Concentration Distribution Of Particulate Matter Research Articles

Analysis of gas flow and PM movement characteristics inside diesel particulate filter channel

Based on experimental data from engine after-treatment tests, the diesel particulate filter (DPF) microscopic channel model was constructed using UG, and the accumulation of particulate matter (PM) in the deep bed filter layer and carbon deposit layer was analyzed through computational fluid dynamics (CFD). A comprehensive study was conducted on the exhaust gas flow characteristics and PM movement within the DPF channel, and focus on the effects of PM distribution type on capture and regeneration characteristics. The results show that with the increase in particle size in the exhaust, the distribution of PM concentration along the axial position of the channel shows a trend of gradually shifting from 0.2 to 0.25 m (rear section) to 0.125–0.2 m (front section). In particular, PM with 1 μm particle size has a large motion inertia, which is easy to move out of the airflow and deposit in the middle part of the channel. For PM in the front and middle sections of the channel, the airflow obstruction is relatively small, but the heat transfer loss is large, which is not beneficial for PM regeneration in the rear section. Therefore, with the distribution of LID, PD-1, and PD-2, the regeneration efficiency is relatively low, at 50.84%, 55.01%, and 54.59%, respectively.

Operator learning for urban water clarification hydrodynamics and particulate matter transport with physics-informed neural networks

Computational fluid dynamics (CFD) can be a powerful tool for higher-fidelity water infrastructure planning and design. Despite decades of development and demonstration over a wide range of water systems such as clarification basins, activated sludge processes, ozone contactors, etc., CFD remains primarily used in academic research, with limited application in civil and environmental engineering practice. This limitation is contributed by its higher computational cost and demand for specialized user skills. This, however, need not be the case, if a robust and efficient surrogate model can be developed from CFD simulations and independently deployed for engineering purposes. Leveraging the emerging scientific machine learning (ML) techniques of physics-informed ML and operator learning, this study develops a composite neural network (CPNN) for learning the flow hydrodynamics and particulate matter (PM) transport and fate in clarification systems. The CPNN consists of a deep operator network (DeepONet) as an encoder and a physics-informed neural network (PINN) as a decoder. In contrast to common “black box” and lumped ML approaches, the developed CPNN directly incorporates physics principles into its architecture. Furthermore, the CPNN is designed for process-resolved and operator learning, enabling it to predict spatial hydrodynamics and PM concentration distribution (i.e., contours) across different basin geometrics and loading conditions. Compared to CFD simulation, the developed CPNN model has significantly higher computational efficiency (∼ milliseconds) while demonstrating robust predictive capability. For predicting basin hydrodynamics across 10,000 test cases, the trained CPNN model achieves an R2 above 0.8 for 66.4% of cases and an R2 above 0.4 for 89.2% of cases. A similar performance is also demonstrated by the CPNN in predicting basin PM concentration. Further investigation reveals that basin geometrics that trigger bi-modal flow solutions can be particularly challenging for ML. Additionally, this study visualizes the dependency of basin hydrodynamics and PM concentration on basin geometrics and loading conditions, providing valuable insights for optimizing basin configuration. Lastly, the potentials and benefits of web-based applications, e.g., DeepXtorm, as a user-friendly interface for the developed CPNN model is discussed. This study represents the initial step toward achieving real-time higher-fidelity water infrastructure planning, design, optimization, and regulation.

Structure of an urban green space indirectly affects the distribution of airborne particulate matter: A study based on structural equation modelling

Urban green spaces can effectively regulate microclimate and improve air quality. However, the relationship between the structure of an urban green space, meteorological conditions, and the concentration distribution of different sized particles inside and outside of green spaces is unclear. In this study, a field survey was conducted on the structures of 188 green spaces in Beijing. The temperature and relative humidity were also monitored, and the concentrations of different sized particles were measured both inside and outside of the selected green spaces to comprehensively analyse the relationships between these variables and the structure of a green space. The results of structural equation modelling showed that the structure of a green space indirectly affected the concentration distribution of particulate matter (PM) through the effects of cooling and humidification, while the direct effect was not obvious. In addition, the impact mechanisms of the structure of a green space on the concentration distribution of particles differed between PM1, PM1–5, and PM5–25, primarily due to the influence of particle size and meteorological factors on the movement of PM. This study deepens the understanding of the influencing mechanisms of the structure of a green space on the concentration distribution of PM, thus providing an important reference for subsequent related field and numerical simulation research.

