., shipping emissions) and southeastern Vietnam (e.g., Binh Duong and Dong Nai provinces) having several industrial zones with PM2.5 emission sources, especially in the dry season (December to April of the following year). In the rainy season (May–November), PM2.5 derived from emission sources in the Mekong Delta (e.g., biomass burning) might be transported to HCM City. However, contribution of the non-local sources to PM2.5 pollution in HCM City during the rainy season is less important because of PM2.5 deposition stemmed from the high rainfall amount in this season. [ABSTRACT FROM AUTHOR]

Studies on air quality frequently adopt clustering, in particular the k-means technique, owing to its simplicity, ease of implementation and efficiency. The aim of the present paper was the assessment of air quality in a winter season (December–February) in the conditions of temperature inversion using the k-means method, representing a non-hierarchical algorithm of cluster analysis. The air quality was assessed on the basis of the concentrations of particulate matter (PM10, PM2.5). The studies were conducted in four winter seasons (2015/16, 2016/17, 2017/18, 2019/20) in Wrocław (Poland). As a result of the application of the v-fold cross test, six clusters for each fraction of PM were identified. Even though the analysis covers only four winter seasons, the applied method has unequivocally revealed that the characteristics of surface-based (SBI) and elevated inversions (ELI) affect the concentration level of both fractions of particulate matter. In the case of PM10, the average lowest daily concentration (15.5 µg·m−3) was recorded in the conditions of approx. 205 m in thickness, 0.5 °C intensity of the SBI and at the height of the base of the ELI at approx. 1700 m a.g.l., a thickness of 148 m and an intensity of 1.2 °C. In turn, the average highest concentration (136 µg·m−3) was recorded at a thickness of SBI of approx. 400 m and an intensity of 1.4 °C. Such high concentration occurred when the lowest location of ELI formed at 764 m a.g.l. with a thickness of 308 m and an intensity of 0.96 °C. A marked role of the thickness of the SBI and ELI as well as the height of the base of the lowest location of ELI was also manifested with respect to PM2.5 concentrations. [ABSTRACT FROM AUTHOR]

Beijing, the capital city of China, has achieved remarkable progress in terms of an improvement in air quality under strict control policies in the past 10 years from various sources. In this paper, the characteristics of fine particulate matter (PM2.5) and O3 in January 2013 and 2018 in Beijing are discussed on the basis of daily sample analysis and hourly monitoring data. It was found that the PM2.5 pollution for the month of January in Beijing has been greatly curbed. The SO42− concentration and proportion of PM2.5 decreased, while the proportions of NO3− and NH4+ increased. Organic matter represented the major component during the two periods with the proportions of 31.7% ± 8.2% and 31.4% ± 9.8%. The results of the Hybrid Single Particle Lagrangian Integrated Trajectory (Hysplit) model and Potential Source Contribution Function (PSCF) method showed that air mass from southern nearby regions accounted for 34% and 10% in 2013 and 2018, respectively, which was closely related to the pollution period. Thus, the input direction of air mass in January 2018 was more conducive to the diffusion of pollutants. Modeling results of the Weather Research and Forecasting model (WRF) coupled with Comprehensive Air Quality Model Extensions (CAMx) indicated that the contribution of industry sources to PM2.5 and O3 decreased from 2013 to 2018, while mobile sources increased. This was mainly due to the different control policies on various emission sources. In terms of O3 sources, more control measurements should be taken on volatile organic compounds (VOCs) due to its prominent effect on O3 concentration in both periods. The reduction in emissions and the meteorological conditions both contributed effectively to the sharp decrease in PM2.5 concentration. However, the change in weather conditions had the greater impact on the decrease in PM2.5 concentration, while the reduction in emissions was weakened as a function of this change. [ABSTRACT FROM AUTHOR]

