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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.

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 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.

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.

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.

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.

Air quality of Incheon area has been degraded recently, especially from the ocean going vessels (OGVs). PAQman©(Portal Air Quality management) system has been developed to estimate the air pollution from OGVs in a portal area by using field survey data, AIS (Automatic Identification System) data, activity data, etc. Atmospheric to Computational Fluid Dynamics (A2Cflow/A2Ct&d) was applied for simulating the coastal air flow and air pollution dispersion in the study domain. PAQman© was successful to estimate the emission from OGVs to 50 km far from the Incheon port where air emission amount is significantly higher at anchorage points (8 km, 14 km, and 24 km). The total amount of NO x , SO 2 , CO and PM10 emissions from OGVs are 42,933,187 kg/year, 33,547,238 kg/year, 3,364,604 kg/year and 2,597,351 kg/year, respectively, in 2005. Besides, sea and land breezes are frequently generated in Incheon coastal area where the penetration length of the sea and the land breeze are approximately 25–30 km and 15–20 km, respectively. The suction length of the sea and the land breeze were also estimated 15–20 km in a similar range. It is understood that the air pollution generated by OGVs up to 25 km from the Incheon harbor area would be transported to the inland area and plays a significant role to increase the air pollution level in Incheon coastal area.

This study evaluated road shape and roadside barrier impact on near-road air pollution dispersion using FLUENT computational fluid dynamics (CFD) model. Simulated road shapes are three types, namely atgrade, depressed, and filled road. The realizable k-e model in FLUENT CFD code was used to simulate the flow and dispersion around road. The selected concentration profile results were compared with the wind tunnel experiments. The overall concentration profile results show good agreement with the wind tunnel results. The results showed that noise barriers, which positioned around the at-grade road, decrease the horizontal impact distance (In this study, the impact distance was defined as the distance from road surface origin coordinate to the position whose mass fraction is 0.1.) lower 0.33~0.65 times and change the vertical air pollution impact distance larger 2.0~ 2.27 times than those of no barrier case. In case of filled road, noise barriers decrease the horizontal impact distance lower 0.24~0.65 times and change the vertical air pollution impact distance larger 3.33~3.55 times than those of no barrier case. The depressed road increase 1.53~1.68 times the vertical air pollution impact distance. It contributes the decrease of horizontal air pollution impact distance 0.32~0.60 times compare with no barrier case.

Concentrated animal feeding operations (CAFOs) produce large amounts of animal waste, which potentially pollutes air, soil and water and affects human health if not appropriately managed. This study uses meteorological and CAFO data and applies an air pollution dispersion model (CALPUFF) to estimate ammonia concentrations at locations downwind of hog CAFOs and to evaluate the disproportionate exposure of children, elderly, whites and minorities to the pollutant. Ammonia is one of the gases emitted by swine CAFOs and could affect human health. Local indicator of spatial autocorrelation (LISA) analysis uses census block demographic data to identify hot spots where both ammonia concentrations and the number of exposed vulnerable population are high. We limit our analysis to one watershed in North Carolina and compare environmental justice issues between 2000 and 2010. Our results show that the average ammonia concentrations in hot spots for 2000 and 2010 were 2.5–3-times higher than the average concentration in the entire watershed. The number of people living in the areas where ammonia concentrations exceeded the minimal risk level was 3647 people in 2000 and 3360 people in 2010. We recommend using air pollution dispersion models in future environmental justice studies to assess the impacts of the CAFOs and to address concerns regarding the health and quality of life of vulnerable populations.

