In order to study the sustained impact of particulate matter on urban residents, 572 PM2.5 and PM10 samples in all were attained simultaneously from a densely populated area of Beijing from September ...2015 to August 2016. 11 types of heavy metals were determined as to better ascertain the seasonal variation characteristics of particle composition. In the course of the year, the mean concentrations of PM2.5 and PM10 were detected to be 102.45 μg m−3 and 144.75 μg m−3, respectively. From general perspective, winter turned out to be the longest in haze day quantity and the highest in particle concentration, followed by spring, autumn and summer, successively. The mass concentrations of numerous PM2.5 elements fluctuated evidently in the decreasing order of Ba, Zn, Mn, Sr, Cu, Pb, Cr, V, Ni, Cd and Sb. In contrast with other seasons, winter displayed the most evident increase of metal content in particulates, especially under the condition of haze. Relationship between different size particles was also reckoned with in this study. High concentration ratio of PM2.5 and PM10 (0.76–0.84) was detected during haze period, and PM2.5 was accounted as a primary pollutant during haze-fog day in Beijing. Moreover, the carcinogenic and non-carcinogenic risk posed by detected heavy metals was investigated. Cr posed the highest carcinogenic risk, and in the meantime, as compared with other non-carcinogenic metals, value of Pb was the highest in Hazard Quotient. Risk arising from Cr and Cd in winter shall be noteworthy, and accordingly it may pose danger or potential risk to adults in haze days. Eventually, a large amount of fly ash and soot particles in winter samples were indicated from SEM-EDX analysis results, whereas those are rarely detected in summer samples.
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•A whole year's variation of daily PM2.5 and PM10 concentration was studied in urban Beijing.•Relationship between different size particles was also involved in this study.•The increase of metal concentration in winter particulates was the most obvious.•Risk arising from Cr and Cd in winter may pose danger or potential risk to adults in haze days.•A large amount of fly ash, soot particles in winter samples were indicated from SEM-EDX analysis.
In present study, the variation in concentration of key air pollutants such as PM2.5, PM10, NO2, SO2 and O3 during the pre-lockdown and post-lockdown phase has been investigated. In addition, the ...monthly concentration of air pollutants in March, April and May of 2020 is also compared with that of 2019 to unfold the effect of restricted emissions under similar meteorological conditions. To evaluate the global impact of COVID-19 on the air quality, ground-based data from 162 monitoring stations from 12 cities across the globe are analysed for the first time. The concentration of PM2.5, PM10 and NO2 were reduced by 20–34%, 24–47% and 32–64%, respectively, due to restriction on anthropogenic emission sources during lockdown. However, a lower reduction in SO2 was observed due to functional power plants. O3 concentration was found to be increased due to the declined emission of NO. Nevertheless, the achieved improvements were temporary as the pollution level has gone up again in cities where lockdown was lifted. The study might assist the environmentalist, government and policymakers to curb down the air pollution in future by implementing the strategic lockdowns at the pollution hotspots with minimal economic loss.
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•Positive impacts of COVID-19 on environment are demonstrated.•Air quality data of 162 monitoring stations from 12 cities across the globe are assessed.•PM2.5, PM10 and NO2 have shown a significant reduction in lockdown phase as compared to pre-lockdown phase.•A notable improvement in air quality is observed during lockdown across the globe.•Achieved improvement in air quality is temporary as pollution level increased in cities where lockdown was lifted.
Wavelet transform (WT) is an advanced preprocessing technique, which has been widely used in PM 10 prediction. However, this technique cannot provide stable performance due to the empirical selection ...of wavelet's layers. For fixing the optimal wavelet's layers in PM10 forecasting, an innovative coupled model based on WT, long short-term memory (LSTM), and SAE (stacked autoencoder) are proposed. This study designs a crossover experiment with 960 high- and low-frequency components by wavelet decomposition and predicts each component with SAE-LSTM based on 12 samples from different regions. The results indicate that the developed model outperforms other BiLSTM (Biredictional LSTM) and LSTM based on some error evaluation indicators (i.e. Nash-Sutcliffe efficiency coefficient (NSEC)), and compared with other steps, the accuracy of two-step prediction is the highest in view of root mean squares error (RMSE). In addition, for 12 samples, the prediction accuracy by using high layers is higher than that by adopting low layers for decomposing them. This paper fixes the optimal wavelet’ layers in PM10 prediction, which provides a meaningful reference in other prediction scenarios based on the application of WT.
•A novel WT-SAE-LSTM is proposed to forecast PM10 based on twelve samples in China.•Compared with the advanced machine learning algorithms (e.g., BiLSTM), SAE-LSTM has better forecasting performance.•The optimal wavelet's layers are determined for different samples.