It is well documented that Southern Poland is one of the most polluted areas in Europe due to the highest airborne concentrations of particulate matter (PM). Concentrations of fine particles are especially high in winter. Apart from detailed number concentrations, it is essential to accurately identify and quantify specific particulate pollution sources. Only a few Polish research centers are involved in such experiments—among them is Krakow research group. For the most part, research focuses on collecting 24-h average samples from stationary PM samplers at ambient monitoring sites and quantifying the specific elements and chemical constituents in PM. This approach includes modeling methods that can use the variability in physical and chemical PM characteristics as an input dataset to identify possible sources of the particles. The objective of this paper is to provide research results based on data collected from June 2018 to May 2019 from a single monitoring station at a central urban site. Careful comparison of data obtained prior to a 2019 law prohibiting solid fuel burning in the city of Krakow with data (2019–2020) when a regulation went into effect should indicate progress by noting lower PM levels. This work has shown that the method applied and Krakow results might be of interest to the broader community in regions of high PM concentration. [ABSTRACT FROM AUTHOR]

This study aims to develop a second order self-organizing fuzzy neural network (SOFNN) to predict the hourly concentrations of fine particulate matter (PM2.5) for the next 24 h at a regional background station called Shangdianzi (SDZ) in China from 14 to 23 January 2010. The structure of the SOFNN was automatically adjusted according to the sensitivity analysis (SA) of model output and the parameter-learning phase was performed applying a second order gradient (SOG) algorithm. Principal component analysis (PCA) was employed to select the dominating factors for PM2.5 concentrations as the input variables for the SOFNN. It was found that the dominating variables (relative humidity (RH), pressure (Pre), aerosol optical depth (AOD), wind speed (WS) and wind direction (WD)) extracted by PCA agreed well with the characteristics of PM2.5 at SDZ where the PM2.5 concentrations were heavily affected by meteorological parameters and were closely related to AOD. The forecasting results showed that the proposed SOG-SASOFNN performed better than other models with higher coefficient of determination (R2) during both training phase and test phase (0.89 and 0.84, respectively) in predicting PM2.5 concentrations at SDZ. In conclusion, the developed SOG-SASOFNN provided satisfying results for modeling the hourly distribution of PM2.5 at SDZ during the studied period. [ABSTRACT FROM AUTHOR]

Emerging evidence suggests a possible link between exposure to household air pollution (HAP) from a reliance on polluting solid fuels (SFs) (e.g., wood and charcoal) for cooking and high blood pressure. As part of the CLEAN-Air(Africa) project, we measured the blood pressure among 350 cooks in Obuasi Municipality, Ghana after 24 h exposure to particulate matter (PM2.5) from the combustion of either solid fuels (n = 35) or liquefied petroleum gas (LPG) (n = 35). Multinomial regression models were used to describe the relationship between different stages of blood pressure (mmHg) and the respondents' main fuel type used, adjusting for key covariates. A linear regression model was used to describe the relationship between personal exposure to PM2.5 and the respondent's systolic as well as diastolic blood pressure, adjusting for key covariates. Blood pressure was higher in cooks using SFs for cooking than in those using LPG. A significant exposure–response relationship was not observed between increasing exposure to PM2.5 and increasing blood pressure (systolic: β = −2.42, 95% CI: −8.65, 3.80, p-value = 0.438, and diastolic: β = −0.32, 95% CI: −5.09; 4.45, p-value = 0.893). [ABSTRACT FROM AUTHOR]

The pervasiveness of "stacking" between traditional and clean fuels/stoves is moving the household clean cooking research and policy agenda from programs that are centered on one-fuel or stove, to multi-clean fuel/device interventions that could result in a more effective displacement of traditional biomass stoves. However, there is little recognition and knowledge of the benefits realistic clean-stacking cooking solutions can have on indoor air quality and health. In this paper, particulate matter (PM2.5) and carbon monoxide (CO) concentration levels that are associated with stove-stacking options (Patsari-U-shaped open fire (U-type), Patsari-liquefied petroleum gas (LPG) stove, U-type-LPG, and Patsari-U-type-LPG) common within rural Mexico were evaluated while using a controlled cooking cycle (CCC) from the Purepecha Highlands, which mimics the most common dishes prepared daily within the region as well as their sequential arrangement. The results confirms that the WHO Interim Target-1 (IT1) of 35 μg/m3 for PM2.5 is exceeded whenever woodburning open fires are part of the stacking combination (Patsari-U-type 107 ± 9 μg/m3, U-type-LPG 131 ± 55 μg/m3 and Patsari-U-type-LPG 107 ± 22 μg/m3). However, well-designed and properly operated woodburning chimney stoves, such as the Patsari can meet the IT1, either used exclusively (21 ± 8 μg/m3) or as a "clean stacking" option with LPG (24 ± 5 μg/m3). Given stove stacking patterns, evaluating the health and environmental consequences of stove transitions while assuming the total replacement of traditional fires by clean cooking options will lead to misplaced expectations, and programs should evaluate more realistic "clean-stacking" options. [ABSTRACT FROM AUTHOR]