Surrogate-Assisted Fine Particulate Matter Exposure Assessment in an Underground Subway Station.

With the increase in subway travelers, the air quality of underground enclosed spaces at subway stations has attracted much more attention. The study of pollutants exposure assessment, especially fine particulate matter, is important in both pollutant control and metro station design. In this paper, combining pedestrian flow analysis (PFA) and computational fluid dynamics (CFD) simulations, a novel surrogate-assisted particulate matter exposure assessment method is proposed, in which PFA is used to analyze the spatial-temporal movement characteristics of pedestrians to simultaneously consider the location and value of the pedestrian particulate generation source and their exposure streamline to particulate matter; the CFD model is used to analyze the airflow field and particulate matter concentration field in detail. To comprehensively consider the differences in the spatial concentration distribution of particulate matter caused by the time-varying characteristics of the airflow organization state in subway stations, surrogate models reflecting the nonlinear relationship between simulated and measured data are trained to perform accurate pedestrian exposure calculations. The actual measurement data proves the validity of the simulation and calculation methods, and the difference between the calculated and experimental values of the exposure is only about 5%.

Low-cost sensor system for monitoring the oil mist concentration in a workshop.

Metalworking fluids used in industrial workshops may present a major threat to the health of workers who have been exposed to a high oil mist concentration over a long period of time. Therefore, monitoring the temporal and spatial distribution of particulate matter concentration has great practical significance for the control of oil mist. Traditional particle monitors are generally cumbersome, expensive, and difficult to maintain, which to some extent restricts their extensive use in workshops. Recent years have witnessed tremendous developments in the area of low-cost sensors, which are of great help in obtaining high-density pollution data. In this paper, we evaluate the performance of an inexpensive laser sensor (A4-CG) during long-term oil mist monitoring in a machine shop for the first time. With the use of Lora technology, we developed an online oil mist monitoring network to access real-time concentration, temperature, and humidity information from distributed monitors. According to the results, the sensor data correlated well with measurements by the reference instrument (R2 = 0.96), which means that the distributed sensor network can accurately detect the concentration level of oil mist in the workshop. In fact, most of the sensors demonstrated stable operation for up to half a year according to cluster analysis, while several sensors exhibited serious data drift. Furthermore, the results indicate that the peak oil mist concentration in most areas during production exceeded the value of 0.5 mg m-3 recommended by NIOSH, and it was found that appropriately lowering the relative humidity can make sampling more accurate, while lowering the temperature can reduce the oil mist concentration in the workshop. Thus, measures to control oil mist such as generation and distribution of pollution sources should be on the agenda.

Temporal Changes in Air Quality According to Land-Use Using Real Time Big Data from Smart Sensors in Korea.

This study analyzed the changes in particulate matter concentrations according to land-use over time and the spatial characteristics of the distribution of particulate matter concentrations using big data of particulate matter in Daejeon, Korea, measured by Private Air Quality Monitoring Smart Sensors (PAQMSSs). Land-uses were classified into residential, commercial, industrial, and green groups according to the primary land-use around the 650-m sensor radius. Data on particulate matter with an aerodynamic diameter <10 µm (PM10) and <2.5 µm (PM2.5) were captured by PAQMSSs from September‒October (i.e., fall) in 2019. Differences and variation characteristics of particulate matter concentrations between time periods and land-uses were analyzed and spatial mobility characteristics of the particulate matter concentrations over time were analyzed. The results indicate that the particulate matter concentrations in Daejeon decreased in the order of industrial, housing, commercial and green groups overall; however, the concentrations of the commercial group were higher than those of the residential group during 21:00–23:00, which reflected the vital nighttime lifestyle in the commercial group in Korea. Second, the green group showed the lowest particulate matter concentration and the industrial group showed the highest concentration. Third, the highest particulate matter concentrations were in urban areas where commercial and business functions were centered and in the vicinity of industrial complexes. Finally, over time, the PM10 concentrations were clearly high at noon and low at night, whereas the PM2.5 concentrations were similar at certain areas.

