Dispersion Of Particulate Matter Research Articles

Efficient particulate matter source localization in dynamic indoor environments: An experimental study by a multi-robot system

This study tackles the urgent issue of indoor particulate matter (PM) pollutants, highlighting the critical need for efficient source localization due to its significant health risks. Moving beyond traditional constant-rate gas emission research, we explore real-world PM releases using a multi-robot system, enabling effective PM source localization in dynamically ventilated environments. Initial data collection on airflow and PM concentrations helps optimize our system for mechanical ventilation challenges. Through 90 experiments across six scenarios, we evaluate the improved whale optimization algorithm (IWOA) and improved particle swarm optimization (IPSO) methods, assessing their ability to identify both constant and variable PM sources and their adaptability to different pollutants. The results show a success rate of 73.3 % for both methods in locating constant PM sources, with IPSO offering a slight edge in efficiency. Moreover, in tasks involving ethanol vapor, the methods exhibited higher success rates, indicating uniform performance across a variety of pollutants. This consistency, alongside the noted variations in success rates, is primarily attributed to the complexities of PM dispersion. Importantly, our study highlights the methods' adaptability to periodic PM sources with variable release rates, maintaining high success without extra localization steps. This research advances indoor air quality management and emphasizes the importance of flexible, effective pollutant control strategies for public health protection.

Comprehending particulate matter dynamics in transit-oriented developments: Traffic as a generator and design as a captivator

In Transit-Oriented Development (TOD), the close integration of residential structures with community activities and traffic heightens residents' exposure to traffic-related pollutants. Despite traffic being a primary source of particulate matter (PM), the compact design of TODs, together with the impact of urban heat island (UHI), increases the likelihood of trapping emitted PM from traffic, leading to heightened exposure of TOD residents to PM. Although PM originates from two distinct sources in road traffic, exhaust and non-exhaust emissions (NEE), current legislation addressing traffic-related PM from non-exhaust emissions sources remains limited. This paper focuses on two TOD typologies in Manchester City—Manchester Piccadilly and East Didsbury—to understand the roles of TOD traffic as a PM generator and TOD place design as a PM container and trapper. The investigation aims to establish correlations between street design canyon ratios, vehicular Speed, and PM10/PM2.5, providing design guidance and effective traffic management strategies to control PM emissions within TODs. Through mapping the canyon ratio and utilising the Breezometer API for PM monitoring, the paper revealed elevated PM levels in both TOD areas, exceeding World Health Organization (WHO) recommendations, particularly for PM2.5. Correlation analysis between canyon configuration and PM2.5/PM10 highlighted the importance of considering building heights and avoiding the creation of deep canyons in TOD design to minimise the limited dispersion of PM. Leveraging UK road statistics and the PTV Group API for vehicle speed calculations, the paper studied the average speeds on the TOD roads concerning PM. Contrary to conventional assumption, the correlation analyses have revealed a noteworthy association shift between vehicular speed and PM concentrations. A positive correlation existed between speed increase and PM increases on arterial roads. However, a negative correlation emerged on main, collector, and local streets, indicating that PM levels rise for both PM10 and PM2.5 as Speed decreases. These findings challenge the traditional assumption that higher Speed leads to increased emissions, highlighting the potential impact of NEE on PM concentrations. This paper calls for thorough design considerations and traffic management strategies in TOD, especially in dense areas, considering building height, optimising traffic flow, and enhancing compromised air quality associated with vehicular emissions.

Simulation of Submicron Particulate Matter (PM1) Dispersion Due to Traffic Rerouting to Establish a Walkable Cultural Tourism Route in Ratchaburi’s Old Town, Thailand

Cultural tourism helps preserve cultural heritage and provides economic opportunities for local communities. A walkable cultural tourism route has been developed for the old town of Ratchaburi, Thailand. Here, we assessed changes in PM1 after cars were banned from the walkable tourist route. A near-roadway dispersion model, R-LINE, was evaluated and used to explore the base case (BC) and two scenarios, S1 and S2. In the BC, road traffic activities reflected the current situation; in S1, all vehicles were banned from the walkable route; and in S2, all drivers were encouraged to park their vehicles outside the study area. The road traffic activities in the study area were observed and used to calculate the PM1 emission rates for the model inputs. The model was capable of simulating PM1 concentration, especially the average PM1 concentration over the monitoring period. An increase in PM1 concentration was seen at the main road in S1 due to the increased traffic volume that had been redirected from the walkable route, with an increase in daily PM1 of 4.5% compared to BC. S2 showed a decrease in the PM1 concentration of 8.9%. These findings suggest the need for traffic mitigation measures prior to initiating a walkable route for cultural tourism, to meet environmental sustainability requirements.