This paper describes the use of citizen science-derived data for the creation of a land-use regression (LUR) model for particulate matter (PM2.5 and PMcoarse) for a vulnerable community in Imperial County, California (CA), near the United States (US)/Mexico border. Data from the Imperial County Community Air Monitoring Network community monitors were calibrated and added to a LUR, along with meteorology and land use. PM2.5 and PMcoarse were predicted across the county at the monthly timescale. Model types were compared by cross-validated (CV) R2 and root-mean-square error (RMSE). The Bayesian additive regression trees model (BART) performed the best for both PM2.5 (CV R2 = 0.47, RMSE = 1.5 µg/m3) and PMcoarse (CV R2 = 0.65, RMSE = 8.07 µg/m3). Model predictions were also compared to measurements from the regulatory monitors. RMSE for the monthly models was 3.6 µg/m3 for PM2.5 and 17.7 µg/m3 for PMcoarse. Variable importance measures pointed to seasonality and length of roads as drivers of PM2.5, and seasonality, type of farmland, and length of roads as drivers of PMcoarse. Predicted PM2.5 was elevated near the US/Mexico border and predicted PMcoarse was elevated in the center of Imperial Valley. Both sizes of PM were high near the western edge of the Salton Sea. This analysis provides some of the initial evidence for the utility of citizen science-derived pollution measurements to develop spatial and temporal models which can make estimates of pollution levels throughout vulnerable communities. [ABSTRACT FROM AUTHOR]

This paper presents measurements and analyses of the concentrations of black carbon (BC), particle number concentration (PNC), and PM2.5 (≤2.5 μm) while commuting by ferries in Istanbul. In this context, exposures to the mentioned pollutants were estimated for car ferry, fast ferry, and at the piers, and for two travel routes, for a total of 89 trips. BC, PNC, and PM2.5 measurements were simultaneously performed in a ferry and at the piers, and the correlation between pollutant concentrations, meteorological parameters, and environmental factors were analyzed. The mean concentrations for all pollutants in car ferry were lower than the average concentrations in fast ferry. The concentration ratios of fast ferry to car ferry for BC, PNC, and PM2.5 were 6.4, 1.2, and 1.3, respectively. High variability in the concentrations was observed at the piers and in ferry during berthing. The highest mean concentrations (±standard deviation) of BC (14.3 ± 10.1 µg m−3) and PNC (42,005 ± 30,899 pt cm−3) were measured at Yalova pier. The highest mean concentration (±standard deviation) of PM2.5 (26.1 ± 11.5) was measured at Bostancı pier. It was observed that the main external sources of BC, PNC, and PM2.5 at the piers were road transport, residential heating, and shipping activity. There were no significant correlations between BC, PNC, and PM2.5 in fast ferry, while BC was positively correlated with PNC (r = 0.61, p < 0.01) and PM2.5 (r = 0.76, p < 0.01) in car ferry. At the piers, significant relations between pollutants and meteorological variables were observed. It was noticed that there was no significant difference between summer and winter in ferry and at the pier concentrations of BC, PNC, and PM2.5 except for Yenikapı pier and Bakırköy pier. The highest total exposure to PNC and PM2.5 was in car ferry mode, while the highest total exposure to BC was in fast ferry mode. [ABSTRACT FROM AUTHOR]

