Aerosol Science and Engineering最新文献

The study in Delhi presents a thorough examination of road dust pollution, revealing elevated levels of dust and heavy metals throughout the city. Areas with high road dust concentrations, notably in traffic-congested and industrial zones, show significant metal contamination, with cadmium (Cd), cobalt (Co), chromium (Cr), copper (Cu), nickel (Ni), lead (Pb), and zinc (Zn) levels exceeding background soil values. Particularly heightened Cr and Zn levels, attributed to industrial and vehicular emissions, pose increased non-carcinogenic risks to children through ingestion and inhalation. While the Hazard Index (HI) indicates lower risks for the general population, carcinogenic risk index (RI) values surpass acceptable limits for Pb, Cr, Cd, and Ni, highlighting substantial cancer risks for both children and adults. To mitigate these risks, sustainable road dust management practices are essential to enhance air quality, protect ecosystems, and reduce health hazards linked to dust exposure. Monitoring and controlling heavy metal presence in dust is critical for a cleaner environment. Cd, Pb, and Cr, known to cause various health issues, underscore the necessity of managing their presence in road dust. Recommendations include wearing masks, avoiding outdoor exposure during high pollution events, and maintaining indoor cleanliness. The study emphasizes the importance of green infrastructure and municipal interventions, such as road watering, to combat road dust pollution, emphasizing the need for proactive measures to safeguard public health and the environment.

The objective of this work is to predict daily PM2.5 air quality in Dakar, Senegal using data from an automated measurement station integrated into a server using a data assimilation model. Initially, a 3-year data set was used to identify and validate an appropriate ARIMA data assimilation model. The data was split into an 80% training set and a 20% test set. The Augmented Dickey-Fuller (ADF) test was used to check the normality of the data series. Subsequently, we used the AutoArima method to determine the optimal model to represent the time series. Preliminary results show that a model with order (2,1,1) accurately represents the series. Additional analysis using model fit tests showed that the (3, 0, 1) model was most effective in representing and predicting the data. The statistical validation performance of this model demonstrates its capability to forecast PM2.5 concentrations for up to 72 h (3 days), achieving correlation coefficients exceeding 80%. However, after three days, the predictions returned to background levels. In the final stage of the study, data from automatic stations were integrated into a server hosting the assimilation model to improve daily PM2.5 forecasts for Dakar. An interactive platform was developed to visualize measurements and forecasts over two days. The results show that by integrating the data with the assimilation model, predictions are significantly improved.

The high level of chemical compounds in the atmosphere of many West African cities is worrying because of the potential threats to human health and other environmental problems they are known for. However, routine monitoring and adequate control measures are rare due to technical, social and economic problems. This paper analyzed the health and visibility effects of aerosol optical depth chemical species within some West African cities from (2010–2020) using the aerosol optical depth data set obtained from the European Center for Medium-Range Weather Forecasts (ECMWF-UK). The results of the analysis showed that the visual range of the study cities ranged from 4600 to 5600 km, while the potentials of human health effects T_PHhe existing in the cities are between 0.9 and 1.2 signifying low visibility and high potential threats to human health. There exist several weak and also inverse correlations between the variability of the aerosol optical depth chemical species in the study cities with a coefficient of determination ({r}^{2}) ranging from 0.01 to 0.98. This implies that aerosol loads are not uniformly distributed across cities and also come from a plethora of sources across cities. The variability of aerosol optical depth chemical species in the West African cities presented is useful in evaluating and improving the accuracy of the models for aerosol prediction in the region and can assist in the easy determination of aerosol effects in the atmosphere. The total chemical composition of aerosol loads was gauged with acceptable standards limit set by Environmental Protection Agencies to determine the health effects on humans and the results are useful not only in measuring the health implication but also in evaluating safety measures to tackle the effects, while the identified poor visibility in the cities is a clear call for policymakers to step up regulation and design action to tackle the menace of visibility reduction in these cities.