Emission of black carbon in heavy traffic areas of Jamshedpur and Ranchi: Risk exposure to humans

The suspended aerosol particulate matters are global issue towards the environmental loss as it contaminates the atmosphere. This study explains the comprehensive investigation of BC concentration and its health risk estimation in different topographical urbanites i.e., Jamshedpur (JSR), and Ranchi (RNC). The concentration of BC as 15.87 µgm-3 at JSR, and 12.16 µgm-3 at RNC using the portable aethalometer (AE-33) and air sampler (Envirotech APM 550 mini). The BC concentration was observed higher at JSR, it may be due to the high traffic load and industrial emission in AIADA region (Adityapur Industrial Area Development Authority). Similarly, concentration of PM2.5 was observed around 165.4 µgm-3, and 151.3 µgm-3 in above respective sites. The health risk was calculated around 17.4, 21.4, 10.2 and 16.7 number of PSC (passive smoking cigarette) per day concerning to health outcomes of LBW, PLFD, CM and LC respectively in JSR which observed higher compared to RNC (i.e., 10.6, 14.3, 8.5 and 12.8) which might be due to larger consumption of coal and fuel in Industrial city, JSR. Backward Trajectory analysis explained that the dispersal of airborne particulate matter mostly coming from southwestern part of India and Arabian sea in all the two sites.

Effects of street plants on atmospheric particulate dispersion in urban streets: A review

Numerous empirical studies have demonstrated that street trees not only reduce dust pollution and absorb particulate matter (PM) but also improve microclimates, providing both ecological functions and aesthetic value. However, recent research has revealed that street tree canopy cover can impede the dispersion of atmospheric PM within street canyons, leading to the accumulation of street pollutants. Although many studies have investigated the impact of street trees on air pollutant dispersion within street canyons, the extent of their influence remains unclear and uncertain. Pollutant accumulation corresponds to the specific characteristics of individual street canyons, coupled with meteorological factors and pollution source strength. Notably, the characteristics of street tree canopy cover also exert a significant influence. There is still a quantitative research gap on street tree cover impacts with respect to pollution and dust reduction control measures within street spaces. To improve urban traffic environments, policymakers have mainly focused on scientifically based street vegetation deployment initiatives in building ecological garden cities and improving the living environment. To address uncertainties regarding the influence of street trees on the dispersion of atmospheric PM in urban streets, this study reviews dispersion mechanisms and key atmospheric PM factors in urban streets, summarizes the research approaches used to conceptualize atmospheric PM dispersion in urban street canyons, and examines urban plant efficiency in reducing atmospheric PM. Furthermore, we also address current challenges and future directions in this field to provide a more comprehensive understanding of atmospheric PM dispersion in urban streets and the role that street trees play in mitigating air pollution.

Application of CFD Modelling for Pollutant Dispersion at an Urban Traffic Hotspot

Health factors concerning the well-being of the urban population urge us to better comprehend the impact of emissions in urban environments on the micro-scale. There is great necessity to depict and monitor pollutant concentrations with high precision in cities, by constructing an accurate and validated digital air quality network. This work concerns the development and application of a CFD model for the dispersion of particulate matter, CO, and NOx from traffic activity in a highly busy area of the city of Augsburg, Germany. Emissions were calculated based on traffic activity during September of 2018 with COPERT Street software version 2.4. The needed meteorological data for the simulations were taken from a sensor’s network and the resulting concentrations were compared and validated with high-precision air quality station indications. The model’s solver used the steady-state RANS approach to resolve the velocity field and the convection–diffusion equation to simulate the pollutant’s dispersion, each one modelled with different molecular diffusion coefficients. A sensitivity analysis was performed to decide the most efficient computational mesh to be used in the modelling. A velocity profile for the atmospheric boundary layer (ABL) was implemented into the inlet boundary of each simulation. The cases concerned applications on the street level in steady-state conditions for one hour. The results were evaluated based on CFD validation metrics for urban applications. This approach provides a comprehensive state-of-the-art 3D digital pollution network for the area, capable of assessing contamination levels at the street scale, providing information for pollution reduction techniques in urban areas, and combining with existing sensor networks for a more thorough portrait of air quality.

