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Epidemiological studies use direct and indirect methods to estimate exposure to air pollutants. In this paper, we compared the exposure to NO2 given by dispersion modeling and a fixed monitoring station and personal monitors during the daily routine of twenty-one children. Indoor-outdoor (I/O) ratios were used to represent differences between indoor and outdoor environments. A sensitivity test was performed to assess the impact of this parameter. Questionnaires were used to obtain subjects’ routines during campaigns. The results obtained using dispersion models agree with the individual differences in exposure assessed by personal monitors — if compared to a method relying solely on the monitoring station. Nevertheless, an overestimation occurred for children living near roadways. The exposure results revealed higher sensitivity to I/O ratios used than to children’s pathways between their usual destinations. The time spent outdoors (or indoors) and the concentration variation at the urban microscale (

Air pollution generated from airport activities has become public concern and the subject of more rigorous government regulations. The Airport Operators are stipulated to control the pollution and for the accountability of air quality that might affect public health. The main objective of this study is to establish a model for the distribution of air pollutants and to predict their concentrations generated by the runway and apron operations at Sam Ratulangi International Airport (Manado) until 2024, in accordance with the airport expansion program. The data was collected in the airport surrounding area in 2018, while the climate data over a span of 10 years, from 2009 to 2018, was obtained from Sam Ratulangi Meteorological Station. The modeling on dispersion of air pollutant gases was developed by the Gaussian Plume Equation. The simulation was performed using AERMOD software, and the results visualized by GIS software. AERMOD software was recommended by the US-EPA to predict the impact of air pollutants. The results predicted that the maximum concentrations of NOx; HC; and CO generated by runway activities modeling in 2024 were 250 μg.m-3; 6.4 μg.m-3; and 87 μg.m-3 respectively. The results also predicted that the maximum concentrations of NOx; CO; and PM10 due to apron operational activities in 2024 were 260 μg.m-3; 892 μg.m-3; and 2.5 μg.m-3 respectively. The model predicted that in 2024 the air pollution at Sam Ratulangi International Airport will remain under the limit as defined in Indonesian Government Regulation No. 22 of 2021. To mitigate the future increase in air emissions due to the increase in airport capacity, the recommendation were proposed in the several areas, which were including operation management, technology, policies and airport regulations, as well as the provision of green area.

El presente estudio realizo la dispersión de PM2.5 emitidos por pollerías en Huancayo Metropolitano. Se utilizaron datos topográficos de 30 metros de resolución, datos meteorológicos superficiales y verticales de enero a diciembre del 2017 y características de la fuente como concentraciones de emisión de PM2.5 de pollerías. Para la determinar la eficiencia del modelo AERMOD en la dispersión de PM2.5 se instalaron estaciones de monitoreo de calidad de aire en tres áreas urbanas de Huancayo Metropolitano (UNCP, HYO y CHI), la campaña de muestreo se llevó a cabo de marzo a noviembre del 2017. Los resultados, identificaron la asociación de la dirección y velocidad del viento con la dispersión de PM2.5 y que las concentraciones obtenidas por el modelo AERMOD subestimaron con las obtenidas de las estaciones de monitoreo de calidad de aire, esto debido a que se modelo solo pollerías y no de otras aportaciones de fuentes de emisión. AERMOD provee buenas opciones para modelar dispersión de contaminantes del aire en regiones de montaña para gestión de la calidad del aire.

Livestock is one of the main activities of the agricultural sector in Tan Thanh district, Ba Ria – Vung Tau province. Beside of pollution sources such as waste water, solid waste, livestock activity in Tan Thanh district, Ba Ria - Vung Tau province in recent years has caused air pollution in the livestock area and surrounding area. This research was carried out to evaluate the process of air pollution dispersion from livestock activities based on applying the TAPM meteorological model and AERMOD air quality model. The results showed that the maximum concentrations of air pollutants from livestock area such as NH3, H2S and CH3SH exceeded the National Technical Regulation on Ambient Air Quality (average hour) in the centre of Tan Thanh district, such as Toc Tien commune, part of Tan Phuoc and Phuoc Hoa communes, is 505 μg/m3; 57.4 μg/m3 and 111 μg/m3, respectively. Phu My district and other suburban communes (Hac Dich, Song Xoai, Chau Pha, Tan Hoa, Tan Hai, My Xuan, etc.) have distribution of lower concentrations of air pollutants. Base on the present results of modeling, the authors have proposed livestock development scenarios to control air pollution from this activity, contributing to environmental protection for Tan Thanh district.