Fine particulate matter (PM2.5) is a ubiquitous air pollutant that is harmful to human health. Social vulnerability indices (SVIs) are calculated to determine where vulnerable populations are located. We developed an SVI for Uganda to identify areas with high vulnerability and exposure to air pollution. The 2014 national census was used to create the SVI. Mean PM2.5 at the subcounty level was estimated using global PM2.5 estimates. The mean PM2.5 for Kampala at the parish level was estimated using low-cost PM2.5 sensors and spatial interpolation. A local indicator of spatial association (LISA) was performed to determine significant spatial clusters of social vulnerability, and a bivariate analysis was performed to identify where significant associations were between SVI and annual PM2.5 mean concentrations. The LISA results showed significant clustering of high SVI in the northern and western regions of the country. The spatial bivariate analysis showed positive linear associations between SVI and PM2.5 concentration in subcounties in the northern, western, and central regions of Uganda, as well as in certain northern parishes in Kampala. Our approach identified areas facing both high social vulnerability and air pollution levels. These areas can be prioritized for health interventions and policy to reduce the impact of ambient PM2.5. [ABSTRACT FROM AUTHOR]

There is increasing awareness in Australia of the health impacts of poor air quality. A common public concern raised at a number of "roadshow" events as part of the federally funded Clean Air and Urban Landscapes Hub (CAUL) project was whether or not the air quality monitoring network around Sydney was sampling air representative of typical suburban settings. In order to investigate this concern, ambient air quality measurements were made on the roof of a two-storey building in the Sydney suburb of Auburn, to simulate a typical suburban balcony site. Measurements were also taken at a busy roadside and these are discussed in a companion paper (Part 2). Measurements made at the balcony site were compared to data from three proximate regulatory air quality monitoring stations: Chullora, Liverpool and Prospect. During the 16-month measurement campaign, observations of carbon monoxide, oxides of nitrogen, ozone and particulate matter less than 2.5-µm diameter at the simulated urban balcony site were comparable to those at the closest permanent air quality stations. Despite the Auburn site experiencing 10% higher average carbon monoxide amounts than any of the permanent air quality monitoring sites, the oxides of nitrogen were within the range of the permanent sites and the pollutants of greatest concern within Sydney (PM2.5 and ozone) were both lowest at Auburn. Similar diurnal and seasonal cycles were observed between all sites, suggesting common pollutant sources and mechanisms. Therefore, it is concluded that the existing air quality network provides a good representation of typical pollution levels at the Auburn "balcony" site. [ABSTRACT FROM AUTHOR]

Ambient bioaerosols affect ecosystems and public health, but their seasonal variations and their contributions to aerosol particles are limitedly understood. Ambient bioaerosols in PM2.5 and PM10 samples were measured in Huangshi City, Hubei Province of China from April 2018 to December 2018. Bioaerosols were measured using a fluorescence microscope after staining with 4′, 6-diamino-2-phenylindole dihydrochloride (DAPI) following a direct staining technique. The bioaerosol number concentrations ranged from 0.12 to 15.69 # cm−3 for PM2.5 and 0.22 to 18.20 # cm−3 for PM10, with averages of 2.79 # cm−3 and 4.66 # cm−3, respectively. The bioaerosol concentrations of PM2.5 and PM10 varied significantly by seasons and were arranged in the following descending order: spring > fall > winter > summer. Bioaerosol numbers were dominated by fine particles of 0.37–2.5 μm diameter, while the spring bioaerosol particles were detected at the peak concentration of 0.56–1 μm diameter. Bioaerosol fractions accounted for 18.3 ± 10.6% PM10 mass and 13.7 ± 12.5% PM2.5 mass. Bioaerosol concentrations were increased during the haze event, but the increased amounts were not as large as those of the dust event, and higher bioaerosol contributions to PM were observed in the dust event than in the haze event. As enhanced emission controls have reduced PM concentrations in China, bioaerosols can be important contributors to PM mass. [ABSTRACT FROM AUTHOR]