Design, Validation and CFD Modeling of an Environmental Wind Tunnel

The wind erosion of granular materials stored within the open yards of industrial plants (i.e., industrial wind erosion) and the subsequent emission and dispersion of particulate matter (PM) in the surrounding areas represent an important issue for the exposed population and for the environment as a whole. The Environment Impact Assessment (EIA) and the design of emission control measures require a deep knowledge of the erosion phenomenon, and the precise estimation of the Emission Factors (EF) associated with the specific PM source under investigation. Aiming to characterize the emission potential of industrial granular materials, a new Environmental Wind Tunnel (EWT) has recently been built at the Department of Civil and Environmental Engineering and Architecture (DICAAR) laboratories, in Cagliari University. The article discusses the EWT’s updated design and the set-up methodologies applied to reproduce the Atmospheric Boundary Layer (ABL) acting over the surfaces of coarse and heterogeneous granular materials. In addition, a Computational Fluid Dynamics (CFD) model of the EWT has been developed to reproduce and analyze the wind field throughout the entire tunnel volume and preliminarily evaluate possible modifications to the original design. The accuracy of the simulation has been verified by comparing the CFD model and the results of the experimental tests.

Enhancing spatial inference of air pollution using machine learning techniques with low-cost monitors in data-limited scenarios.

Ensuring environmental justice necessitates equitable access to air quality data, particularly for vulnerable communities. However, traditional air quality data from reference monitors can be costly and challenging to interpret without in-depth knowledge of local meteorology. Low-cost monitors present an opportunity to enhance data availability in developing countries and enable the establishment of local monitoring networks. While machine learning models have shown promise in atmospheric dispersion modelling, many existing approaches rely on complementary data sources that are inaccessible in low-income areas, such as smartphone tracking and real-time traffic monitoring. This study addresses these limitations by introducing deep learning-based models for particulate matter dispersion at the neighbourhood scale. The models utilize data from low-cost monitors and widely available free datasets, delivering root mean square errors (RMSE) below 2.9 μg m-3 for PM1, PM2.5, and PM10. The sensitivity analysis shows that the most important inputs to the models were the nearby monitors' PM concentrations, boundary layer dissipation and height, and precipitation variables. The models presented different sensitivities to each road type, and an RMSE below the regional differences, evidencing the learning of the spatial dependencies. This breakthrough paves the way for applications in various vulnerable localities, significantly improving air pollution data accessibility and contributing to environmental justice. Moreover, this work sets the stage for future research endeavours in refining the models and expanding data accessibility using alternative sources.

Modelling the dispersion of particulate matter (PM10) via wind erosion from opencast mining-Moldova Nouă tailings ponds, Romania.

Historically, Romania is known as a mining site of mineral substances, including gold, silver, copper, lead, zin, uranium, manganese, salt, and coal, whereby their long periods of exploitation and extraction affected human health and the environment in various ways. In Moldova Nouă southwest region of Romania, we investigated the environmental impacts of mining activities on air quality over 2021. We quantified PM10 emission rates through in situ monitoring, dispersion modelling, and horizontal and vertical fluxes. Statistical metrics, including the fraction within factor 2 (FAC2), mean bias (MB), mean gross error (MGE), normalized mean bias (NMB), normalized mean gross error (NMGE), coefficient of efficiency (COE), index of agreements (IOAs), and Taylor diagram signifying standards deviation (SD), root mean squared error (RMSE), and correlation coefficient (R), were used to evaluate the reliability of modelling results against observation. Results conclude that PM10 dispersion agrees with MB, MGE, NMB, NMGE, COE, IOA, and Taylor diagram and moderately with FAC2 metrics. PM10 hotspot was investigated in the vicinity of the tailings ponds of 115.5µgm-3 annual mean, 563.7µgm-3 daily mean, 63.3µgm-2s-1 annual horizontal flux, and 3.0µgm-2s-1 annual vertical flux. PM10 dispersion was identified to expand to Moldova Nouă City and nearby country Serbia. Findings concluded that a windy air mass accumulation across the overburdened dumps and ponds causes the increase of PM10 in the air, resulting in the region's pollution. Therefore, results recommend adopting a strategic mitigation measure for residents, policymakers, stakeholders, and urban planners.