Temporal and spatial distributions of particulate matters around mining areas under two coal mining methods in arid desert region of northwest China

Abstract The particulate matter (PM) in coal mining can bring pollution to the surrounding environment and have an adverse effect on human health. In order to prevent and control the PM pollution in coal mine and better understand the PM transportation in the air, spatial and temporal distribution of PM concentration in two typical coal mining methods – opencast and above-ground of underground coal mines – were studied in the arid desert region of northwest China. The mass concentrations of particulate matters, i.e., PM1, PM2.5, PM10 and TSP (total suspended particulate), were monitored by portable environmental PM meter during two windy seasons — spring and winter. The results show that the PM concentrations in both mining areas all exceeded a certain range of concentration limits. In the opencast mine, high concentrations of PM appeared in the mining area (MA), while it was concentrated in the coal yard (CY) in the above-ground of underground mine. The variations of pollution index (PI) showed similar patterns in both spring and winter at the opencast and above-ground of underground mine area respectively. In the opencast mine, the PM10 and TSP concentrations varied more obviously from season to season and from area to area than the concentrations of PM1 and PM2.5. Barometric pressure had the most significant influence on PM1, PM2.5 and PM10. Wind speed had the greatest influence on TSP. In the above-ground of underground mine, the variation patterns of the concentration of the four different-sized particulate matters were basically the same from area to area. The concentrations of PM1 and PM2.5 had greater seasonal variation than PM10 and TSP. The most important meteorological factors were temperature and barometric pressure for PM1 and PM2.5, while air relative humidity had the greatest impact on PM10 and TSP.

Characteristics of the atmospheric boundary layer and its relation with [formula omitted] during haze episodes in winter in the North China Plain

Interactions between the spatiotemporal distribution of pollutants and the structure of the atmospheric boundary layer were studied using data obtained by GPS (Global Positioning System) sounding balloons in an intensive observation period from December 2018 to January 2019 at the Dezhou experimental station in the North China Plain. Under haze weather conditions, negligible variation or a slight increase in temperature, higher relative humidity (RH) and lower wind speed with uncertain wind direction are common characteristics. The concentration distribution of particulate matter ≤2.5 μm (PM2.5) has a close relationship with the inversion layer, which contributes to the accumulation of PM2.5 in the lower atmosphere. The reduction of pollutants suspended in the upper layer during haze periods is closely related to low-level jets and intermittent turbulence. Higher RH values are also favourable for the formation of heavy haze, and the value of PM2.5 increases with an increase in humidity. During hazy days, the heat fluxes and turbulent kinetic energy (TKE) are much smaller than those during clear days. The values of the average maxima of net radiation, sensible heat flux, and latent heat flux are 154, 76, and 15 W/m2, respectively, and the value of TKE is approximately 0.67 m2/s2. The decrease in atmospheric boundary layer height (ABLH) is caused by weaker turbulent transfer during haze episodes. The ABLH is approximately 400 m during the daytime and 240 m at night. The power function relationship is shown by a negative correlation between the ABLH and surface PM2.5 concentration in the convective boundary layer.

Vertical Distribution of Particulates within the Near-Surface Layer of Dry Bulk Port and Influence Mechanism: A Case Study in China

Knowing the vertical distribution of ambient particulate matter (PM) will help port authorities choose the optimal dust-suppression measures to reduce PM concentrations. In this study, we used an unmanned aerial vehicle (UAV) to assess the vertical distribution (0–120 m altitude) of PM in a dry bulk port along the Yangtze River, China. Total suspended particulates (TSP), PM10, and PM2.5 concentrations at different altitudes were measured at seven sites representing different cargo-handling sites and a background site. Variations in results across sites make it not suitable to characterize the vertical distribution of PM concentration at this port using simple representative distributions. Bulk cargo particle size, fog cannon use, and porous fence all affected the vertical distribution of TSP concentrations but had only minor impacts on PM10 and PM2.5 concentrations. Optimizing porous fence layout according to weather conditions and cargo demand at port have the most potential for mitigating PM pollution related to port operation. As ground-based stations cannot fully measure vertical PM distributions, our methods and results represent an advance in assessing the impact of port activities on air quality and can be used to determine optimal dust-suppression measures for dry bulk ports.