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•1. The principle of dynamic interaction between fog droplets and coal dust was explored.•2. Droplet size ratio ≥ 2, speed between 20–40 m/s dust capture PM10 effect is better.•3. The ...preferred spray parameters were found to be: 1.5 mm caliber, pressure of 6mpa.•4.The wetting effect was better when the ratio was 0.1 % ALES + 0.08 % CDEA + 0.01 % NaCl.
Coal mines produce large quantities of dust during production and transportation of coal, especially respirable particulate matter of diameter < 10 µm (PM10). This not only jeopardizes the health of workers but also pollutes the environment. Currently, spraying is the most commonly used dust reduction technology. Traditional spraying techniques use pure water; however, the wetting and condensing of dust by pure water droplets is poor, resulting in low dust reduction efficiency. To address this issue, we used orthogonal tests to determine the optimal ratio of composite dust-reducing agents. Using the coupled level set and volume of fluid (CLSVOF) method, we investigated the dynamic wetting process of PM10 when using solutions of dust-reducing agents. We found that a particle size ratio of ≥ 2 and an initial velocity of droplets of 20–40 m/s resulted in optimal wetting and encapsulation of dust particles by droplets. We then conducted macroscopic experiments and found that the settling efficiency of PM10 reached 88.98 % when using a 1.5-mm caliber fine atomizing nozzle and a spray pressure of 6 MPa. Our findings reveal the dynamic wetting law of droplets and dust during the process of spray dust reduction, which could lead to improved efficiency of dust-reducing agents and better management of respirable dust. These findings will help to improve clean production in coal mines and the occupational health of coal miners.
Particulate matter (PM) is a key indicator of air pollution brought into the air by a variety of natural and human activities. As it can be suspended over long time and travel over long distances in ...the atmosphere, it can cause a wide range of diseases that lead to a significant reduction of human life. The size of particles has been directly linked to their potential for causing health problems. Small particles of concern include “inhalable coarse particles” with a diameter of 2.5 to 10μm and “fine particles” smaller than 2.5μm in diameter. As the source–effect relationship of PM remains unclear, it is not easy to define such effects from individual sources such as long-range transport of pollution. Because of the potent role of PM and its associated pollutants, detailed knowledge of their human health impacts is of primary importance. This paper summarizes the basic evidence on the health effects of particulate matter. An in-depth analysis is provided to address the implications for policy-makers so that more stringent strategies can be implemented to reduce air pollution and its health effects.
•Diverse sources of particulate matter deteriorate air quality and exert impact on human health.•An overview of PM is provided by synthesizing information of its impact and regulation efforts.•The management skills of PM from the areas suffering from its worst pollution are also addressed.
In this study, a method for the determination of tire and road wear particle (TRWP) contents in particulate samples from road environment was developed. Zn was identified as the most suitable ...elemental marker for TRWP, due to its high concentration in tire tread and the possibility of separation from other Zn sources. The mean concentration of 21 tire samples was 8.7 ± 2.0 mg Zn/g. Before quantification in samples from road environment, TRWP were separated from the particulate matrix by density separation. Method development was conducted using shredded tread particles (TP) as a surrogate for TRWP. Recovery of TP from spiked sediment was 95 ± 17% in a concentration range of 2 - 200 mg TP/g. TP determination was not affected by other Zn containing solids or spiked Zn-salts. By adjusting the density of the separation solution to 1.9 g/cm³, more than 90% of total TRWP were separated from the sample matrix. TRWP concentrations in particulate matter collected in two road runoff treatment systems ranged from 0.38 to 150 mg TRWP/g. Differences in quantified TRWP contents of the two systems indicate changes in particle dynamics due to ageing and aggregation processes. The developed method allows TRWP determination in road runoff and in environments that are influenced by road traffic. The validated separation procedure can also be applied for TRWP characterization in future studies.
•Density separation is able to enrich TRWP.•Density separation can be a powerful tool for TRWP characterization.•TRWP density was determined and may change upon ageing.•Zn is the best suited elemental marker for TRWP quantification.•Samples from road environment were analyzed for TRWP.
Abstract
The evidence for adverse effects of ambient particulate matter (PM) pollution on mental health is limited. Studies in Western countries suggested higher risk of autism spectrum disorder (ASD) ...associated with PM air pollution, but no such study has been done in developing countries.
A case-control study was performed in Shanghai with a multi-stage random sampling design. Children's exposures to PM1, PM2.5 and PM10 (particulate matter with aerodynamic diameter < 1 μm, < 2.5 μm and < 10 μm, respectively) during the first three years after birth were estimated with satellite remote sensing data. Conditional logistic regression was used to examine the PM-ASD association.