The motivation for this work stems from the increased number of high-rise buildings/skyscrapers all over the world, and in London, UK, and hence the necessity to see their effect on the local environment. We concentrate on the mean velocities, Reynolds stresses, turbulent kinetic energies (TKEs) and tracer concentrations. We look at their variations with height at two main locations within the building area, and downstream the buildings. The pollution source is placed at the top of the central building, representing an emission from a Combined Heat and Power (CHP) plant. We see how a tall building may have a positive effect at the lower levels, but a negative one at the higher levels in terms of pollution levels. Mean velocities at the higher levels (over 60 m in real life) are reduced at both locations (within the building area and downstream it), whilst Reynolds stresses and TKEs increase. However, despite the observed enhanced turbulence at the higher levels, mean concentrations increase, indicating that the mean flow has a greater influence on the dispersion. At the lower levels (Z &lt, 60 m), the presence of a tall building enhanced dispersion (hence lower concentrations) for many of the configurations.

Highways passing through cities cause additional pollution inside the city. However, most of the current studies are using ground-based monitoring technologies, which make it difficult to capture the dispersion patterns of pollutants near elevated highways or transportation interchanges. The purpose of this study is to discover short-term three-dimensional variations in traffic-related pollutants based on unmanned aerial vehicles. The monitoring locations are at suburban elevated highway and transportation interchanges. The monitoring parameters include the particle number concentration (PN), particle mass concentration (PM), and black carbon (BC). The vertical profiles showed that most air pollutants increased significantly with the height of the elevated highways. Compared with the ground level, PNs increased by 54%&ndash, 248% and BC increased by 201%. The decline rate of particle concentrations decreased with the increase of height and remained stable after 120m. Furthermore, the R2 heatmap for regressions between each altitude showed that the linear relationship between 0&ndash, 120m was higher than that of other altitudes. In horizontal profiles, PNs spread to 100m and then began to decline, BC began to decay rapidly after 50m, but PMs varied less. After crossing another highway, PNs increased by 69&ndash, 289%, PMs by 7&ndash, 28%, and BC by 101%. Furthermore, the formation of new particles was observed at both locations as PN3 increased with distance within 100m from the highway. This paper fills in the void of three-dimensional in situ monitoring near elevated highways, and can help develop and refine a three-dimensional traffic-related air pollution dispersion model and assess the impacts of transportation facilities on the urban environment.

Observed meteorological conditions, usually measured at airports or weather monitoring stations, have long provided the only source of meteorology for many Gaussian air pollution dispersion models. This introduces uncertainty and limitations in numerical model estimates, especially for locations of interest far removed from these monitoring stations. Hence, it is advantageous to employ predicted meteorology from a prognostic meteorological model as a substitute. The objective of this study was to compare estimates from the R-LINE near road dispersion model at three inland sites and one coastal site in Connecticut using observation-derived (weather station) and model-derived (Weather Research and Forecasting Model) meteorology. Both the graphical and statistical comparisons indicated less pronounced discrepancies in model estimations in the time periods generally characterized by unstable atmospheric conditions than those characterized by stable atmospheric conditions. There were also more pronounced differences at larger distances from roadways. Comparison of the estimated surface characteristic variables using both the observation-derived and model-derived meteorology displayed similar diurnal trends.

We investigate the effect of conventional pitched roofs on ventilation and pollution in street canyons using Computational Fluid Dynamics and a parametric approach. We studied parallel street canyons with several street morphologies, created by assigning a set of streets with pitched roofs, and varying their pitch and arrangement for three different height-to-width aspect ratios. The distribution of flow properties and pollution concentrations within the street canyons are examined and the effect of different parameter combinations is assessed. We find the relationship between these properties and the street morphology to be complex and case specific. For most morphologies, the pitched roofs lead to higher average pollution concentrations, and in some cases to pollution hotspots near emission sources especially on the leeward side. The pitched roofs are rarely beneficial to ventilation of the street canyons, but a few roof arrangements lead to reduced concentrations on the windward side. Roof slope is shown to significantly relate to both average pollution concentrations and their distribution inside the street; in some street geometries more than others. The results have implications for pedestrian and residential pollution exposure, and for conservation of building facades on historical buildings.

