Particulate matter (PM) emission from coal mining activities is inevitable and a significant concern worldwide. American Meteorological Society/Environmental Protection Agency Regulatory Model (AERMOD) is one of the most widely used dispersion models for predicting air PM dispersion in coal mines. However, validation of AERMOD-predicted PM concentration in a large mine complex has not been reported. So, in this study, AERMOD predicted PM concentration was validated against the PM concentrations measured by nine continuous ambient air quality monitoring stations (CAAQMS) stationed in the Singrauli coal mining complex. The complex contains nine coal mines across 438 square kilometers, with around 129 pollution sources chiefly from the area, pit, and line categories. PM10 and PM2.5 concentrations peak during summer (204.58 µg/m3) and winter (67.67 µg/m3), respectively. The AERMOD model predicts peak dispersion of PM10 (500–1200 µg/m3) and PM2.5 (100–800 µg/m3) during the winter season. The AERMOD model reveals that the region’s wind movement caused by land and lake breezes was the predominant driver of PM surface dispersion. In the winter season, atmospheric inversion increases ground-level PM concentrations in the region. The AERMOD cannot represent the vertical dispersion of PMs in the summer, resulting in an underestimation of PM concentration. The statistical validation shows that AERMOD underestimates PM10 and PM2.5 concentrations across all seasons and years. The AERMOD model’s prediction accuracy for PM10 (R2 = 0.38) and PM2.5 (R2 = 0.56) is also low. Finally, it can be concluded that AERMOD-predicted PM concentrations are not accurate for large mining complexes but more suitable for individual mines.
China is confronted with a severe air pollution challenge, wherein thermal power generation plays a significant role. In recent years, substantial efforts have been made in ultra-low emission retrofitting of coal-fired power plants, however, quantitative study regarding its subsequent impact on air quality is limited. In this study, we estimated the emission reduction of thermal power plants from the perspective of online monitoring system during 2014 ∼ 2016, and investigated the accompanying impacts on air quality in typical regions of China by using a regional chemical model WRF-Chem. The results indicate that the ultra-low emission retrofitting of thermal power plants, which was initiated in 2014, has achieved significant progress, with nearly 80% of planned tasks completed by 2016. As a result, emissions of SO2, NOx and PM2.5 from thermal power plants notably decreased by 67.5%∼72.8% nationwide between 2014 and 2016. WRF-Chem simulations demonstrate that the ultra-low emission retrofitting effectively reduces air pollutant concentrations. Specifically, the monthly mean concentrations of SO2, NO2 and PM2.5 in typical regions have decreased by 0.6 ∼ 1.7, 2.2 ∼ 3.7 and 2.6 ∼ 5.0 µg m− 3, respectively, representing an improvement of 3.1%∼10.4%, particularly notable in winter. Regional variations in installed thermal power capacity and completion of the ultra-low emission retrofitting have led to differential improvements in air quality, with the Yangtze River Delta region exhibiting the most significant reduction in air pollution concentrations, surpassing the Beijing-Tianjin-Hebei and Pearl River Delta regions by up to 2.2 µg m− 3. This study serves as a valuable reference for the ultra-low emission retrofitting of thermal power industry and provides essential data support for future air quality management strategies.
Several studies have reported reductions in atmospheric particulate matter (PM2.5 and PM10) during the social isolation period of the COVID-19 pandemic. We evaluated the monetary and health benefits of PM emission reductions in the short and long term in the city of Florianópolis, Brazil (half a million inhabitants). We collected information on PM10 and PM2.5 concentrations from 2018 to 2020, and population and health-related data (mortality and hospitalizations due to heart and respiratory problems) from 2018 to 2019. The Health Impact Assessment (HIA) tool was applied to the APHEKOM model and two different scenarios were evaluated. In the first scenario, PM levels remained throughout the year at the same average level as the most restrictive period of human mobility to contain Sars-CoV-2 infections. In the second, PM levels remained at WHO recommended levels throughout the year. In the first scenario, PM2.5 reduction would prevent 35 annual deaths from non-external causes and 21 annual deaths from cardiovascular diseases. In addition, PM10 reduction would prevent 28.9 respiratory hospitalizations and 12 cardiovascular hospitalizations, saving the public purse more than US$ 313,000 per year. In the second, based on WHO recommended levels, a reduction in PM2.5 would prevent 47.7 annual deaths from non-external causes and 28.3 annual deaths from cardiovascular disease. Reducing PM10 concentrations would also prevent 53.2 respiratory hospitalizations and 22.1 cardiac hospitalizations, resulting in savings of more than US$ 577,000/year. Therefore, a sustainable PM reduction that does not require the cessation of human activities could improve the quality of population health and reduce hospitalization costs.