We used the objective analysis method in conjunction with the successive correction method to assimilate MODerate resolution Imaging Spectroradiometer (MODIS) Aerosol Optical Depth (AOD) data into the Chimère model in order to improve the modeling of fine particulate matter (PM 2.5 ) concentrations and AOD field over Europe. A data assimilation module was developed to adjust the daily initial total column aerosol concentrations based on a forecast-analysis cycling scheme. The model is then evaluated during one-month winter period to examine how such a data assimilation technique pushes the model results closer to surface observations. This comparison showed that the mean biases of both surface PM 2.5 concentrations and the AOD field could be reduced from − 34 to −15% and from −45 to −27%. The assimilation, however, leads to false alarms because of the difficulty in distributing AOD 550 over different particle sizes. The impact of the influence radius is found to be small and depends on the density of satellite data. This work, although preliminary, is important in terms of near-real time air quality forecasting using the Chimère model and can be further developed to improve modeled PM 2.5 and ozone concentrations. [ABSTRACT FROM AUTHOR]

An assessment and prediction of PM2.5 for a port city of eastern peninsular India is presented. Fifteen machine learning (ML) regression models were trained, tested and implemented to predict the PM2.5 concentration. The predicting ability of regression models was validated using air pollutants and meteorological parameters as input variables collected from sites located at Visakhapatnam, a port city on the eastern side of peninsular India, for the assessment period 2018–2019. Highly correlated air pollutants and meteorological parameters with PM2.5 concentration were evaluated and presented during the period under study. It was found that the CatBoost regression model outperformed all other employed regression models in predicting PM2.5 concentration with an R2 score (coefficient of determination) of 0.81, median absolute error (MedAE) of 6.95 µg/m3, mean absolute percentage error (MAPE) of 0.29, root mean square error (RMSE) of 11.42 µg/m3 and mean absolute error (MAE) of 9.07 µg/m3. High PM2.5 concentration prediction results in contrast to Indian standards were also presented. In depth seasonal assessments of PM2.5 concentration were presented, to show variance in PM2.5 concentration during dominant seasons. [ABSTRACT FROM AUTHOR]

The dispersion of air pollutants and the spatial representation of meteorological variables are subject to complex atmospheric local parameters. To reduce the impact of particulate matter (PM2.5) on human health, it is of great significance to know its concentration at high spatial resolution. In order to monitor its effects on an exposed population, geostatistical analysis offers great potential to obtain high-quality spatial representation mapping of PM2.5 and meteorological variables. The purpose of this study was to define the optimal spatial representation of PM2.5, relative humidity, temperature and wind speed in the urban district in Cartagena, Colombia. The lack of data due to the scarcity of stations called for an ad hoc methodology, which included the interpolation implementing an ordinary kriging (OK) model, which was fed by data obtained through the inverse distance weighting (IDW) model. To consider wind effects, empirical Bayesian kriging regression prediction (EBK) was implemented. The application of these interpolation methods clarified the areas across the city that exceed the recommended limits of PM2.5 concentrations (Zona Franca, Base Naval and Centro district), and described in a continuous way, on the surface, three main weather variables. Positive correlations were obtained for relative humidity (R2 of 0.47), wind speed (R2 of 0.59) and temperature (R2 of 0.64). [ABSTRACT FROM AUTHOR]

The increasing frequency of human activities has accelerated changes in land use types and consequently affected the atmospheric environment. In this manuscript, we analyze the relationships between the particulate matter concentration and land use changes in the Beijing–Tianjin–Hebei (BTH) region, China, from 2015 to 2018. The experimental results indicate that (1) an improved sine function model can suitably fit the periodic changes in the particulate matter concentration, with the average R2 value increasing to 0.65 from the traditional model value of 0.49, while each model coefficient effectively estimates the change characteristics of each stage. (2) Among all land use types, the particulate matter concentrations in construction land and farmland are high, with a large annual difference between high and low values. The concentration decreases slowly in spring and summer but increases rapidly in autumn and winter. The concentrations in forestland and grassland are the lowest; the difference between high and low values is small for these land use types, and the concentration fluctuation pattern is relatively uniform. Natural sources greatly influence the concentration fluctuations, among which frequent dusty weather conditions in spring impose a greater influence on forestland and grassland than on the other land use types. (3) The landscape pattern of land use exerts a significant influence on the particulate matter concentration. Generally, the lower the aggregation degree of patches is, the higher the fragmentation degree is, the more complex the shape is, the higher the landscape abundance is, and the lower the particulate matter concentration is. The higher the construction land concentration is, the more easily emission sources can be aggregated to increase the particulate matter concentration. However, when forestland areas are suitably connected, this land use type can play a notable role in inhibiting particulate matter concentration aggravation. This conclusion is of great relevance to urban land use planning and sustainable development. [ABSTRACT FROM AUTHOR]