In high-density cities, optimization of their compact urban forms is important for the enhancement of pollution dispersion, improvement of the air quality, and healthy urban living. This study aims to identify critical building morphological design factors and provide a scientific basis for urban planning optimization. Through a long-term mobile monitoring campaign, a four-month (spanning across summer and winter seasons) spatiotemporal street-level PM2.5 dataset was acquired. On top of that, the small-scale spatial variability of PM2.5 in the high-density downtown area of Hong Kong was mapped. Seventeen building morphological factors were also calculated for the monitoring area using geographical information system (GIS). Multivariate statistical analysis was then conducted to correlate the PM2.5 data and morphological data. The results indicate that the building morphology of the high-density environment of Hong Kong explains up to 37% of the spatial variability in the mobile monitored PM2.5. The building morphological factors with the highest correlation to PM2.5 concentration are building volume density, building coverage ratio, podium layer frontal area index and building height variability. The quantitative correlation between PM2.5 and morphological factors can be adopted to develop scientifically robust and straightforward optimization strategies for planners. This will allow considerations of pollution dispersion to be incorporated in planning practices at an early stage.

The paper presents the possibilities of selected functions from openair package for R programming environment in urban air pollution assessment. Examples of data analysis were based on the measurements from continuous air quality monitoring stations in Krakow (Poland). In order to present additional functionality of this software, modeling results of back trajectories and air pollution dispersion were used. Functions and visualization methods included in openair package make scrutiny of large data sets easier and less time consuming. They allow for analysis of measurement data with the determination of general relationships between parameters, additional complex spatial analyses for back trajectories, and validation of air pollution dispersion models. Openair package is, therefore, a valuable and functional tool that can be successfully used as a support in the air quality management system.

In this paper, the modeling results of parameters and factors which determine spread of contamination in atmospheric air, are presented. These factors are: aerodynamic coefficient of area roughness, emitters location, exhaust temperature and velocity at the end of emitter. Computer program Ek100w calculates concentration of pollutants in the air on different distance from the emitter. We use calculation results to prepare charts with contour lines of air pollutions concentration. In this article contamination spread from emitters with different work parameters is analyzed. It follows that these parameters and factors have an important effect on contamination spreading in the atmospheric air. We can use such programs for emission design in practice and reduce impurities and immission on area where people are especially endanger for industrial emission.

The simulation results of air pollution dispersion for 4 scenarios are calculated in the condition that the air treatment system is operating normally and efficiently, the results of pollutants are almost lower than Vietnamese standards (QCVN 05:2013), but only SO2 is slightly higher than standard (1.3 times higher than standard, and happening only 2 hours / year). In the case of air pollution treatment systems of four power plants were not in operating, the affect to ambient air and to human health were very seriously as the highest average hourly concentrations of NO2 and SO2 exceeded the standard of 8.4 and 11.8 times, respectively.

In this study, we present an evaluation of the (PM10) and carbon monoxide (CO) particulate matter exposure level originated from the four power plants in the area using Air Pollution Dispersion Model. Combined use of AREMOD (The American Meteorological Society/Environmental Protection Agency Regulatory Model), ArcGIS and health risk assessment were applied to estimate the level of pollution in thirteen municipal receptors in the city. The results indicated the long-range transport of the pollutants from the power plants expected to impose significant health impacts on residential receptors. Almost 80000 inhabitants of the city were exposed to PM10 concentration, ranging between 50-75 µg/m3 and 100000 were exposed to CO concentration, ranging between 40-45 µg/m3. Approximately, 1200 hectares of the city were exposed to PM10 concentration, ranging from 40 to 50 µg/m3 and 370 hectares of the city area were exposed to CO concentration between 50-75 µg/m3. Comparison between simulated and observed concentrations of pollutants shows a little overestimation by model.