Introduction The development of urbanization has had many negative outcomes in different societies. Population growth and the increase in the transportation are the consequences of urban growth which has resulted into problems. Vehicles are responsible for 90% of air pollution in Iran and it is essential to use authentic models of traffic emissions in accordance with the current conditions to predict this and future emissions. Iran has a lot of different air pollution dispersion parameters compared with the developed countries. Method In this paper air pollution emission parameters in signalized intersections have been modelled and results have been compared to measured concentrations of air pollution in intersections. For this purpose the use of IVE (International Vehicle Emission) model that is common air pollution modelling and besides, Sensitivity Analysis has been performed to show modeling accuracy in comparison with current emissions. Result By modeling and measurement results, it’s easily understood in warm seasons emission concentration is more than cold seasons. Minimum and maximum rate of Carbon-monoxide (CO) and Nitrogen-oxides (NOX) has been evaluated. Conclusion IVE model has shown a bit difference amount of pollutions by comparison with field measurement emission. It could be said it is appropriate for model vehicle emission in Iran.

For the simultaneous analysis of short- and long-term effects of air pollution in the Heinz Nixdorf Recall Cohort a sophisticated exposure modelling was performed. The dispersion and chemistry transport model EURAD (European Air Pollution Dispersion) was used for the estimation of hourly concentrations of a number of pollutants for a horizontal grid with a cell size of 1 km² covering the whole study area (three large adjacent cities in a highly urbanized region in Western Germany) for the years 2000–2003 and 2006–2008. For each 1 km² cell we estimated the mean concentration by calculating daily means from the hourly concentrations modelled by the EURAD process. The modelled concentrations showed an overall tendency to decrease from 2001 to 2008 whereas the trend in the single grid cells and study period was inhomogeneous. Participant-related exposure slightly increased from 2001 to 2003 followed by a decrease from 2006 to 2008. The exposure modelling enables a very flexible exposure assessment compared to conventional modelling approaches which either use central monitoring or temporally static spatial contrasts. The modelling allows the calculation of an average exposure concentration for any place and time within the study region and study period with a high spatial and temporal resolution. This is important for the assessment of short-, medium and long-term effects of air pollution on human health in epidemiological studies.

In high-density megacities, air pollution has a higher impact on public health than cities of lower population density. Apart from higher pollution emissions due to human activities in densely populated street canyons, stagnated air flow due to closely packed tall buildings means lower dispersion potential. The coupled result leads to frequent reports of high air pollution indexes at street-side stations in Hong Kong. High-density urban morphologies need to be carefully designed to lessen the ill effects of high density urban living. This study addresses the knowledge-gap between planning and design principles and air pollution dispersion potentials in high density cities. The air ventilation assessment for projects in high-density Hong Kong is advanced to include air pollutant dispersion issues. The methods in this study are CFD simulation and parametric study. The SST κ–ω model is adopted after balancing the accuracy and computational cost in the comparative study. Urban-scale parametric studies are conducted to clarify the effects of urban permeability and building geometries on air pollution dispersion, for both the outdoor pedestrian environment and the indoor environment in the roadside buildings. Given the finite land resources in high-density cities and the numerous planning and design restrictions for development projects, the effectiveness of mitigation strategies is evaluated to optimize the benefits. A real urban case study is finally conducted to demonstrate that the suggested design principles from the parametric study are feasible in the practical high density urban design., Highlights • The study clarifies the air pollutant dispersion mechanism in high-density cities. • The study addresses the urban traffic pollutant problems and provides the solutions. • The study relies on a parametric study that tests the performance of design options. • The study validates LES and RANS models using wind tunnel data. • The study provides understandings to map the pollutant dispersion in the urban areas.

ADMS and AERMOD are the two most widely used dispersion models for regulatory purposes. It is, therefore, important to understand the differences in the predictions of the models and the causes of these differences. The treatment by the models of flat terrain has been discussed previously; in this paper the focus is on their treatment of complex terrain. The paper includes a discussion of the impacts of complex terrain on airflow and dispersion and how these are treated in ADMS and AERMOD, followed by calculations for two distinct cases: (i) sources above a deep valley within a relatively flat plateau area (Clifty Creek power station, USA); (ii) sources in a valley in hilly terrain where the terrain rises well above the stack tops (Ribblesdale cement works, England). In both cases the model predictions are markedly different. At Clifty Creek, ADMS suggests that the terrain markedly increases maximum surface concentrations, whereas the AERMOD complex terrain module has little impact. At Ribblesdale, AERMOD predicts very large increases (a factor of 18) in the maximum hourly average surface concentrations due to plume impaction onto the neighboring hill; although plume impaction is predicted by ADMS, the increases in concentration are much less marked as the airflow model in ADMS predicts some lateral deviation of the streamlines around the hill.