In total, 124 ASD cases and 1240 healthy controls were included in this study. The median levels of PM1, PM2.5 and PM10 exposures during the first three years of life were 48.8 μg/m3, 66.2 μg/m3 and 95.4 μg/m3, respectively, and the interquartile range (IQR) for these three pollutants were 4.8 μg/m3, 3.4 μg/m3 and 4.9 μg/m3, respectively. The adjusted odds ratios (and 95% confidence intervals) of ASD associated with an IQR increase for PM1, PM2.5 and PM10 were 1.86 (1.09, 3.17), 1.78 (1.14, 2.76) and 1.68 (1.09, 2.59), respectively. Higher ORs of ASD associated with PM pollution were observed in the second and the third year after birth.
Exposures to PM1, PM2.5 and PM10 during the first three years of life were associated with the increased risk of ASD and there appeared to be stronger effects of ambient PM pollution on ASD in the second and the third years after birth.
•Post-natal exposure to PM1 significantly increased the risk of autism (OR = 1.86).•Post-natal exposure to PM2.5 significantly increased the risk of autism (OR = 1.78).•Stronger associations were observed in the second and the third year after birth.
This study investigates river dust episodes along the Choshui and Kaoping Rivers in Taiwan, focusing on their spatiotemporal distribution and correlation with hydrometeorological factors ...(temperature, precipitation, relative humidity, and wind speed). Using the Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN) algorithm and time-dependent intrinsic correlation (TDIC) analysis, we identified significant annual and diurnal correlations between PM10 concentrations and these factors. The analysis revealed that wind speed at Lunbei station had a positive annual correlation with PM10, while other factors exhibited significant negative correlations. Seasonal variations in PM10 correlations with temperature, relative humidity, and wind speed were observed, aligning with the prevailing seasons of river dust episodes. Wind motion analysis highlighted diurnal associations with land-sea breezes and annual correlations with the winter monsoon. Specifically, the Choshui River's dust events coincided with the northeast monsoon, whereas the Kaoping River's events occurred during the northwest and southwest monsoons. The study also uncovered that downstream stations (Lunbei and Daliao) were more prone to severe dust events than upstream stations (Douliu and Pingtung). These findings enhance our understanding of the dynamics and environmental impacts of river dust episodes, providing valuable insights for air quality management and health risk mitigation.
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•Wind patterns, including diurnal breezes and monsoons, significantly affect PM10 concentrations.•Seasonal PM10 variations align with dust event seasons along Choshui and Kaoping rivers.•ICEEMDAN analysis reveals significant correlations between PM10 and hydrometeorological factors.•Choshui experiences more severe dust events mainly in winter, while Kaoping’s occur in summer.•Choshui's dust primarily comes from bare riverbeds, whereas Kaoping's also comes from typhoon sediment.
CSEOF analysis is applied for the springtime (March, April, May) daily PM10 concentrations measured at 23 Ministry of Environment stations in Seoul, Korea for the period of 2003–2012. Six ...meteorological variables at 12 pressure levels are also acquired from the ERA Interim reanalysis datasets. CSEOF analysis is conducted for each meteorological variable over East Asia. Regression analysis is conducted in CSEOF space between the PM10 concentrations and individual meteorological variables to identify associated atmospheric conditions for each CSEOF mode. By adding the regressed loading vectors with the mean meteorological fields, the daily atmospheric conditions are obtained for the first five CSEOF modes. Then, HYSPLIT model is run with the atmospheric conditions for each CSEOF mode in order to back trace the air parcels and dust reaching Seoul. The K-means clustering algorithm is applied to identify major source regions for each CSEOF mode of the PM10 concentrations in Seoul. Three main source regions identified based on the mean fields are: (1) northern Taklamakan Desert (NTD), (2) Gobi Desert and (GD), and (3) East China industrial area (ECI). The main source regions for the mean meteorological fields are consistent with those of previous study; 41% of the source locations are located in GD followed by ECI (37%) and NTD (21%). Back trajectory calculations based on CSEOF analysis of meteorological variables identify distinct source characteristics associated with each CSEOF mode and greatly facilitate the interpretation of the PM10 variability in Seoul in terms of transportation route and meteorological conditions including the source area.
•Distinct modes of PM10 variability in Seoul are identified via CSEOF analysis and are tied with associated meteorological variables and source regions in East Asia. HYSPLIT model is run with the meteorological fields for each CSEOF mode to identify the respective source regions in East Asia.•In the mean climatological sense, main source regions for the PM10 concentrations in Seoul are identified as: northern Taklamakan Desert, Gobi Desert, and East China industrial area. GD is the most dominant source region of PM10 events, while NTD contributes most to extreme PM10 events.•Due to the distinct atmospheric conditions associated with the CSEOF modes, the source characteristics—source regions and frequencies for PM10 and extreme PM10 events—differ significantly from one mode to another.
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