The Effect of Vegetation Enhancement on Particulate Pollution Reduction: CFD Simulations in an Urban Park

Vegetation in parks is regarded as a valuable way to reduce particulate pollution in urban environments but there is little quantitative information on its effectiveness. The aim of this study was to conduct on-site measurements and computational fluid dynamic (CFD) simulations to determine the aerodynamic and deposition effects of vegetation enhancement on particulate matter (PM) dispersions in an urban park in Xi’an, China. Initially, the airflow characteristics and deposition effects of vegetation were predicted and compared with measured air velocities and particulate pollution data to validate the numerical modeling. Then, associated coverage ratios and supplementary green areas (tree coverage ratio, crown volume coverage (CVC), and roof greening) were added to numerical simulations. After a series of numerical simulations and comparisons, results indicated that: (1) Numerical models with simplified vegetation method could reproduce the distribution of particulate matter concentrations in the real park environment; (2) with a tree coverage ratio &gt;37.8% (or CVC &gt; 1.8 m3/m2), the pedestrian-level PM2.5 could meet the World Health Organization’s air quality guidelines (IT-1) standards in the park; (3) roof greening on leeward buildings produced greater PM removal effects compared with windward buildings; and (4) the most economical and reasonable tree coverage ratio and CVC to reduce atmospheric PM in urban parks should be 30% and 1.8 m3/m2, respectively. These results are useful guidelines for urban planners towards a sustainable design of vegetation in urban parks.

Fine-resolution mapping of particulate matter concentration in urban areas and population exposure analysis via dispersion modeling: a study in Daejeon, South Korea.

To deliver accurate particulate matter information to citizens, a detailed particulate matter dispersion model including factors such as land use and meteorological information was developed and used to create particulate matter concentration distribution maps for Daejeon Metropolitan City (South Korea). The results showed differences from existing particulate matter concentration distribution maps created using established methods. For PM2.5, approximately 3600 concentration maps were constructed. Taking a map as an example, according to existing methods, the PM2.5 concentration was "good" in 56% and "moderate" in 44% of areas. However, according to our modeling, the PM2.5 concentration was good in 31%, moderate in 26%, "unhealthy" in 28%, and "very unhealthy" in 15% of areas. Furthermore, the existing methods indicated that no portion of the population was exposed to poor particulate matter concentrations, while the proposed model found that over 170,000 people were exposed to such concentrations. These results will contribute to sustainable urban and environmental planning.

ESTIMATION OF PM2.5 VERTICAL DISTRIBUTION USING CUSTOMIZED UAV AND MOBILE SENSORS IN BRGY. UP CAMPUS, DILIMAN, QUEZON CITY

Abstract. As the unmanned aerial vehicle (UAV) technology has gained popularity over the years, it has been introduced for air quality monitoring. This study demonstrates the feasibility of customized UAV with mobile monitoring devices as an effective, flexible, and alternative means to collect three-dimensional air pollutant concentration data. This also shows the vertical distribution of PM concentration and the relationship between the PM2.5 vertical distribution and the meteorological parameters within 500 m altitude on a single flight in UP Diliman, Quezon City. Measurement and mapping of the vertical distribution of particulate matter (PM)2.5 concentration is demonstrated in this research using integrated air quality sensors and customized Unmanned Aerial Vehicle. The flight covers an area with a radius of 80 meters, following a cylindrical path with 40-meter interval vertically. The PM2.5 concentration values are analyzed relative to the meteorological parameters including air speed, pressure, temperature, and relative humidity up to a 500 meter-flying height in a single flight in Barangay UP Campus, UP Diliman, Quezon City. The study shows that generally, the PM2.5 concentration decreases as the height increases with an exception in the 200–280 m above ground height interval due to a sudden change of atmospheric conditions at the time of the flight. Using correlation and regression analysis, the statistics shows that PM2.5 concentration has a positive relationship with temperature and a negative relationship with relative humidity and wind speed. As relative humidity and wind speed increases, PM2.5 decreases, while as temperature increases, PM2.5 also increases.