Background Indoor air pollution, including fine particulate matter (PM2.5) and carbon monoxide (CO), is a major risk factor for pneumonia and other respiratory diseases. Biomass-burning cookstoves are major contributors to PM2.5 and CO concentrations. However, high concentrations of PM2.5 (> 1000 μg/m3) have been observed in homes in Dhaka, Bangladesh that do not burn biomass. We described dispersion of PM2.5 and CO from biomass burning into nearby homes in a low-income urban area of Dhaka, Bangladesh. Methods We recruited 10 clusters of homes, each with one biomass-burning (index) home, and 3–4 neighboring homes that used cleaner fuels with no other major sources of PM2.5 or CO. We administered a questionnaire and recorded physical features of all homes. Over 24 h, we recorded PM2.5 and CO concentrations inside each home, near each stove, and outside one neighbor home per cluster. During 8 of these 24 h, we conducted observations for pollutant-generating activities such as cooking. For each monitor, we calculated geometric mean PM2.5 concentrations at 5-6 am (baseline), during biomass burning times, during non-cooking times, and over 24 h. We used linear regressions to describe associations between monitor location and PM2.5 and CO concentrations. Results We recruited a total of 44 homes across the 10 clusters. Geometric mean PM2.5 and CO concentrations for all monitors were lowest at baseline and highest during biomass burning. During biomass burning, linear regression showed a decreasing trend of geometric mean PM2.5 and CO concentrations from the biomass stove (326.3 μg/m3, 12.3 ppm), to index home (322.7 μg/m3, 11.2 ppm), neighbor homes sharing a wall with the index home (278.4 μg/m3, 3.6 ppm), outdoors (154.2 μg/m3, 0.7 ppm), then neighbor homes that do not share a wall with the index home (83.1 μg/m3,0.2 ppm) (p = 0.03 for PM2.5, p = 0.006 for CO). Conclusion Biomass burning in one home can be a source of indoor air pollution for several homes. The impact of biomass burning on PM2.5 or CO is greatest in homes that share a wall with the biomass-burning home. Eliminating biomass burning in one home may improve air quality for several households in a community. Electronic supplementary material The online version of this article (10.1186/s12889-019-6751-z) contains supplementary material, which is available to authorized users.

Pollutant dispersion in urban areas has been a key concern in densely populated cities because pollutants can negatively impact human health and the environment. Topography and urban obstructions, such as buildings, greatly affect the atmospheric fluid flow, leading to the dispersion process. Street canyons are usually formed in highly populous cities due to the close proximity of buildings to streets to serve commercial purposes. It can also be termed as pollutant traps that exact adverse effects on urban life. Street canyons cause changes in pollutant dispersion, particularly in the case of vehicle exhaust air pollutants, which cannot be transported by the wind owing to the presence of buildings acting as impediments regardless of the wind flow. Hence, it is imperative to completely understand the behavior of pollutants within the confined urban surroundings for further improving the urban air quality. This study explores the effects of street canyons on the busiest street of Dhaka city in Bangladesh. The Reynolds-averaged Navier–Stokes based dispersion model is used to investigate the air quality phenomenon. Building height, width, building distance, and road width have been taken into consideration in the simulation. It is expected that the findings of the current study would be helpful for urban planners and designers of Dhaka city.

In large cities, pollution composed of many different chemical components and small particles is an important public health problem that affects especially children and people presenting breathing difficulties.

Air pollution dispersion modelling via spatial analyses (Land Use Regression—LUR) is an alternative approach to the standard air pollution dispersion modelling techniques in air quality assessment. Its advantages are mainly a much simpler mathematical apparatus, quicker and simpler calculations and a possibility to incorporate more factors affecting pollutant’s concentration than standard dispersion models. The goal of the study was to model the PM10 particles dispersion via spatial analyses in the Czech⁻Polish border area of the Upper Silesian industrial agglomeration and compare the results with the results of the standard Gaussian dispersion model SYMOS’97. The results show that standard Gaussian model with the same data as the LUR model gives better results (determination coefficient 71% for Gaussian model to 48% for LUR model). When factors of the land cover were included in the LUR model, the LUR model results improved significantly (65% determination coefficient) to a level comparable with the Gaussian model. A hybrid approach of combining the Gaussian model with the LUR gives superior quality of results (86% determination coefficient).

The assessment of exposure to traffic-related air pollution has seen advances along various dimensions. Air pollution dispersion models with fine spatial resolution and ability to reflect near-road...

International audience; Air pollution has major negative effects on both human health and environment. Thus, air quality monitoring is a main issue in our days. In this paper, we focus on the use of mobile WSN to generate high spatio-temporal resolution air quality maps. We address the sensors' online redeployment problem and we propose three redeployment models allowing to assess, with high precision, the air pollution concentrations. Unlike most of existing movement assisted deployment strategies based on network generic characteristics such as coverage and connectivity, our approaches take into account air pollution properties and dispersion models to offer an efficient air quality estimation. First, we introduce our proposition of an optimal integer linear program based on air pollution dispersion characteristics to minimize estimation errors. Then, we propose a local iterative integer linear programming model and a heuristic technique that offer a lower execution time with acceptable estimation quality. We evaluate our models in terms of execution time and estimation quality using a real data set of Lyon City in France. Finally, we compare our models' performances to existing generic redeployment strategies. Results show that our algorithms outperform the existing generic solutions while reducing the maximum estimation error up to 3 times.