The integration of high dimensional geo-visualization, geo-data management, geo-process modeling and computation, geospatial analysis, and geo-collaboration is a trend in GIScience. The technical platform that matches the trend forms a new framework unlike that of GIS and is conceptualized in this paper as a collaborative virtual geographic environment (CVGE). This paper focuses on two key issues. One is scientific research on CVGE including the concept definition and the conceptual and system framework development. The other is a prototype system development according to CVGE frameworks for air pollution simulation in the Pearl River Delta. The prototype system integrates air pollution source data, air pollution dispersion models, air pollution distribution/dispersion visualization in geographically referenced environments, geospatial analysis, and geo-collaboration. Using the prototype system, participants from geographically distributed locations can join in the shared virtual geographic environment to conduct collaborative simulation of air pollution dispersion. The collaborations supporting this simulation happen on air pollution source editing, air pollution dispersion modeling, geo-visualization of the output of the modeling, and geo-analysis.

A semi-empirical mathematical model, Urban Street Model (USM), is proposed to efficiently estimate the dispersion of vehicular air pollution in cities. This model describes urban building arrangements by combining building density, building heights and the permeability of building arrangements relative to wind flow. To estimate the level of air pollution in the city of Krasnoyarsk (in Eastern Siberia), the spatial distribution of pollutant concentrations off roadways is calculated using Markov's processes in USM. The USM-predicted numerical results were compared with field measurements and with results obtained from other frequently used models, CALINE-4 and OSPM. USM consistently yielded the best results. OSPM usually overestimated pollutant concentration values. CALINE-4 consistently underestimated these values. For OSPM, the maximum differences were 160% and for CALINE-4 about 400%. Permeability and building density are necessary parameters for accurately modeling urban air pollution and influencing regulatory requirements for building planning.

Increased concern over regional and international aspects of air pollution, acid deposition and accidental releases has created a need for air pollution dispersion model development and evaluation. Under the framework of the US-China Peaceful Uses of Nuclear Technologies (PUNT) Agreement, a program to carry out an Asian tracer experiment was proposed. In this paper, the long range tracer experiments conducted internationally and evaluation of long range dispersion models were reviewed and a preliminary planning on the former several aspects was analyzed and discussed.

International audience; This paper describes an agent based approach for simulating the control of an air pollution crisis. A Gaussian Plum air pollution dispersion model (GPD) is combined with an Artificial Neural Network (ANN) to predict the concentration levels of three different air pollutants. The two models (GPM and ANN) are integrated with a MAS (multi-agent system). The MAS models pollutant sources controllers and air pollution monitoring agencies as software agents. The population of agents cooperates with each other in order to reduce their emissions and control the air pollution. Leaks or natural sources of pollution are modelled as uncontrolled sources. A cooperation strategy is simulated and its impact on air pollution evolution is assessed and compared. The simulation scenario is built using data about Annaba (a city in NorthEast Algeria). The simulation helps to compare and assess the efficiency of policies to control air pollution during crises, and takes in to account uncontrolled sources.

Based on the inherent property of symmetry of air pollution models, a Symmetrical Air Pollution Model Index (SAPMI) has been developed to calibrate the accuracy of predictions made by such models, where the initial quantity of release at the source is not known. For exact prediction the value of SAPMI should be equal to 1. If the predicted values are overestimating then SAPMI is 1. Specific design for the layout of receptors has been suggested as a requirement for the calibration experiments. SAPMI is applicable for all variations of symmetrical air pollution dispersion models.