Ambient air pollution from PM2.5 is a major risk to human and environmental health, with significant impacts on mortality and morbidity. Mitigation policies—which may be regional or national in extent—need to consider both primary and secondary particles to be effective, balancing within-region emissions and longer-range transport phenomena. The modelling system WRF-CMAQ was used to simulate the impact of emissions reductions in the West Midlands region of the UK, evaluating the change in total PM2.5 and in its primary and secondary components. Domestic combustion, road transport and agriculture emissions were reduced individually or in combination, at a national or at local level. Combined reduction of road transport and agriculture emissions showed the strongest reduction (29%) in average PM2.5 if applied at national level. At the local level, reductions from domestic combustion were shown to be the most effective policy (13.4% on average). Secondary inorganic fractions of PM2.5 are the most abundant, with 25% NO3− 21% SO42− and 13% NH4+ on average. Scenario analysis shows that the contribution of secondary components to the fractional change of PM2.5 dominates for national policies (up to 0.86 for NO3−) when road transport and agriculture activities are reduced, while at the regional level the elemental and organic carbon fractional changes are dominant (up to 0.64 for organic carbon). [ABSTRACT FROM AUTHOR]

Air pollution is becoming increasingly serious with rapid economic development in China, and the primary pollutant has converted from PM10 to PM2.5, which is associated with more adverse impacts on human health. Satellite remote sensing, with help of its quantitative observations over large spatial and temporal extent, has become a significant supplement to the in situ measurements. This study exploits the aerosol optical depth (AOD) product retrieved from the Medium Resolution Spectrum Imager (MERSI) onboard Fengyun-3C satellite to estimate PM2.5 estimation over the major urban areas of Chongqing, a metropolitan city in the Southwestern China. A semi-empirical model and the linear mixed effect (LME) model are combined based on in situ observations from local air quality and meteorological networks from May 2014 to May 2015. This combined model is able to explain about 90% of the variation of the estimated PM2.5, and performs better than LME model by achieving higher correlation and smaller deviations between the satellites estimated PM2.5 and in situ measurements. Benefiting from the high resolution of 1 km × 1 km, MERSI AOD achieves much more detailed spatial distribution of ground-level PM2.5 over the major urban areas of Chongqing, compared to most of concurrent satellite products, such as the MODIS L2 AOD. According to the estimation, PM2.5 concentration is higher in cold seasons than in warm seasons in Chongqing. Peak levels of PM2.5 is found in Yuzhong District, the center of Chongqing urban area, while the concentration gradually decreases in surrounding areas, indicating that air pollution in Chongqing is highly contributed by local anthropogenic emissions. [ABSTRACT FROM AUTHOR]

PM2.5 samples were collected in the rural and urban areas of Taiyuan, China during a typical haze episode and the heavy metals (Cr, Mn, Ni, Cu, Zn, As, Cd and Pb) in PM2.5 were analyzed. The haze was characterized by start-up stage with a daily mean PM2.5 of 149.34 ± 52.33 and 146.73 ± 18.96 μg m-3 in the rural and urban sites, respectively, a peak stage (288.20 ± 12.43 and 323.44 ± 5.23 μg m-3), and a weakening stage (226.59 ± 12.43 and 195.60 ± 2.93 μg m-3). The concentrations of PM2.5 in the rural and urban sites in the peak stage were 5.9 and 5.5 times higher than those in the normal stage, respectively. The order of concentrations of heavy metals in PM2.5 at the rural and urban sites were the same and are listed as follows: Zn > Pb > Mn > Cr > Cu > Ni > Cd > As. Pb at the rural site, As at the urban site, and Cd at the both sites failed to meet the air quality standard. The concentrations of Pb and Zn were higher at the rural site than those at the urban site. Principal component analysis indicated that the main sources of heavy metals for the rural area were raw coal combustion and soil/road dust, and for the urban area were coal combustion/industrial emissions, road/soil dust, and vehicle emissions/oil combustion. [ABSTRACT FROM AUTHOR]