Dispersion of Particulate Matters from Separated Plume Stacks using Discrete Phase Modelling

Dispersion of particulate matter from plume stacks of industries is crucial in analysing the extent of air pollution. Particulate matter is highly influenced by gravity causing it settle quickly. Dispersion of pollutants from dual plume stack separated by a certain distance is analysed here. Dual stack is effective in shielding the downstream plume causing it to spread vertically higher than single stack. PM10 particles are emitted along with gaseous pollutants from the stack in to the atmosphere having a temperature gradient of -0.2K/100 m. Particles from the stack disperse to higher distance from the ground when there is pollutant gas along with it. Furthermore, presence of discreet plumes causes splitting of the upstream one in horizontal direction causing the particles to spread more. Present study helps to monitor the spreading of particulate matter from plume stacks.

Reduced Sediment Settling in Turbulent Flows Due To Basset History and Virtual Mass Effects

AbstractThe behavior of suspended particles in turbulent flows is a recalcitrant problem spanning wide‐ranging fields including geomorphology, hydrology, and dispersion of particulate matter in the atmosphere. One key mechanism underlying particle suspension is the difference between particle settling velocity (ws) in turbulence and its still water counterpart (wso). This difference is explored here for a range of particle‐to‐fluid densities (1–10) and particle diameter to Kolmogorov micro‐eddy sizes (0.1–10). Conventional models of particle fluxes that equate ws to wso result in eddy diffusivities and turbulent Schmidt numbers contradictory to laboratory experiments. Incorporating virtual mass and Basset history forces resolves these inconsistencies, providing clarity as to why ws/wso is sub‐unity for the aforementioned conditions. The proposed formulation can be imminently used to model particle settling in turbulence, especially when sediment distribution outcomes over extended time scales far surpassing turbulence time scales are sought.

Analysis of the effect of dust barriers on particulate matter dispersion from a construction site using CFD simulation

Urban construction activities generate particulate matter (PM), which poses a severe threat to urban residents' quality of life. The complex topography of urban areas and turbulent air flow make it difficult to predict PM dispersion and establish control measures. This study predicted PM dispersion from construction sites to urban areas under four wind directions using CFD simulations and determined optimal dust barrier heights for reducing PM exposure. This study showed that PM exposure on residents depends on wind directions in the urban area up to 2.3 times, still a flexible approach to determine optimal barrier heights based on stakeholder preferences was effective in all wind directions. Accurate PM dispersion predictions and control measures are crucial for improving urban air quality and residents' well-being.

Efficacy of green infrastructure in reducing exposure to local, traffic-related sources of airborne particulate matter (PM)

One aim of roadside green infrastructure (GI) is to mitigate exposure to local, traffic-generated pollutants. Here, we determine the efficacy of roadside GI in improving local air quality through the deposition and/or dispersion of airborne particulate matter (PM). PM was collected on both pumped air filters and on the leaves of a recently installed ‘tredge’ (trees managed as a head-high hedge) at an open road environment next to a primary school in Manchester, U.K. The magnetic properties of PM deposited on leaves and filters (size fractions PM10 and PM2.5) were deduced from hysteresis loops, first-order reversal curves (FORCs), and low-temperature remanence measurements. These were complemented with electron microscopy to identify changes in magnetic PM concentration downwind of the tredge/GI. We show that the tredge is permeable to airflow using a simple CO2 tracer experiment; hence, it allows interception and subsequent deposition of PM on its leaves. Magnetic loadings per m3 of air from filters (PM10 saturation magnetisation, Ms, at 5 K) were reduced by 40 % behind the tredge and a further 63 % in the playground; a total reduction of 78 % compared to roadside air. For the PM2.5 fraction, the reduction in magnetic loading behind the tredge was remarkable (82 %), reflecting efficient diffusional capture of sub-5 nm Fe-oxide particles by the tredge. Some direct mixing of roadside and playground air occurs at the back of the playground, caused by air flow over, and/or through gaps in, the slowly-permeable tredge. The magnetic loading on tredge leaves increased over successive days, capturing ~23 % of local, traffic-derived PM10. Using a heuristic two-dimensional turbulent mixing model, we assess the limited dispersion of PM < 22.5 μm induced by eddies in the tredge wake. This study demonstrates that PM deposition on leaves reduces exposure significantly in this school playground setting; hence, providing a cost-effective mitigation strategy.