This work adopts a continuum approximation method based on long-term planning to describe the continuous characteristics of air pollution dispersion, street and rail transit network flow as well as household distribution. The model of air pollution distribution in a metropolitan area with surface streets and rail transit networks is constructed by applying dispersion equations of air pollutants in the field of Physics. Variations in air pollution due to decreased car flows, which result from commuters' shifting to the newly completed rail transit system, are also analyzed. Benefits from the reduction of air pollution are estimated by accumulating all of the exposed households at each residential site. Finally, a case study demonstrates the model application and estimates the improvement of residential environment in the Taipei metropolitan area due to the rail transit system.

The importance of atmospheric conditions for the assessment of an air pollution situation has been demonstrated by their influence on the various compartments of an air pollution system, comprising all stages from emission to effects. Especially, air pollutants dispersion phenomenon are very sensitive according to wind data. But the discussions of how to apply representative meteorological data in air pollution dispersion model are not frequent in Korean environmental assessment processes. In this study, we investigated the difference of air pollutants dispersion phenomenon using U.S EPA ISCLT3 model according to applying the different meteorological data observed at two points for Seongseo industrial complex of Daegu. Two points are the spot site of Seongseo industrial complex and Daegu meteorological observatory. The winds speed of the spot site were smaller than those of Daegu meteorological observatory. In the winter season, the differences came to about for the period. Wind directions were also fairly different at two points. The air pollutants dispersion phenomenon estimated from our numerical experiments were also fairly different owing to the meteorological conditions at two points.

The air pollution concentration in Kathmandu valley in the winter season was found to be higher than in the summer season due to the formation of the inversion layer. This mechanism was simulated in the water-tank experiment by measuring the temperature and flow field using liquid crystal thermometry and particle image velocimetry. Thermal stratification was made at the beginning of the experiment and the surface temperature of the valley model was changed with 12 minutes period matching the diurnal field temperature pattern of the Kathmandu valley. The updraft wind and Bernard convection occurred during daytime and downdraft wind and inversion layer were realized during nighttime. The temperature, flow field and mass dispersion characteristics obtained in the water-tank experiment explained clearly the mechanism of air pollution in Kathmandu valley.

French researchers from the Building Scientific and Technical Center have produced a traffic-exposure index. To achieve this, they used an air pollution dispersion model that enabled them to calculate automobile pollutant concentrations in front of subjects' residences and places of work. Researchers used this model, which was tested at 27 Paris canyon street sites, and compared nitrogen oxides measurements obtained with passive samplers during a 6-wk period and calculations derived from the model. There was a highly significant correlation (r = .83) between the 2 series of values; their mean concentrations were not significantly different. The results suggested that the aforementioned model could be a useful epidemiological tool for the classification of city dwellers by present—or even cumulative—exposure to automobile air pollution.

The abilities of two line-source dispersion models to predict road-edge concentrations of carbon dioxide (CO2) along several roads are tested. The comparison of the predicted CO2 concentrations from the CALINE4 and HIWAY-2 models, using a new power-based emissions module, are compared with 8 days of experimental data collected at three sites in Sydney, Australia, through a recent field study carried out by the Commonwealth Scientific Industrial Research Organization on behalf of the Road and Traffic Authority in Sydney to quantify air pollution near roads and highways in Sydney. The measurements were made at locations up to 60 m downwind from the roadside and to heights 10 m above the ground. The comparison of the measured CO2 concentrations and those predicted on the basis of appropriate traffic and meteorological data shows that both concentrations and those predicted on the basis of appropriate traffic and meteorological data shows that both CALINE4 and HIWAY-2, when used in conjunction with the new vehicle emissions module, satisfactorily estimate road-edge concentrations of CO2. As such, both of the two dispersion models, CALINE4 and HIWAY-2, in conjunction with the new power-based emissions module, are potentially useful tools for highway planners.

Background Modelling of air pollution dispersion is one of the ambient air quality assessment methods, allowing predicting air pollution and accurately assessing the spatial distribution of ambient...