Proteins in aerosols are the core biological components connecting geographical isolated ecosystems via the atmosphere, and also an increasing concern for their high allergic potential. The prolonged sample collection time of days and even weeks required in current aerosol proteins studies makes it difficult to investigate the variation of proteins concentration with weather and, consequently, to explore the sources of the proteins and their correlations with other aerosol components. Using bicinchoninic acid (BCA) assay and cold acetone precipitation, we developed a protocol to quantify the aerosol soluble proteins (ASPs) in samples collected at a low flow rate in hours. Laboratory experiments with bovine serum albumin solution were conducted to optimize the operational procedures and conditions for a stable and efficient proteins recovery rate (RC_P). The results showed that the optimal dosage of cold acetone for the protein precipitations was a 5-fold sample solution in volume. The most effective air-dried conditions for protein precipitations were in an environment with a relative humidity of 20 ± 2% and a temperature below 20 °C for 2 h. The RC_P was stable at 42.0 ± 8.0% (proteins concentration in solution: 2–40 µg mL^–1). Test applications of the protocol to samples collected for 6 h at 10–16 L min^–1 flow rate in the lightly polluted urban air of a coastal city in Japan demonstrated the effectiveness of the protocol in measuring the variation of ASPs in hours. The result revealed that the ASPs concentration in Kumamoto ranged from 0.07 to 1.29 µg m^–3 in winter and had a positive correlation with particulate matter.

The health impact of atmospheric pollution is one of the hot topics in current environmental research. Herein, we examined the impacts of indoor and outdoor air pollution on respiratory health across three distinct communities in Xi’an, China. By employing a mixed-methods approach, this research quantitatively assessed particulate matter concentrations alongside gases such as CO, CO₂, NO, and NO₂, contrasting indoor and outdoor environments. The indoor and outdoor pollutants of urban communities presented higher emission levels, the disparities in indoor pollutant concentrations across the communities were primarily attributed to domestic activities including cooking, incense burning, and smoking. Notably, CO and CO₂ levels were elevated indoors, underscoring the influence of human activities and inadequate ventilation on indoor air quality. The higher indoor/outdoor (I/O) pollutant ratios of CO and NO pointed to predominant indoor sources of these pollutants; additionally, the suburban community showed higher I/O ratio. Through lung function assessments, a negative correlation between air pollutant concentrations and respiratory health outcomes among residents was established, demonstrating the detrimental effects of air pollution on pulmonary health. The findings underscored the critical public health implications of air pollution, advocating for comprehensive interventions to enhance air quality and mitigate the adverse health impacts of pollution in residential settings.

The aerosol optical properties are studied for all seasons at northeast India’s high-altitude station. In this study, total scattering and backward scattering data of Integrating Nephelometer 3563 is utilized for computation of scattering Ångström exponent (α_450-700 nm), backscatter fraction (bf), and asymmetric parameter (g). Noteworthy, the asymmetric parameter and particle size cannot be inferred directly from the Integrating Nephelometer. Theoretical approximation (Kokhanovsky and Nauss Atmos Chem Phys 6:5537–5545, 2006; Sviridenkov et al. Atmos Ocean Opt 30:435–440, 2017) and model simulation (MieTab and Mieplot) are utilized to estimate the g and particle size. The α_450-700 nm varies from 1.47 to 1.88, indicating that the fine aerosol particles with a radius of < 0.5 µm are dominant at the station. The bf and g are found to be in the range of 0.11–0.13 and 0.68 to 0.74. After comparison of the estimated g value with the model simulated particle size, observed that the radius varies from (approx ) 0.17 µm to 0.21 µm. Here, the aerosol particles are a homogeneous mixture of graphite-air and dry ash with a size range of 0.17 µm ≥ radius ≥ 0.21 µm. The bf decreased from winter to monsoon season, while the g values enhanced and demonstrated a negative correlation. The bf value decreased owing to the lower backscatter and higher forward scatter for bigger particles from winter to monsoon. Thus, the g values were smaller for higher bf values and associated with smaller aerosol particles.

During physical and chemical processes, aerosol particles often undergo coagulation to form Agglomerates. Agglomerates are fractal-like in a statistical sense, whose dynamical evolution of particle size distribution is governed by the population balance equation (PBE). In this study, the Radial Basis Function (RBF) method RBF is firstly extended to the solution of fractal-like agglomerate dynamics problems. The applicable conditions, and advantages and disadvantages of this method are studied. Two dynamic processes of fractal-like agglomerates, namely Brownian coagulation in the continuum regime and Brownian coagulation in the free molecular regime, are investigated. As a comparison, the sectional method (SM) is utilized as the referenced method. The initial geometric standard deviation (GSD) and the fractal dimension ((D_{f})) of agglomerates are found to be the two main key factors affecting the accuracy and efficiency of the RBF. The RBF method is more suitable for calculating cases with larger GSD. As the GSD increases (i.e., GSD > 1.2), the computational efficiency and accuracy of the RBF increase accordingly. The RBF method is more suitable for calculating cases with larger (D_{f}). As the (D_{f}) decreases, the calculation error of RBF method becomes further larger, which is more obvious in the free molecular regime. Compared with the SM method, the calculation efficiency of RBF method increases by 3–4 orders of magnitude. This study provides excellent application of RBF method to the solution of the PBE.