In order to measure the seasonal variations, source identification, and human health risk of elements in PM2.5 were collected from January 2021 to December 2021 at an urban site in Sheohar, India. Element (Na, Mg, K, Ca, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, As, Cd, and Pb) concentrations were estimated using an inductively coupled plasma optical emission spectrometer (ICP-OES). The enrichment factors (EFs) and principal component analysis (PCA) were used to identify the sources of these elements, and the United States Environmental Protection Agency (US EPA) models were used to evaluate the carcinogenic and non-carcinogenic risks to both children and adults. The results showed that the annual mean concentration of PM2.5 was 104.22 μg m−3 which exceeded the National Ambient Air Quality Standards (NAAQS, 40 μg m−3) and the World Health Organisation (WHO, 10 μg m−3) limits. The seasonal variation of PM2.5 was characterised by high concentrations in winter, followed by post-monsoon, summer, and monsoon. The average annual concentrations of As, Cd, Ni, and Cr were 17.25 ng m−3, 32.68 ng m−3, 158.16 ng m−3, and 177.41 ng m−3, respectively, which were above the WHO limits. The concentrations of Ca and Fe were highest in the summer season. The concentrations of Mg and Na were highest in the monsoon season. The other element concentrations were highest in the winter season. Enrichment factor analysis showed that Zn, As, Pb, and Cd were predominantly from anthropogenic sources. In addition, source apportionment by PCA identified six components for the studied elements. The total carcinogenic and non-carcinogenic risks of elements exceeded the safe level of exposure for both children and adults, which indicate further research on sources of air pollution and measures for controlling pollutants in Sheohar, India.
The passive containment cooling system adopted in advanced light water reactors can enhance the natural removal of suspended aerosols inside containment during accidents. The primary removal mechanism is the diffusiophoresis in steam environments with the presence of non-condensable gas. The lumped-parameter methodology is widely used to calculate the natural removal of aerosol in the nuclear industry, which cannot obtain the mechanistic analysis of aerosol behavior. A numerical simulation methodology based on the Euler–Lagrange system was developed in this paper for the mechanistic analysis. COPAIN experiments and a hypothetical case validated the steam wall condensation model and aerosol diffusiophoresis model in this methodology. Then the experiments on aerosol transport and deposition inside a heat transfer tube were simulated using the validated numerical methodology. The simulation results agree well with the experiments. Numerical analysis indicates that the aerosol deposition rate decreases with increasing particle size with the combination effect of Stefan flow, thermophoretic, and diffusiophoretic forces. Stefan flow plays a dominant role; In the steam–air environment, diffusiophoretic force slightly weakens the aerosol wall deposition. The numerical simulation methodology developed in this work can be used to mechanistically analyze the behavior of aerosol transport and deposition inside containment during accidents.
The characteristics of exhaled aerosol outside the human respiratory airway are of significant importance in understanding virus transmission, yet they remain poorly understood. In order to effectively prevent and control the spread of respiratory infectious diseases, this study numerically investigates the exhaling characteristics of respiratory aerosol exhaled from the bronchus or larynx of a human upper airway model. This is achieved using the Euler–Lagrange method and considering various aerosol diameters (dp = 0.1, 0.3, 0.5, and 1–20 μm) as well as five expiratory flow intensities (Q = 15, 30, 60, 90, and 120 L/min). The important findings of this study are as follows: (1) Expiratory airflow exhibits complex flow phenomena, including jet-flow, flow separations, and vortex structures, with their characteristics being influenced by the expiratory flow intensities. (2) The exhaling characteristics of aerosol vary depending on the combined effects of expiratory flow intensities, aerosol diameters, and initial exhaled locations from either the bronchus or larynx. (3) A critical diameter (dc) is identified to represent the size at which aerosol can effectively exit the respiratory airway and potentially pose a transmission risk. This critical diameter is identical for aerosol exhaled from both the bronchus and larynx under the same expiratory flow intensity, but it decreases as the expiratory flow intensity increases. In conclusion, expiratory flow intensity is the most critical factor in determining whether aerosol droplets can be expelled from the respiratory airway, as well as influencing the critical diameter (dc) for aerosol droplets initially located in/after the larynx.