For air quality modeling, the Gaussian plume model has been extensively used to analytically solve the steady and unsteady transport equation including the effects of particle deposition and settling. Along with the standard analytical solution of the Gaussian plume model for modeling air pollution dispersion in the atmosphere this work explores the adaptation of a Lagrangian Branched Atmospheric Trajectory approach for computing ground level concentrations of PM10 (suspended Particulate Matter of aerodynamic diameter of less than10 micro meter), PM2.5 emitted from different air pollution sources in and around the city of Ahmedabad, India. With a transport time step of 6 hrs, the model includes necessary meteorological data which couple with multi-pollutant emission grid. The results obtained from the model are found to be very close to the measured PM10 data (2009-2011) from Gujarat Pollution Control Board, by S. Kar and M. Damodaran

Air pollution transport and dispersion in the atmospheric boundary layer are modeled by the advection-diffusion equation, that is, essentially, a statement of conservation of the suspended material in an incompressible flow. Many models simulating air pollution dispersion are based upon the solution (numerical or analytical) of the advection-diffusion equation assuming turbulence parameterization for realistic physical scenarios. We present the general time dependent three-dimensional solution of the advection-diffusion equation considering a vertically inhomogeneous atmospheric boundary layer for arbitrary vertical profiles of wind and eddy-diffusion coefficients. Numerical results and comparison with experimental data are shown.

OA gold Two commonly used models to assess air pollution concentration for investigating health effects of air pollution in epidemiological studies are Land Use Regression (LUR) models and Dispersion and Chemistry Transport Models (DCTM). Both modeling approaches have been applied in the Ruhr area, Germany, a location where multiple cohort studies are being conducted. Application of these different modelling approaches leads to differences in exposure estimation and interpretation due to the specific characteristics of each model. We aimed to compare both model approaches by means of their respective aims, modeling characteristics, validation, temporal and spatial resolution, and agreement of residential exposure estimation, referring to the air pollutants PM2.5, PM10, and NO2. Residential exposure referred to air pollution exposure at residences of participants of the Heinz Nixdorf Recall Study, located in the Ruhr area. The point-specific ESCAPE (European Study of Cohorts on Air Pollution Effects)-LUR aims to temporally estimate stable long-term exposure to local, mostly traffic-related air pollution with respect to very small-scale spatial variations (≤100 m). In contrast, the EURAD (European Air Pollution Dispersion)-CTM aims to estimate a time-varying average air pollutant concentration in a small area (i.e., 1 km2), taking into account a range of major sources, e.g., traffic, industry, meteorological conditions, and transport. Overall agreement between EURAD-CTM and ESCAPE-LUR was weak to moderate on a residential basis. Restricting EURAD-CTM to sources of local traffic only, respective agreement was good. The possibility of combining the strengths of both applications will be the next step to enhance exposure assessment.

Air quality within the European Union (EU) is controlled by the Member States' monitoring networks. In this study, measured data is compared with the EURAD (EURopean Air pollution Dispersion) model system diagnostic output. Simulations for the German state North Rhine-Westphalia (NRW) with a horizontal grid resolution of 5 km × 5 km are analyzed. The comparison is performed for NO2, O3, and PM10, for the 4-year time period from 2002 through 2005. Although the spatial representativity of data of the two systems differs, the analyzed temporal variability of the averages shows good agreement of modeled and observed concentrations for all three parameters. This confirms the applicability of the EURAD model to mesoscale air quality assessment. Discrepancies between the model and observed data occurred at low concentrations close to the detection limits of the analyzers, for high O3 concentrations in summer, and for PM10 before 2004, when earlier versions of the MM5 meteorological module and of the emission inventory were used. Possible causes of O3 overprediction and NO2 underprediction preferably showing up in summer are considered. It is found that modeled Ox = O3 + NO2 leads to better representation of the observations than the individual species themselves. The model performance can probably be increased by further development of the emission inventories and more accurate land use data. These input data seem to be the main reason for deviations between observations and model results.