Specifics of particulate matter dispersion in the atmosphere of a clay quarry

Specifics of particulate matter dispersion in the atmosphere of a clay quarry

Effective use of an extraoral vacuum in preventing the dispersal of particulate matter from metal dental materials.

When dentists adjust prostheses at the dental chairside, particulate matter (PM) from the dental material is dispersed. Little is known about the effective use of an extraoral vacuum (EOV) in preventing PM dispersal. This study aimed to evaluate the effective use of an EOV in preventing the dispersal of metal PM in dental offices. The following experimental conditions were planned: the distance from the EOV to the metallic materials (50, 100, 150, and 200 mm), the horizontal angle between the long axis of the micromotor handpiece and EOV (0°, 45°, and 90°), and the operating time of the EOV (during grinding, during grinding and 1 min thereafter, during grinding and the preceding 1 min, 1 min before and after grinding, and during grinding). Carborundum and silicone points were used for grinding and PM dispersal. Diameters (0.3, 0.5, 1.0, and 3.0 μm) were measured using a laser particle counter. Depending on the instrument used to grind, there were undetectable PMs of different diameters (Carborundum point: 0.3 and 0.5 μm, Silicone point: 0.3 μm). PMNs were reduced as the distance from the EOV to metal materials decreased. Operating the EOV before grinding along the long axis of the micromotor handpiece was effective in reducing the PMNs. PMNs dispersed when grinding metals were effectively reduced by positioning the EOV closer to the grinding surface (within 150 mm) along the long axis of the micromotor handpiece and operating the EOV before grinding.

Simulation of Fine Particulate Matter Dispersion from the Grand Cereals Industry Jos, Nigeria Using a Modified Gaussian Plume Model

Air pollution in Jos, Nigeria, is concerning due to a variety of anthropogenic sources which expose residents to possible respiratory health risks. Good emission source data and an understanding of all elements connected to air pollution is the foundation for successful pollution abatement. The study established a model for crosswind-integrated concentrations by solving the advection-diffusion equation using the reducible and irreducible techniques and utilizes it to forecast the concentration of PMX pollutants released from a stack in Jos, Nigeria. Data was collected from the Grand Cereals environment using a Handheld Portable Particle Counter for PMx with model number CW-HAT 200 for a year. High values of fine particulates recorded in the study are worrisome because they represent a threat to human health. Moreover, the monitored and modeled results evidenced a higher risk for human health in specific points, particularly areas less than 100m away from the stack which is seen as deriving from the stack emissions. Electrostatic precipitators or mist collectors can be installed to reduce the PMX emissions and its impact on human health. We proposed that the boiler stack be replaced with a taller one and that people living and working near the industry wear a nasal mask to mitigate inhaling dangers. The model performs better in estimating PM2.5 concentrations under unstable conditions and is recommended for PM2.5 monitoring.