The influence of wind speed on airflow and fine particle transport within different building layouts of an industrial city

ABSTRACTIn atmospheric environment, the layout difference of urban buildings has a powerful influence on accelerating or inhibiting the dispersion of particle matters (PM). In industrial cities, buildings of variable heights can obstruct the diffusion of PM from industrial stacks. In this study, PM dispersed within building groups was simulated by Reynolds-averaged Navier-Stokes equations coupled Lagrangian approach. Four typical street building arrangements were used: (a) a low-rise building block with Height/base H/b = 1 (b = 20 m); (b) step-up building layout (H/b = 1, 2, 3, 4); (c) step-down building layout (H/b = 4, 3, 2, 1); (d) high-rise building block (H/b = 5). Profiles of stream functions and turbulence intensity were used to examine the effect of various building layouts on atmospheric airflow. Here, concepts of particle suspension fraction and concentration distribution were used to evaluate the effect of wind speed on fine particle transport. These parameters showed that step-up building layouts accelerated top airflow and diffused more particles into street canyons, likely having adverse effects on resident health. In renewal old industry areas, the step-down building arrangement which can hinder PM dispersion from high-level stacks should be constructed preferentially. High turbulent intensity results in formation of a strong vortex that hinders particles into the street canyons. It is found that an increase in wind speed enhanced particle transport and reduced local particle concentrations, however, it did not affect the relative location of high particle concentration zones, which are related to building height and layout.Implications: This study has demonstrated the height variation and layout of urban architecture affect the local concentration distribution of particulate matter (PM) in the atmosphere and for the first time that wind velocity has particular effects on PM transport in various building groups. The findings may have general implications in optimization the building layout based on particle transport characteristics during the renewal of industrial cities. For city planners, the results and conclusions are useful for improving the local air quality. The study method also can be used to calculate the explosion risk of industrial dust for people who live in industrial cities.

Correlation between Polycyclic Aromatic Hydrocarbon Concentration and Particulate Matter during the Removal Process of a Low-Low Temperature Electrostatic Precipitator

To check the relation between polycyclic aromatic hydrocarbons (PAHs) and particulate matter (PM), this study chose a low-low temperature electrostatic precipitator (LLT-ESP) in an ultra-low-emission coal-fired power plant to study the mechanism at two operation conditions. Sixteen U.S Environmental Protection Agency PAHs in filterable and condensable PM were analyzed. The removal efficiency of the LLT-ESP for filterable PM2.5 was 98.8% and 98.4% at two different stages. The removal efficiency of the LLT-ESP for condensable PM was 77.1% at high temperature and 89.6% at low temperature. The boiler load markedly affected the emission of filterable PM, condensable PM, and particle-associated PAHs. The PAHs’ concentration distribution was related to the PM concentration distribution. The mass concentration of total PAHs and the proportion of 5-ring and 6-ring PAHs in filterable PM2.5 increased significantly when the flue gas passed through the LLT-ESP. The 3- and 4-ring PAHs accounted for the main part of con...

Test and Analysis on Air Particulate Matter Concentration of a Subway Station in Chongqing