A mathematical analysis as well as some field experiments were conducted to investigate the possible effects of convective currents in inclined plates exposed to solar radiation on the roof of a building. An array of such inclined plates might be used as a radiation shield to decrease the heat load of a building, as well as to mitigate the air pollution in its vicinity.

Performance and efficiency of the general purpose air-pollution dispersion modeling system based on Lagrangian particle model Spray is evaluated on highly complex terrain over Saleska region. The evaluation is made to determine if modeling system complies with the requirements of the Slovenian legislation for industrial air pollution control which requires efficient modeling systems for small domains over complex terrain. Evaluation has proved that the modeled concentrations show significant agreement in time and place with the observations. After this proof was available the model was used for regulatory planning of the new block stack.

The rapid economic development of the PRD (Pearl River delta) has led to severe air pollution. Air pollution control is critical for sustainable development. From a geographic viewpoint, this paper will focus on how to develop a virtual geographic environment (VGE) to study environmental issues. Unlike existing work, our methods pay attention to dynamic 3D volume representation in virtual PRD terrain environment, which make air pollution transportation and dispersion easily understood to public and officers. Moreover, some GIS spatial analysis and real-time interactive operation with VGE will also be implemented to support the management and control of atmospheric pollution. A prototype system is developed to construct a virtual geographic environment for simulating the PRD region air pollution and dynamic interaction. Experimental results prove that the scheme addressed in the paper is effective and feasible.

The paper shows extensive verification of Lagrangian particle model Spray coupled with Minerve 3D mass consistent diagnostic wind field model in extremely complex terrain of Zasavje region in Slovenia. On-line measured emission and ambient data was used to reconstruct air pollution situation across the area for one-year time interval.

Emission from road traffic constitutes one of the most important sources of air pollution in urban areas. This paper describes a newly developed air pollution dispersion model for open roads and highways called WORM (Weak Wind Open Road Model), and give some results using this model at Nordbysletta, Norway, in the period 1 January 2002–15 April 2002. Generally, a good correspondence was found between observed and model-predicted concentrations of nitrogen oxides (NOx), even in periods with low wind speed and (strongly) stable atmospheric conditions.

Air pollution dispersion models depend critically on the pollutant emissions, on the atmospheric photochemical processes and especially on the meteorology. In this study we restrict the analysis to the last component, the meteorology, showing up the meteorological models ability to perform good air pollution forecast in very stable conditions during night time periods. We use the mesoscale model MM5 coupled to CMAQ model. Results indicate that in very stable conditions and in topographically complex areas, models usually tend to forecast lower winds and higher temperatures, thus noticeably modifying the dispersion patterns.

The objective of this investigation is to utilize nanoscale materials, devices, and systems as in-situ sensors and detectors of chemical and biological agents coupled with remote satellite image processed data for environmental air pollution monitoring. The efficacy of nanoparticles based environmental sensing using commercially available nanoscale metal-oxide based gas detectors is augmented by image processed satellite data to monitor local and regional air pollution dispersion. To extend our research to other forms of pollution, we present preliminary investigations of nanotechnology based sensors towards mitigating air and water based contaminants. Similar extensions of preliminary research results towards the detection of food and air-borne pathogens using nanoparticles based diagnostic tools are also presented

Information about air pollution at the territory of the municipal Pancevo (∼30×30 km), recently was attainable only from two air monitoring stations located in the city, in spite of suburbs of the city are in touch with faces of the industrial plants which routinely emit more than million tons of air pollutants in the atmosphere yearly.

In 1991, three institutions from Slovenia and Italy performed a measuring campaign to collect data for the validation of air pollution dispersion models. The target was a local area around the Sostanj Thermal Power Plant in north-east Slovenia. As one of the co-authors of the work we decided to renew the database and to produce the necessary up-to-date information that would allow other researchers to use this experimental database for modern models testing. The measurements data set describes in detail the following situation: Sostanj Thermal Power Plant at that time did not have any desulphurisation plants, therefore the sulphur dioxide (SO2) continuously emitted from its blocks was acting like a tracer in the environment. The area is characterised by very complex meteorological situations. In the paper we will present a description of the experiment (area description, site, terrain, measurement stations location, database) and two most interesting cases suitable for a model validation (emission, ambient concentrations, meteorology).