Atmospheric deposition of particulate matter from beef cattle feedlots is a likely contributor of pyrethroid occurrence in isolated wetland sediment: Source apportionment and ecological risk assessment

Industrial cattle feeding operations (feedlots) have been subject to public scrutiny in recent decades regarding environmental impacts of site runoff and aerial dispersion of agrochemical-laden particulate matter (PM). However, source apportionment of multi-use pesticides is challenging in mixed agricultural settings. Beef cattle on feed and row crop production are heavily concentrated in the Southern Great Plains of North America, where playa wetlands are vulnerable to agrochemical inputs and sedimentation from surrounding land use. In the current study, playa basin sediment (n = 33) was analyzed via UHPLC-MS for 21 agrochemicals spanning eight classes (macrocyclic lactones, neonicotinoids, organophosphates, pyrethroids, triazoles, β-methoxyacrylates, a carboximide, and phenylpyrazole). Pyrethroids were detected most frequently (75.8% of basins). Sediment pyrethroid concentrations were also significantly correlated (R2 = 0.178, p = 0.007) with feedlot proximity (<1–50 km). Principal component analysis (PCA) of land use metrics extracted three principal components (74.3% of total variance), with principal component regression (PCR) showing the greatest agrochemical occurrence in basins heavily weighted by cropland buffer acreage (≤1 km) and feedlot proximity. Sediment toxicity benchmarks protective of two benthic invertebrates (Hyallela azteca and Chironomus spp.) identified λ-cyhalothrin, fenvalerate, and esfenvalerate as individual compounds exceeding levels of acute (RQ > 0.5) and chronic (RQ > 1) concern in >5% and >50% of cases, respectively. However, additive toxicity of co-occurring pyrethroids represents an acute high risk (RI > 1; median RI; acute = 2.4, chronic = 38.6) to benthic invertebrates in >75% of cases, which may threaten higher-order wetland taxa via bioaccumulation and trophic transfer.

Estimation methods for the ratio of medians of three-parameter lognormal distributions containing zero values and their application to wind speed data from northern Thailand.

Wind speed has an important impact on the formation and dispersion of fine particulate matter (PM), which can cause several health problems. During the transition from the winter to the summer season in northern Thailand, the wind speed has been low for longer than usual, which has resulted in fine PM accumulating in the air. Motivated by this, we have identified a need to investigate wind speed due to its effect on PM formation and dispersion and to raise awareness among the general public. The hourly windspeed can be approximated by using confidence intervals for the ratio of the medians of three-parameter lognormal distributions containing zero values. Thus, we constructed them by using fiducial, normal approximation, and Bayesian methods. By way of comparison, the performance measures for all ofthe proposed methods (the coverage percentage, lower and upper error probabilities (LEP and UEP,respectively), and expected length) were assessed via Monte Carlo simulation. The results of Monte Carlo simulation studies show that the Bayesian method provided coverage percentages close to the nominal confidence level and shorter intervals than the other methods. Importantly, it maintained a good balance between LEP and UEP even for large variation and percentage of zero-valued observations. To illustrate the efficacy of our proposed methods, we applied them to hourly wind speed data from northern Thailand.

Investigation of total suspended particulate matter dispersion from quarries in Artvin, Turkey, using AERMOD and its relationship with topography

Investigation of total suspended particulate matter dispersion from quarries in Artvin, Turkey, using AERMOD and its relationship with topography

The Impact of Meteorological Conditions and Agricultural Waste Burning on PM Levels: A Case Study of Avellino (Southern Italy).

In this work, the effect of the meteorological conditions and the agricultural waste burning on PM air pollution levels has been investigated in the city of Avellino, located in the Sabato Valley (southern Italy). Avellino has been described among the most polluted towns in Italy in terms of particulate matter (PM) during the last 10 years. The main aim of this study was to analyze the air quality data collected in Avellino and its surroundings during September 2021. In this period, the air quality in the Sabato Valley has been adversely affected by agricultural practices, which represent a significant source of PM. The impact of agricultural waste burning on PM levels in Avellino has been determined through an integrated monitoring network, consisting of two fixed urban reference stations and by several low-cost sensors distributed in the Sabato Valley. In the considered period, the two reference stations recorded several exceedances of the daily average PM10 legislative limit value (50 µg m−3) in addition to high concentrations of PM2.5. Moreover, we provide a detailed description of the event that took place on 25 September 2021, when the combined effect of massive agricultural practices and very stable atmospheric conditions produced a severe pollution episode. Results show PM exceedances in Avellino concurrent with high PM values in the areas bordering the city due to agricultural waste burning and adverse meteorological conditions, which inhibit PM dispersion in the atmosphere.