Aerosol Science and Engineering最新文献

Air pollution is a global issue, and its health impacts are discussed at the major level. Among different types of air pollutants, Particulate Matter (PM) is a primary pollutant that causes serious health issues related to pulmonary functions. India, one of the rapidly developing countries propelled by intense urbanization and industrial growth, faces escalating emissions of air pollutants in its major urban cities. This study estimates the air quality and associated respiratory deposition doses (RDD) of PM_2.5 and PM₁₀ in five major cities of India: Delhi, Mumbai, Kolkata, Chennai, and Bangalore from 2019 to 2022. The study collectes air quality data from the air quality monitoring station, data are analysed using R software and the visualizations are done using Origin software. The study period experienced different emission patterns due to restrictions imposed on anthropogenic sources due to the widespread pandemic. The study helps to estimate the role of anthropogenic sources on urban air quality and found that reducing sources improves air quality and leads to less exposure. The PM_2.5 concentration in the cities ranges from 17 to 65 µg/m³, and PM₁₀ ranges from 41 to 178 µg/m³ for four consecutive years. The walking mode RDD for PM_2.5 ranged from 0.52 to 1.42 µg/min and 1.21 to 3.73 µg/min for PM₁₀. Similarly, the RDD ranged for sitting mode from 0.18 to 0.51 µg/min for PM_2.5 and 0.45 to 1.34 µg/min for PM₁₀. In general, over the four years of study period, Delhi city experienced the highest pollution load, and Mumbai experienced the lowest. The maximum reduction of RDD values was found in Kolkata, with a 41% reduction. The study outcomes revealed the role of anthropogenic emissions in urban air quality and emphasize the need to adopt mitigation measures to improve air quality and human health.

To investigate the characteristics and sources of atmospheric fine particulate matter (PM_2.5) in the medium-sized cities in East China, continuous observation of PM_2.5 was conducted in Huai’an City from April 18th to May 11st, 2021. During the observation process, the average mass concentration of PM_2.5 was 58.5 ± 26.9 µg/m³, with a low-to-high trend for observation periods: midnight and early morning < night < morning < afternoon. The composition of PM_2.5 remained consistent across all sampling periods, with the highest content being water-soluble ions, followed by carbonaceous components. The total concentration of water-soluble ions in PM_2.5 accounted for 43.4% of PM_2.5, and the secondary inorganic components (NH₄ ⁺, NO₃^-, and SO₄^2-) were the main ion components, accounting for 36.1%, 33.6%, and 18.2% of the total ion concentration, respectively. The organic carbon (OC) and element carbon (EC) were 11.5 ± 5.0 µg/m³ and 1.4 ± 0.9 µg/m³, with OC/EC ratio more than 2 in all periods, indicating a significant presence of secondary pollution throughout the observation process. The positive matrix factorization (PMF) model results indicate that the atmospheric PM_2.5 in Huai’an was influenced by vehicle exhaust (29.6%), other sources (19.0%), dust sources (18.5%), and secondary sources (13.9%). The sources of PM_2.5 were mainly secondary sources during midnight and early morning (18.0%), soil dust during morning and night (21.7% and 20.0%), and motor vehicle exhaust in the afternoon (21.8%), respectively. The results of this study have significance for the scientific prevention and control of atmospheric PM_2.5 in East China.

In recent years, the problem of atmospheric particulate pollution has become more and more serious. Atmospheric fine particulate matter (PM_1.0) has a large specific surface area. PM_1.0 can carry a large number of metal elements into the depths of human lungs and blood circulation through the respiratory tract. In this paper, the PM_1.0 in autumn and winter in Harbin was taken as the research object. The mass and number concentration of PM_1.0 were analyzed. The metal elements in PM_1.0 were detected by inductively coupled plasma emission spectrometer (ICP-OES) and inductively coupled plasma mass spectrometry (ICP-MS). The top priority of this study was the source analysis of metal pollution in PM_1.0 using scanning electron microscopy (SEM) and positive matrix factor (PMF) method. It was found that the PM_1.0 number concentration was high in autumn and winter in Harbin. Particulate matter tended to accumulate and was not easy to settle due to the low temperature and relative humidity. In autumn and winter, combustion of fossil fuels such as coal and oil, soil dust and motor vehicle exhaust were the primary sources of PM_1.0.