The subway traffic travel has been widely recognized by the general public for its convenience and efficiency. Because of the large croud of passengers and the relatively closed environment, indoor air quality directly affect the health of the passengers and the staff of the station. The concentration and distribution of particulate matter are mainly controlled by the ventilation and air conditioning system in the station, and simultaneously influenced by outdoor air quality and passengers flow. In this paper, a subway station in Chongqing, China was taken as an example to test and analyze the concentration of particulate matter in the subway station under air-conditioned situation. The results show: in the summer, the daily average concentration of PM2.5 is about 0.07 mg/m3 in outdoor test-point, the concentration of PM2.5 is among 0.035-0.057 mg/m3 in the ticket hall and on the platform, the PM2.5 concentration of the transition area exceeded the limits of the standard(0.075mg/m3) for some periods of time in the summer; the daily average concentration of PM10 is about 0.083 mg/m3 in outdoor test-point, the concentration of PM10 is among 0.038-0.074 mg/m3, and reaches a peak at 13:00 in the ticket hall and on the platform; the concentration of particulate matter in the transition area is significantly higher than that on the platform and in the ticket hall. In the winter, the transition area is very close to the concentration of particulate matter in the ticket hall, and the concentration of particulate matter on the platform is significantly higher than that in other areas. It is necessary to adopt a reasonable ventilation strategy in the transition area. The concentration distribution of particulate matter has outstanding temporal and spatial distribution, which can be used as reference for space ventilation and air conditioning operation strategy.

Fine airborne particles: when alarming levels are the standard

ObjectivesTo quantify the contribution of each individual month to the annual mortality burden attributable to particulate matter (PM) in 2015 in Milan, Italy, after authorities and media considered December 2015 as an outlying month carrying an exceptional population exposure to PM. Study designWe used routinely available daily time series of air pollution and mortality to perform an assessment of the impact of PM exposure on population health. MethodsBy combining daily death counts with daily PM levels, as well as the yearly average of the number of deaths with the yearly average of PM concentrations, impact estimates were calculated in terms of deaths attributable (AD) to levels of PM10 and PM2.5 exceeding the daily or the annual European Union (EU) exposure limits. ResultsOn a monthly basis, the estimated AD for exceeding the daily EU limits for more than 35 days were 18.4 (PM10) and 33.2 (PM2.5) between January and March, and 20.0 and 31.9 between October and December, respectively. On an annual basis, the EU limit for PM10 was almost met and, therefore, the estimated impact in terms of AD was practically null. ConclusionsImpact results should be interpreted in the light of the skewness of the daily PM concentration distribution. The number of days above the limits is more important than the average annual concentration in determining the number of attributable deaths. The impact of PM on mortality is substantial during the whole winter season irrespective of its annual average concentration. Our estimates further stress the need for a revision of the current European air quality standards.

Satellite imagery for urban air quality evaluation: using Landsat and Systeme Pour l'Observation de la Terre image for particulate matter concentration retrieval

Based on a literature review, it is known that satellite remote sending, coupled with ground-based instrumentation, is a feasible approach to study air pollutants from a spatial statistical perspective and is meanwhile a work around to the absence of emission inventory and the impracticability of interpolation modelling. Since this approach has yet gained familiarity among the researchers of urban air quality, this study of particulate matter (PM) distribution across Hong Kong over 2001, 2006 and 2011 acts as an example to promote a new framework for air quality evaluation using the integrated approach for future applications. Techniques regarding the modelling and simulation for PM concentration retrieval, hotspot visualisation for localised analysis, as well as detection for temporal change in PM concentration distribution are all demonstrated. Results show that the new approach is able to provide highly reliable spatial statistics and is capable of locating hotspots both macroscopically and microscopic...

Spatial Distribution of Nitrogen Oxides and Particulate Matter Concentrations in Taipei