Background: Air pollution is a major global environmental risk factor. Since people spend most of their time indoors, the sole measure of outdoor concentrations is not sufficient to assess total exposure to air pollution. Therefore, the arising interest by the international community to indoor-outdoor relationships has led to the development of various techniques for the study of emission and exchange parameters among ambient and non-ambient pollutants. However, a standardised method is still lacking due to the complex release and dispersion of pollutants and the site conditions among studies. Methods: This review attempts to fill this gap to some extent by focusing on the analysis of the variety of site-specific approaches for the assessment of particulate matter in work and life environments. Results: First, the main analogies and differences between indoor and outdoor particles emerging from several studies are briefly described. Commonly-used indicators, sampling methods, and other approaches are compared. Second, recommendations for further studies based on recent results in order to improve the assessment and management of those issues are provided. Conclusions: This review is a step towards a comprehensive understanding of indoor and outdoor exposures which may stimulate the development of innovative tools for further epidemiological and multidisciplinary research. [ABSTRACT FROM AUTHOR]

The Reynolds-averagedNavier-Stokes (RANS)model and revised generalized drift fluxmodel were used to investigate the characteristics of airflow fields and PM2.5 dispersion in street canyons with a variety setting on tree crown morphologies (i.e., conical, spherical, and cylindrical), leaf area densities (LADs = 0.5, 1.5, and 2.5 m2/m3), and street canyon aspect ratios (H/W= 0.5, 1.0, and 2.0). Results were as follows: (1) airflow fields were reversed in the presence of trees and enhanced with higher LAD; (2) air velocity decreased negligibly when LAD increased from 1.5 to 2.5, but significantly when LAD increased from0.5 to 1.5; (3) tree crownmorphologies, building aspect ratios, and LADswere interrelated. The comparison of PM2.5 showed that themost critical situations inH/W= 0.5, 1.0, and 2.0 corresponded to LAD = 0.5 with a conical canopy; (4) the H/W = 1.0 and LAD = 1.5 scenario was identified as the most efficient combination for PM2.5 capture; (5) the maximum PM2.5 reduction ratio was ordered from low to high in the sequence of conical, spherical, and cylindrical canopies. At predestinated LADs and aspect ratio, Populus tomentosa with cylindrical crown morphology exhibited the best efficiency on PM2.5 capture with a reduction ratio of 75% to 85% at pedestrian height. [ABSTRACT FROM AUTHOR]

Though land use regression (LUR) models have been widely utilized to simulate air pollution distribution, unclear spatial scale effects of contributing characteristic variables usually make results study-specific. In this study, LUR models for PM2.5 in Houston Metropolitan Area, US were developed under scales of 100 m, 300 m, 500 m, 800 m, and 1000-5000 m with intervals of 500 m by employing the idea of statistically optimized analysis. Results show that the annual average PM2.5 concentration in Houston was significantly influenced by area ratios of open space urban and medium intensity urban at a 100 m scale, as well as of high intensity urban at a 500 m scale, whose correlation coefficients valued -0.64, 0.72, and 0.56, respectively. The fitting degree of LUR model at the optimized spatial scale (adj. R2 = 0.78) is obviously better than those at any other unified spatial scales (adj. R2 ranging from 0.19 to 0.65). Differences of PM2.5 concentrations produced by LUR models with best-, moderate-, weakest fitting degree, as well as ordinary kriging were evident, while the LUR model achieved the best cross-validation accuracy at the optimized spatial scale. Results suggested that statistical based optimized spatial scales of characteristic variables might possibly ensure the performance of LUR models in mapping PM2.5 distribution. [ABSTRACT FROM AUTHOR]