Rainfall removal of aerosol particles is an important atmospheric aerosol self-scavenging process. Studying the scavenging mechanism of rainfall on aerosol particles and developing a suitable theoretical model are of great significance for preventing and controlling aerosol pollution and improving the accuracy of air quality forecasting. In this paper, the influence of the turbulence effect on aerosol capture by raindrops is investigated using numerical simulation, and the contribution of the turbulence effect to the capture of aerosol particles by raindrops, Et, is given via the introduction of dimensionless parameters. The scavenging coefficients of the accumulated model particles calculated by simultaneously considering seven mechanisms, namely, Brownian diffusion, interception, inertial impaction, thermophoretic action, diffusiophoretic action, electrostatic action, and the turbulence effect, were found to be 2–10 times higher than those calculated using the currently commonly used Slinn formula (which considers only Brownian diffusion, interception, and inertial impaction). A rainfall scavenging of polydisperse aerosol prediction model was established by taking the actual rainfall events in Guangzhou City, China, as an example and considering seven mechanisms simultaneously, and the characteristics of small particulate matter (PM2.5) changes over time simulated using the model matched well with the actual measurements.
The carbonaceous aerosol source apportionment is crucial for targeted prevention and control of PM2.5 in China. The 24 h integrated PM2.5 samples were collected from Xi’an, China during pollution events in 2015 in summer (biomass open burning period) and winter (haze period). Source apportionment of carbonaceous aerosols in PM2.5 was conducted using radiocarbon (14C) and levoglucosan (a biomass combustion tracer). Results showed that in the study period of biomass open burning in the wheat harvest season (early June) in Xi’an, fossil and non-fossil sources contributed approximately 47% and 53% to total carbon (TC), respectively. In the haze pollution period, non-fossil sources dominated water-insoluble organic carbon (WISOC) at around 53%, and fossil sources accounted for about 71% of elemental carbon (EC), with the rest from biomass combustion. The usage of coal and biomass for heating in the study period in winter increased the contribution of fossil fuel combustion to carbonaceous aerosols, particularly EC. In order to reduce PM2.5 and carbon emission in Xi’an, controlling biomass fuels burning in summer and solid fuels use in winter are essential. Biomass fuel is a renewable negative carbon fuel, contributing significantly to greenhouse gas emission reduction. After estimation, biomass fuel usage by rural residents in Xi’an in 2015 reduced by 545,000 t of CO2 equivalent emission, with a carbon benefit of 38.1 million yuan. Replacing scattered coal with biomass fuel could further reduce 75,500 t of CO2 equivalent emission, generating a carbon gain of 5.29 million yuan.
A high-efficiency bioaerosol sampler is a necessary tool to capture airborne pathogenic microbes, and can effectively ensure the concentration and biological viability of the microbes for further biological and medical analysis. The Andersen sampler and Fuji cyclone were applied in the bacteria and virus under a bioaerosol emission chamber. The four factors selected for this study were temperature, microbial culture concentration, aerosol emission time, and the placing position of biosamplers, and each influencing factor with its varying levels were optimized by orthogonal analysis to evaluate the sampling efficiency of alternative biosamplers under both bacterial and viral environment in a bioaerosol generating chamber. The Andersen impactor had a better collecting effect than Fuji cyclone under four influence factors in bacterial aerosol environment. In the viral aerosol collection, a high air flow rate Fuji cyclone has a better performance for collecting viral aerosol without considering the viability. The two best factors from both Andersen and Fuji were the emission concentration and the angle of 45° sampler placing position under bacteria environment. The two best factors from both Andersen and Fuji were the temperature and the emission time under the virus environment.
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：