In the paper a multilayer, dynamical model of air quality analysis is presented. The model has been designed for predicting dispersion and deposition of air pollution in urban and industrial areas and for evaluation of emission control strategies. The computer implementation of pollutant dispersion is based on numerical solving advection-diffusion equations. The wind field structure and dynamics are preprocessed by a specialized generator. The real data computational examples are presented.

Atmospheric dispersion model accuracy is dependent not only on the quality of the data used, but also on the methodology of the model and its user. The air pollution dispersion model CALINE4 (Benson, 1984; 1992) is commonly used in Ireland but was developed outside Ireland and has not been validated for use in Irish conditions. The objective of this work was to validate the use of the model for road traffic emissions in Ireland. However, the results will also be of wider European interest. The model was assessed using one year of air quality data collected at two sites: a free-flowing motorway and a periodically congested roundabout. Model predictions of CO concentrations were generally within ±50% of measured concentrations, though under stable atmospheric conditions, and when wind speeds are low, significant discrepancies may arise. The major difficulty in modelling was found to be the scarcity of local traffic, meteorological and background concentration data.

To support the data interpretation of the dense air pollution monitoring network in The Netherlands, two numerical models were developed which describe the air pollution transport over the 400 km × 400 km area around the country. The models are designed for efficient, operational applications on a mini-computer system and require only limited sets of input data. The Eulerian GRID model, described in this paper, is based on the pseudo-spectral advection scheme for a 32 × 32 grid with a gridsize of 15km. Vertical stratification in a surface, mixing and reservoir layer enables an adequate description of the temporal variability of concentrations. Spatial correlations between measured and modelled SO2 concentrations are moderate. Some lack of correlation is ascribed to inaccuracies in foreign emission data rather than the degree of meteorological sophistication.

The effect of the short-term correlation of a specific emission (sulfur dioxide) from residential space heating, with air pollution dispersion rates on the accuracy of model estimates of urban air pollution on a seasonal or annual basis is analyzed. Hourly climatological and residential emission estimates for six U.S. cities and a simplified area source-dispersion model based on a circular receptor grid are used. The effect on annual average concentration estimations is found to be slight (approximately + or - 12 percent), while the maximum hourly concentrations are shown to vary considerably more, since maximum heat demand and worst-case dispersion are not coincident. Accounting for the correlations between heating demand and dispersion makes possible a differentiation in air pollution potential between coastal and interior cities.

The lake-breeze circulation on the Great Lakes is often as vigorous as its oceanic counterpart. This paper shows that lake breezes frequently exert drastic control on mesoscale air pollution patterns in urbanized shore-line areas, in this case, Chicago, Ill. Observational data were gathered from a surface mesonetwork, surface and satellite cloud photography, a chain of pilot balloons normal to the shore, optically tracked constant-level balloons, and aircraft measurements of suspended particulate concentrations in several size ranges. On the 2 late summer days studied, the lake breezes were extremely well developed. Inflow depths ranged from 500 to 1000 m, with peak inflow velocities of 6–7 m/s. Beginning at the shore-line between 0800 and 0900 LST, the breezes penetrated inland over 40 km. Clearly defined return flow layers were present both days. Eulerian wind field measurements from serial pilot, balloon releases were used to make cross sections of the u wind component. Computed one-dimensiona...

Environmental impact assessment (EIA) of air pollution diffusion of different kinds of hazardous sources can provide support information for decision making of emergency plan preparation and approval of new construction project. A GIS-based software was developed as a computer aided tool for EIA of atmospheric pollution dispersion. The industrial source complex (ISC3) dispersion models developed by US Environmental Protection Agency were used for calculation of the ground-level concentrations for point, area, flare and volume sources. The Fortran code named SCREEN3 was integrated to implement ISC3 models. Hybrid programing technology of C# and Fortran was used to develop the software. ArcGIS was used as GIS platform to represent the pollutant concentration with contour visualization on a map. The developed software can be used as a tool for air pollution assessment with preliminary estimation scheme (screening scheme).