The Plain Forestation Project is an important measure designed to alleviate air pollution in Beijing, the capital of China. Ten commonly cultivated forest types of the Plain Forestation Project were studied at three growth stages of leaves. The particulate matter (PM)2.5 concentrations and forest structures were surveyed to analyze the PM2.5 concentration differences between different forest types, and establish a linear relationship between forest structures and PM2.5 concentration differences. The results suggested that forest ecosystems can block and capture PM2.5 from the air. Forests with luxuriant foliage are most effective in removing PM2.5 from the air. The average PM2.5 mass concentration in the Leaf-on Period (LOP) was the lowest when compared with other periods. The PM2.5 concentrations in the forest usually were higher than the control. Correspondingly, PM2.5 concentration indexes were negative values during daytime, but this results were reversed at night. Forests can reduce the diffusion rate of PM2.5 leading to PM2.5 were detained in the forest during daytime, and play an important role in the adsorption or deposition of particulate matter at night. Forest structure was primary reason of the PM2.5 concentration difference between different forests. The PM2.5 concentration index was positively correlated to canopy density, leaf area index (LAI), and mean diameter at breast height (DBH), and negatively correlated to the average tree height (height), forestland area, grass coverage and height. [ABSTRACT FROM AUTHOR]

New ambient air quality standards were released in 2012 and implemented in 2013 with real time monitoring data publication of six atmospheric pollutants: particulate matter (PM)2.5, PM10, O3, SO2, NO2and CO. According to the new standards, Beijing began to publicize real-time monitoring data of 35 monitoring stations in 2013. In this study, real time concentrations of all six atmospheric pollutants of all 35 monitoring stations were collected from September 2014 to August 2015 to investigate the spatial and temporal pattern of the air quality and atmospheric pollutants. By comparing the annual and seasonal variations of all six pollutants' concentrations, it was found that particulate matter, especially PM2.5, is still the major contributor to the deterioration of air quality in Beijing. Although the NO2 and O3 concentrations of some stations were still high under certain circumstances, their contributions to air quality index (AQI) were not comparable to those of PM2.5 and PM10. SO2 and CO concentrations have dropped to well below the qualification standards. Winter and autumn were the most polluted seasons for all pollutants except O3, whose concentrations are higher in summer. South and southeast stations were the most polluted compared with the rest of the stations, especially for particulate matter. Wind profile analysis with heavy pollution situations indicates that low speed southwest or east wind situations have the higher possibility of heavy pollution, suggesting that it is highly possible that long-range transportation of air pollutants from south or east neighboring provinces played an important role in the worsening air conditions in Beijing. [ABSTRACT FROM AUTHOR]

Ambient particles whose aerodynamic diameters were not greater than 2.5 µm (fine fraction of Particulate Matter; PM2.5) and 1 µm (PM1; sub-fraction of PM2.5) were sampled at three sites in Upper Silesia (Poland): urban background site, rural background site, and urban traffic site. In total, 240 samples were collected between 2 August 2009 and 27 December 2010. The reflectance of the collected PM1 and PM2.5 samples was determined with a digital smoke stain reflectometer. The 24-h courses and seasonal changes of three determined optical parameters for PM1 and PM2.5 (absorption coefficient (a), mass absorption (s), and mean light reflection coefficient (Rav)) were illustrated and discussed. The mean values of the regional background absorption coefficient (a) were 1.27 × 10-5 m-1 and 0.87 × 10-5 m-1 for PM2.5 and PM1, respectively. In Katowice (urban background), the mean absorption levels were 2.37 × 10-5 m-1 and 2.09 × 10-5 m-1 for PM2.5 and PM1, respectively. The highest values of the absorption coefficient for both PM fractions were found close to the highway (urban traffic site). In the heating season (winter), the absorption coefficient (a) for PM2.5 and PM1 increased significantly when compared with the non-heating season. The obtained results confirmed the thesis about the significant increase in the elemental PM2.5-bound carbon concentration caused by the intensified hard coal combustion in Upper Silesia in winter. Moreover, it turned out that the increase in the concentration of the PM2.5-bound sulphates was even higher, which resulted in the relative decrease of the elemental carbon content in this PM fraction in some areas. Consequently, the mass absorption value dropped there as well. [ABSTRACT FROM AUTHOR]