Cities in the air pollution transmission corridor of Beijing–Tianjin–Hebei, also known as the "2 + 26" cities, face considerable challenges regarding air pollution control and carbon reduction (APCR). However, few studies have explored the county-level synergy between CO₂ emissions and air pollution. In this study, we take 327 counties in the "2 + 26" cities as the study area, introduce bivariate and trivariate synergistic indicators (BSI and TSI) to quantify the synergistic characteristics of air pollution and CO₂ emissions from 2016 to 2019, and comprehensively analyze their spatial variation and aggregation characteristics. The results revealed that: (1) The annual average concentration of PM_2.5 in counties under the jurisdiction of municipalities and provinces has a downward trend year by year, while the annual average concentration of O₃ continues to increase, and the total amount of CO₂ emissions continues to rise. (2) The number of counties with significant PM_2.5-O₃ synergies increased considerably from 2016 to 2019, whereas more than 95% of the counties in Beijing and Tianjin showing large low-value aggregation areas of BSI_CO2-PM2.5 and low-value aggregation areas of BSI_CO2-O3. (3) In each year from 2016–2019, more than 64% of counties have TSI values less than 2, the average TSI value of counties fluctuates, and changes between 1.83–2, and the distribution of high-value aggregation areas of TSI values is also more dispersed, all of which reflect the characteristics of the weak trivariate synergistic effect in the counties. The results furnish empirical data and policy recommendations for the synergistic governance of APCR at the county level.

In this article, a previously developed model that was set to describe the deposition of particles onto the internal smooth surfaces of small containers is modified to account for rough surfaces. This is basically achieved by modifying the limits of the boundary layer that are used for calculating the deposition velocities. Deposition inside a ventilation duct is taken as an example for performing the applications. The deposition velocities onto all surfaces with different orientations are calculated in comparisons to the experimental data. Different friction velocities are considered, where the shifts in the lower limit of the boundary layer are put in relation to the friction velocities. The presented model has the merit of being able to predict the shift in the boundary layer limit for any given friction velocity, which is a very important parameter that is required for describing the deposition and penetration of aerosol particles through ventilation ducts with rough surfaces.

Calcium (Ca) is a principal element of mineral dust in the atmosphere, and its presence varies in time and space, making its distribution in widespread aerosol particles and environmental significance poorly understood. Using a scanning electron microscope, we analyzed and compared Ca-rich particles collected during slow-moving and fast-moving dust events in southwestern Japan. The abundance and occurrence of the particles differed significantly between the two types of events. In slow-moving events, their number fraction among the total mineral particles ranged from 51.2 to 55.6%, while in fast-moving events, the fraction was much lower, ranging from 3.6 to 16.7%. The Ca-rich particles in slow-moving events were larger (with a peak size of about 2.0 μm) and rounder (with a roundness peak of about 0.8) than those in fast-moving events (about 1.4 μm and 0.7, respectively), suggesting a more aged state of the particles in slow-moving dust events. The Ca-rich particles were attributed to anthropogenic emissions based on their distinctive characteristics and likely entrained into the dusty air when the events passed populated areas in eastern China. These results indicate substantial anthropogenic Ca-rich particles within slow-moving Asian dust plumes, and their active involvement in atmospheric physical and chemical processes.

Low-cost sensors (LCS) have gained significant attention in recent years due to their application in urban air quality mapping, community monitoring networks, indoor air quality monitoring, personal exposure monitoring, and citizen science initiatives. This study has developed an integrated approach combining measurements from LCS and existing source apportionment (SA) results with machine learning (ML) algorithms to achieve real-time SA. Source contributions apportioned by Chemical Mass Balance (CMB) model and PM_2.5 as well as particle number concentration (PNC) in size bins (0–0.3 μm, 0.3–0.5 μm, 0.5–1 μm, and 1–2.5 μm) from LCS are acquired from May 2019 to February 2020 at Major Dhyan Chand National Stadium (NS), Delhi. The PNC in size bins was converted to mass (PM_{0 − 0.3}, PM_{0.3 − 0.5}, PM_{0.5 − 1}, PM_{1 − 2.5}) for respective sizes. The objective function is {S₁, S₂, S₃, …. S₈} = f {PM_{0 − 0.3}, PM_{0.3 − 0.5}, PM_{0.5 − 1}, PM_{1 − 2.5}, PM_2.5} where S₁, S₂, S₃, …. S₈ are the sources. Four ML algorithms, namely support vector regression (SVR), k-nearest neighbour (kNN), random forest (RF) and gradient boosting (GB), are applied for SA. GB performs the best among all algorithms with a train and test score (R²) of 0.82 and 0.75. The R² (in parentheses) between actual and predicted PM_2.5 for sources of biomass burning (0.92), dust (0.83), gasoline vehicle (0.75), diesel vehicle (0.78), coal combustion (0.70), waste burning (0.76), industrial (0.77) and secondary aerosol (0.89) indicate the acceptable performance of the model. The statistical t-test comparing the PM_2.5 contributions obtained from CMB and ML for each source indicates no significant difference (p > 0.05) except for dust and waste burning. This study demonstrated the ability of LCS to perform real-time SA with the help of an existing dataset. This cost-effective approach will provide rough estimations of the sources to regulatory agencies and policymakers for immediate action.