For the quantitative interpretation of the measurement results of the Dutch national air pollution monitoring network two retrospective numerical models were developed. Based on the same meteorology both models only differ in numerical treatment of air pollution transport. In the Eulerian GRID-model advection is simulated by the pseudo-spectral advection scheme as given by Christensen and Prahm (1976), over a 32×32 grid with gridpoint-distances of 15 km, covering the 450×450 km2 surroudings of the Netherlands. In the Lagrangian PUFF-trajectory model the 15×15 km2 source areas are aggregated to puffs which are advected according to the same wind fields as used in the GRID-model.

This paper describes a simplified method, the Smeared Concentration Approximation, developed to characterize air pollution dispersion for use in models concerned with long-term policy analysis at the regional level. The methodology centers around the urban scale. The main assumptions behind the approximation are described and the SCA Dispersion Parameter is defined. Development of the SCA Dispersion parameter, using detailed air pollution despersion models, is described. Illustrations of the use of this methodology in regional energy/environment analysis are presented and some conclusions concerning the use of the method are given.

A major application of air pollution dispersion models is to aid in the selection of abatement plans. Present technology dictates that air pollution abatement be a result of altering an area's emission characteristics. The need for pollution abatement is most acute during air pollution episodes. This study is an investigation of how air quality in a hypothetical city is affected by the city's emission characteristics during a simulated air pollution episode. Emission characteristics examined include configuration of sources, diurnal emission rate schedules, total emission rates, and the combination of point and area sources. Results are used to gain insight into how models may be used to reduce air pollution emergencies through long term planning and short term abatement schemes. Among the observations are: 1. (1) air quality is improved significantly by selecting configurations whose major sources are too far apart to jointly contaminate a locality. Increasing the mean distance between major sources 75 per cent led to an improvement in air quality as high as 28 per cent; 2. (2) altering diurnal schedules of emission rates to take advantage of diurnal variations in atmospheric dilutive capacity improved air quality by as much as 23 per cent; 3. (3) the set of priorities given to emission rate reductions of different types of sources is an important determinant of air quality improvement attained with a given reduction in the total emission rate. Differences as high as 28 per cent were observed for a given reduction in the total emission rate; 4. (4) pollutant concentration at a receptor location is primarily the result of emissions in contiguous locations; 5. (5) longer time lags between emission rate reductions and air quality improvement during episodes place a premium on controls which can be implemented quickly. The model is verified using St. Louis emission data. Predicted concentrations are within a factor of 1.4 of observed ones.

Forecasting air pollution is considered as an essential key for early warning and control management of air pollution, especially in emergency situations, where big amounts of pollutants are quickly released in the air, causing considerable damages. Predicting pollution in such situations is particularly challenging due to the strong dynamic of the phenomenon and the various spatio-temporal factors affecting air pollution dispersion. In addition, providing uncertainty estimates of prediction makes the forecasting model more trustworthy, which helps decision-makers to take appropriate actions with more confidence regarding the pollution crisis. In this study, we propose a multi-point deep learning model based on convolutional long short term memory (ConvLSTM) for highly dynamic air quality forecasting. ConvLSTM architectures combines long short term memory (LSTM) and convolutional neural network (CNN), which allows to mine both temporal and spatial data features. In addition, uncertainty quantification methods were implemented on top of our model’s architecture and their performances were further excavated. We conduct extensive experimental evaluations using a real and highly dynamic air pollution data set called Fusion Field Trial 2007 (FFT07). The results demonstrate the superiority of our proposed deep learning model in comparison to state-of-the-art methods including machine and deep learning techniques. Finally, we discuss the results of the uncertainty techniques and we derive insights.