PP-30-013 Background/Aims: Spatial patterns of nitrogen oxides (NOx) and particulate matter (PM), 2 major traffic-related air pollutants in urban environments, have not been characterized in most cities. The purpose of this study is to characterize horizontal and vertical distributions of NOx, PM concentration distribution, and investigate factors affecting their distributions in Taipei. Methods: We used a population density based sampling strategy to select 40 locations as our sampling sites in Taipei, including 20 urban background sites at small alleys with limited traffics and 20 traffic background sites neighboring roads with 4–6 lanes. Among these monitoring sites, 11 sites were used to characterize vertical profiles of NOx in Taipei. We used Ogawa passive samplers to measure NOx and NO2, and Harvard impactors to measure PM10, PM2.5 simultaneously for 2 weeks per season for 2 seasons during 2009–2010. Results: NOx concentrations at traffic sites (46.6 ppb) were significantly higher than the urban background sites (31.1 ppb). And PM2.5 concentration at traffic sites is 2.72 μg/m3 higher than at urban sites. PM10 concentration at traffic sites is 9.05 μg/m3 higher than at urban sites. Seasonal difference was significant for both NOx and PM concentrations. On the aspect of the vertical profile of NOx, the NOx and NO2 concentrations at 4th–6th floors were 0.79 times and 0.75 times lower than the concentrations at first—third floors, the NOx and NO2 concentrations at seventh—eighth floors were 0.64 times and 0.67 times lower than the concentrations which at first—third floors, respectively. There is a significant difference between our sampling results and the monitoring data from fixed-site stations nearby. Conclusion: Our study indicates that traffic pattern is an important factor for influencing the spatial distributions of both NOx and PM in urban environments.

Assessment of impact distances for particulate matter dispersion: A stochastic approach

It is known that pollutants can be dispersed from the emission sources by the wind, or settled on the ground. Particle size, stack height, topography and meteorological conditions strongly affect particulate matter (PM) dispersion. In this work, an impact distance calculation methodology considering different particulate sizes is presented. A Gaussian-type dispersion model for PM that handles size particles larger than 0.1 μm is used. The model considers primary particles and continuous emissions. PM concentration distribution at every affected geographical point defined by a grid is computed. Stochastic uncertainty caused by the natural variability of atmospheric parameters is taken into consideration in the dispersion model by applying a Monte Carlo methodology. The prototype package (STRRAP) that takes into account the stochastic behaviour of atmospheric variables, developed for risk assessment and safe distances calculation [Godoy SM, Santa Cruz ASM, Scenna NJ. STRRAP SYSTEM – A software for hazardous materials risk assessment and safe distances calculation. Reliability Engineering and System Safety 2007;92(7):847–57] is enlarged for the analysis of the PM air dispersion. STRRAP computes distances from the source to every affected receptor in each trial and generates the impact distance distribution for each particulate size. In addition, a representative impact distance value to delimit the affected area can be obtained. Fuel oil stack effluents dispersion in Rosario city is simulated as a case study. Mass concentration distributions and impact distances are computed for the range of interest in environmental air quality evaluations (PM 2.5–PM 10).

Characterization of traffic-related PM concentration distribution and fluctuation patterns in near-highway urban residential street canyons

Analyses of outdoor traffic-related particulate matter (PM) concentration distribution and fluctuation patterns in urban street canyons within a microscale distance of less than 500 m from a highway source are presented as part of the results from the Brooklyn Traffic Real-Time Ambient Pollutant Penetration and Environmental Dispersion (B-TRAPPED) study. Various patterns of spatial and temporal changes in the street canyon PM concentrations were investigated using time-series data of real-time PM concentrations measured during multiple monitoring periods. Concurrent time-series data of local street canyon wind conditions and wind data from the John F. Kennedy (JFK) International Airport National Weather Service (NWS) were used to characterize the effects of various wind conditions on the behavior of street canyon PM concentrations.Our results suggest that wind direction may strongly influence time-averaged mean PM concentration distribution patterns in near-highway urban street canyons. The rooftop-level wind speeds were found to be strongly correlated with the PM concentration fluctuation intensities in the middle sections of the street blocks. The ambient turbulence generated by shifting local wind directions (angles) showed a good correlation with the PM concentration fluctuation intensities along the entire distance of the first and second street blocks only when the wind angle standard deviations were larger than 30 degrees. Within-canyon turbulent shearing, caused by fluctuating local street canyon wind speeds, showed no correlation with PM concentration fluctuation intensities. The time-averaged mean PM concentration distribution along the longitudinal distances of the street blocks when wind direction was mostly constantly parallel to the street was found to be similar to the distribution pattern for the entire monitoring period when wind direction fluctuated wildly. Finally, we showed that two different PM concentration metrics-time-averaged mean concentration and number of concentration peaks above a certain threshold level-can possibly lead to different assessments of spatial concentration distribution patterns.