The particulate matter (PM) is known to cause cardiopulmonary diseases as it is redox-active and generates reactive oxygen species (ROS) in the human body. In this study, PM_1.0 and PM_2.5 samples were collected at Agra, India, from July to November 2022. These samples were analyzed for their oxidative potential (OP) using the dithiothreitol (DTT) Assay. The data was classified as seasonal (monsoon and post-monsoon) for different environments. The overall average PM_1.0 mass concentrations in ambient air were 17 ± 7, 19 ± 8, and 31 ± 33 μg/m³ at urban, roadside and rural locations, respectively. Similarly, the overall PM_2.5 mass concentrations in ambient air were 40 ± 17, 53 ± 26, and 82 ± 104 μg/m³ at urban, roadside, and rural locations, respectively. The results showed that the oxidative potential, OP-DTT_v, was higher at urban and roadside for PM_2.5. However, OP-DTTm was higher at urban and roadside locations for PM_1.0. At rural sites, both OP-DTT_v and OP-DTT_m were higher for PM_1.0. This study highlights the importance of understanding the oxidative potential of PM in comprehensively assessing health risks associated with reactive oxygen species in different environments.

Graphical Abstract

Optimizing the form and parameters of ventilation systems is crucial for enhancing the microenvironment around individuals, with a primary focus on human comfort in ventilation design. Additionally, controlling exposure concentrations of respiratory droplets is an essential strategy for dealing with respiratory infections. Therefore, a thorough examination of the relationship between the form and parameters of ventilation systems, human comfort, and the dispersion of droplets becomes particularly significant. This study utilizes computational fluid dynamics (CFD) to optimize ventilation systems, focusing on enhancing individual comfort and reducing droplet dispersion in indoor environments, particularly in cruise cabins where the microenvironment significantly impacts passenger well-being. It evaluates three ventilation systems: orifice plate ventilation system (OPVS), ceiling mixed ventilation system (CMVS), and sidewall mixed ventilation system (SMVS). Employing the Entropy-weighted TOPSIS method, it optimizes ventilation temperature and relative humidity across 20 combinations to achieve optimal thermal comfort and airflow uniformity. The findings indicate that OPVS offers the best thermal comfort and uniform airflow, with an ideal configuration at 21 °C and 60% relative humidity. It also investigates the placement of air purifiers under the optimal ventilation configuration (OPVS), revealing that positioning them near the breathing zone reduces droplet concentrations by 42.6%, while central placement achieves a reduction of 40.1%. This suggests central air purifier placement for practical applications, balancing droplet concentration reduction with minimal occupant disturbance. This work contributes to understanding ventilation strategies for managing respiratory diseases and ensuring indoor comfort.

As the hotspots of population, energy consumption and economic activities, cities contribute > 70% anthropogenic CO₂ emissions, and have become key areas for emission reduction and tackling climate change. Understanding city’s CO₂ emissions is a prerequisite and a fundamental step in formulating strategies to achieve net-zero emissions target before 2060. Chongqing is one of the four municipalities, which plays a critical guiding and exemplary role in promoting Chinese city carbon emissions reduction. However, the high-resolution spatial characteristics and driving forces of anthropogenic CO₂ emissions in Chongqing are still not well understood. Here, we compiled a 1 km × 1 km high-resolution gridded CO₂ emission data based on point, line, and area emission sources, including accurate geographical locations and emissions of 1949 enterprises, four types of traffic roads with different weighting factors, and activity data collected from the statistical yearbook at district and county scales. Results showed that the total CO₂ emissions of Chongqing was 160 million tonnes (Mt) in 2020, of which more than 70% emissions were agglomerated in the urban areas. Energy consumptions are responsible for a considerable part (82%) of the total emissions. These findings improve our understanding of site-specific, sectoral, and county-level anthropogenic CO₂ emissions in Chongqing, as well as providing a key input for carbon monitoring and atmospheric inversion to support carbon peaking and carbon neutral assessment.