Reduced birth weight (RBW) and reduced head circumference (RHC) are adverse birth outcomes (ABOs), often linked to environmental exposures. However, spatial identification of specific health hazards, associated with these ABOs, is not always straightforward due to presence of multiple health hazards and sources of air pollution in urban areas. In this study, we test a novel empirical approach to the spatial identification of environmental health hazards potentially associated with the observed RHC and RBW patterns. The proposed approach is implemented as a systematic search, according to which alternative candidate locations are ranked based on the strength of association with the observed birth outcome patterns. For empirical validation, we apply this approach to the Haifa Bay Area (HBA) in Israel, which is characterized by multiple health hazards and numerous sources of air pollution. We identified a spot in the local industrial zone as the main risk source associated with the observed RHC and RBW patterns. Multivariate regressions, controlling for personal, neighborhood, and geographic factors, revealed that the relative risks of RHC and RBW tend to decline, other things being equal, as a function of distance from the identified industrial spot. We recommend the proposed identification approach as a preliminary risk assessment tool for environmental health studies, in which detailed information on specific sources of air pollution and air pollution dispersion patterns is unavailable due to limited reporting or insufficient monitoring.

Background/aim Prior studies have shown possible links between medium-term air pollution (AP) exposure and glucose metabolism markers. We investigated whether associations between accumulation mode particle number concentration (PN AM ) and glucose metabolism measures are robust to adjustment for other potential co-pollutants. Methods This analysis included observations from non-diabetic participants (n obs =7,108) of the population-based Heinz Nixdorf Recall study at baseline (2000–2003) and follow-up examination (2006–2008). Particulate matter (PM), accumulation mode particle number (PN AM ), and NO 2 exposures were estimated for each examination at every participant’s residence using the spatiotemporal EURopean Air pollution Dispersion (EURAD) chemistry transport model. Associations between a range of short- and medium-term PN AM exposures (1- to 182 day average prior to blood draw) and glucose metabolism measures (blood glucose, HbA1c) were assessed for robustness to co-adjustment for PM 2.5 , PM 10 , and NO 2 using a mixed effects linear regression model Results PN AM exposure showed low to medium correlation with PM 2.5 , PM 10 , and NO 2 (e.g., 0.33–0.53 for 28 day exposures). In the model with only PN AM and blood glucose, associations were seen for 14- to 91 day mean exposures windows with strongest associations observed for the 60 day window (1.05 mg/dL per 1580 n/mL (95% CI: 0.46 to 1.63)). Co-adjustment for PM 2.5 attenuated associations at all time points (e.g., 0.67 mg/dL per 1580 n/mL (95% CI: −0.22 to 1.55) for the 60 day window) whereas associations for PN AM remained largely unchanged upon adjustment for PM 10 and NO 2 . For HbA1c, strongest associations were apparent for the 105 day exposure window (0.096 p.p. per 1257 n/mL (95% CI: 0.075 to 0.116)). HbA1c model estimates were largely unchanged upon adjustment for co-pollutants. Conclusion The associations between exposure to PN AM and glucose metabolism measures were robust to adjustment for co-pollutants, possibly suggesting that exposure to small particles plays an independent role in influencing glucose regulation.

Cumulative impact evaluation is one of the most actual problems in air quality monitoring. At the same time, it is also the most problematic factor to evaluate due to lack of appropriate methodology. The aim of this study was to assess the opportunity to use a new method – Cumulative Pollution Index (CPI) in cumulative impact calculation from two different sets of data – bioindication survey with Index of Atmospheric Purity method and air pollution dispersion modelling. Results show that the usage of modelling data, instead of measurements, in cumulative impact evaluation can be quite difficult due to the fact that dispersion models not always give sufficiently accurate data. Despite the issues with modelling specifics, the use of dispersion modelling in CPI calculation shows that the use of this approach not only gives plausible data – obtained values correlate with pollution level and forming strong clustering in spatial distribution, but also reveals new facts about cumulative impact – demonstrates the city microclimate importance in forming of cumulative effect due to geometry of street canyons.