Tube electrofiltration equipment is mainly used to remove fine particles from exhaust gases and separate them from the emission source. In this paper, we study the flow field characteristics and dust removal efficiency of tubular electrofiltration equipment through numerical simulation. The multi-field coupling model of tubular electrofiltration equipment can be used to investigate the internal ion wind (EHD flow) of tubular electrofiltration equipment and its impact on dust removal efficiency. Furthermore, this approach can also be used to evaluate the particle motion trajectory and its impact on dust removal efficiency in tubular electrofiltration equipment under different particle sizes, air flow velocities, and discharge voltages. The results demonstrate that increasing the airflow rate gradually decreases the influence of EHD flow on the airflow field, the removal effect of particles with size < 6 μm by the EHD flow becomes concealed, and the influence on the overall dedusting efficiency becomes negligible. When the particle size dp and discharge voltage U are increased, the charge elevates and the electric field force strengthens, resulting in particles being guided towards the dust collection electrode and increasing the offset, thus improving the dust removal efficiency. When the gas flow rate u is increased, the offset of particles decreases and the dust removal efficiency declines. The optimal operating conditions for the tubular electrofiltration equipment are U = 45 kV, u = 0.5 m/s, and dp ≥ 6 μm.

There are a significant number of global and regional studies on air pollution prediction using machine learning. This study looks at the application of machine learning to anticipate air pollution, as well as the state of the field right now and its projected expansion. This study searches over 1794 documents created by 5354 academics and published in 745 publications between 1991 and 2023, using Scopus as the primary search engine. For the purpose of identifying and visualising major authors, journals, countries, research publications, and key trends on these concerns, articles published on these themes were evaluated using Biblioshiny, Vosviewer and S-curve analysis. We discover that interest in this subject began to grow in 2017 and has since grown at a rate of 18.56 percent per year. Although prestigious journals such as Environmental Pollution, Atmospheric Environment, and Science of the Total Environment have been at the forefront of advancing research on the application of machine learning to forecast air pollution, these journals are not the only ones doing so. The top four leading countries in terms of total citations are China (6,784 citations), the United Kingdom (2,758 citations), the United States (2145 citations), and India (1,117 citations). The top three most prestigious universities are Fudan University, China (63 articles), the University of Southern California, USA (60 articles), and Tsinghua University, China (56 articles). The authors' keyword co-occurrence network mappings show that machine learning (577 occurrences), air pollution (282 occurrences), and air quality (166 occurrences) are the top three most frequent keywords, respectively. This research focuses on using machine learning to predict air pollution.

This study investigates the complex correlation between air pollution, meteorological factors, and daily COVID-19 cases in central Thailand. The arithmetic means and standard deviations of trace gaseous species, meteorological factors, daily COVID-19 incidence, and PM_2.5 concentrations are displayed. Univariate analysis, using Pearson correlation, shows strong positive relationships with daily COVID-19 incidence and O₃, consistent with global studies. Previous research has found negative connections between the daily average levels of PM_2.5 and NO₂ with O₃. This study investigates the mechanism of the interaction between O₃ and NO_x, with a particular focus on its termination under specific atmospheric circumstances and the subsequent negative correlations between O₃ and NO₂. A Multiple Linear Regression Analysis (MLRA) is performed, which shows significant positive MLRA coefficients for O₃ in different areas of Thailand during the COVID-19 lockdown. The significant decreases in NO₂ and other air pollution emissions are associated with substantial improvements in ground-level O₃. The rise in O₃ levels is linked to an increase in the atmosphere’s ability to oxidize, resulting in the formation of secondary aerosols. This has consequences for human respiratory health and might potentially contribute to a rise in COVID-19 cases and deaths. The existence of positive associations between ground-level O₃ and COVID-19 infections is recognized, taking into account the detrimental impact on respiratory health. Nevertheless, the study prudently acknowledges that a correlation between variables does not necessarily indicate a cause-and-effect relationship. It emphasizes the presence of other influential factors such as population density, healthcare infrastructure, public health initiatives, and socioeconomic determinants that may obfuscate the results. To summarize, the study offers valuable understanding of the intricate relationships among air contaminants, meteorological circumstances, and the occurrence of COVID-19 in Thailand. This highlights the possible influence of ground-level O₃ on respiratory well-being and indicates the necessity for further research to clarify any direct correlation with COVID-19 infection.