Model ADMS-Urban Research Articles

Development of a photocell-based system supported by simulations for road vehicle counting and particle emissions estimation

This work presents the design and implementation of an innovative system that utilizes photocells for road vehicle counting and computational simulations to estimate traffic-related particulate (PM10) emissions. The integration of photocell technology has resulted in a robust system with reduced counting errors, ensuring high accuracy in correlating traffic counts with emission values. The system was evaluated under various environmental conditions, demonstrating its effectiveness in providing reliable real-time data on the impact of road traffic on particle emissions. The methodology included the use of the ADMS-Urban model for emission estimation and simulations with Ansys Fluent, enabling the acquisition of dependable real-time data. The results indicate a strong agreement between measured and calculated PM10 concentrations, with the system maintaining precision comparable to direct observations. The observed values of 5.0 µg/m3 and 19.2% for absolute and relative errors, respectively, demonstrate the system's remarkable performance, supported by a robust correlation represented by R2 = 0.88. This strong correlation underscores the reliability of the employed model, suggesting its capability to capture and explain a substantial portion of the variability in PM10 concentrations. Validation campaigns conducted on a street in Portugal confirmed the system's ability to accurately count vehicles and estimate particle emissions. The analysis revealed a significant agreement between measured and calculated average PM10 concentrations, with standard deviation values of 7.0 µg/m3 for measured concentrations and 7.5 µg/m3 for calculated concentrations, suggesting consistency between the datasets. These conclusions confirm that the system is a valuable tool for researchers and policymakers, providing crucial insights for the management and improvement of urban air quality, contributing to the development of urban planning policies and environmental sustainability.

Study on the Concentration of Top Air Pollutants in Xuzhou City in Winter 2020 Based on the WRF-Chem and ADMS-Urban Models

In recent years, the issue of air pollution has garnered significant public attention globally, with a particular emphasis on the challenge of atmospheric fine particulate matter (PM2.5) pollution. The efficient and precise simulation of changes in pollutant concentrations, as well as their spatial and temporal distribution, is essential for effectively addressing the air pollution issue. In this paper, the WRF-Chem model is used to simulate the meteorological elements including temperature (T), relative humidity (RH), wind speed (WS), and pressure (P), and the concentrations of PM2.5 and PM10 atmospheric pollutants in December 2020 in Xuzhou City. Simultaneously, the ADMS-Urban model was employed to conduct a higher spatial resolution study of PM2.5 concentrations during the heavy pollution days of 11–12 December 2020 in Xuzhou City. The study shows that the WRF-Chem model can simulate the meteorological conditions of the study time period better, and the correlation coefficients (R) of pressure, temperature, wind speed, and relative humidity are 0.99, 0.87, 0.75, and 0.70, respectively. The WRF-Chem model can accurately simulate the PM2.5 concentration on clean days (R of 0.66), but the simulation of polluted days is not satisfactory. Therefore, the ADMS-Urban model was chosen to simulate the PM2.5 concentration on polluted days in the center of Xuzhou City. The ADMS-Urban model can simulate the distribution characteristics and concentration changes of PM2.5 around roads and buildings in the center of Xuzhou City. Comparing the simulation results of the two models, it was found that the two models have their own advantages in PM2.5 concentration simulation, and how to better couple the two models is the next research direction.

Health impact assessment of port-sourced air pollution in Barcelona.

Air pollution is a major health risk factor. Ports might be an understudied source of air pollution. We conducted a spatial health impact assessment (HIA) of port-sourced air pollution for Barcelona for 2017 at the neighbourhood level. Total NO2 and PM10 and port-sourced NO2, PM10 and PM2.5 concentrations were available through the ADMS-Urban model. Population data, mortality and morbidity data, and risk estimates were obtained. We followed standard HIA methodologies and calculated relative risks and impact fractions for 1.35 million adults living in 73 neighbourhoods. The city-wide mean total NO2 and PM10 concentrations were 37.88 μg/m3 (range: 19.61-52.17 μg/m3) and 21.68 μg/m3 (range: 17.33-26.69 μg/m3), respectively, of which 7% (range: 2-36%) and 1% (range: 0-7%) were port-sourced, respectively. The mean port-sourced PM2.5 concentration was 0.19 μg/m3 (range: 0.06-1.38 μg/m3). We estimated that 1,123 (PI: 0-3,060) and 1,230 (95% CI: 0-2,566) premature deaths were attributable to total NO2 and PM10, respectively, of which 8.1% (91; PI: 0-264) and 1.1% (13; 95% CI 0-29) were attributable to port-sourced NO2 and PM10, respectively. 20 (95% CI: 15-26) premature deaths were attributable to port-sourced PM2.5. Additionally, a considerable morbidity burden and losses in life expectancy were attributable to port-sourced air pollution. Neighbourhoods closest to the port in the south-east were most adversely affected, gradually decreasing towards the north-west. The port is an understudied air pollution source in Barcelona with strong health impacts. Cities need local insight into health risk factors, their sources, attributable burdens and distributions for defining targeted policies.

Application of ADMS-Urban for an area with a high contribution of residential heating emissions - model verification and sensitivity study for PM2.5

Air pollution poses a significant risk to both human health and the environment in the contemporary world. Among the various pollutants, particulate matter with a diameter <2.5 μm (PM2.5) is regarded as the most hazardous. It has been implicated in over four million global fatalities in 2019 alone. This research paper divulges the outcomes of modelling the spatial-temporal fluctuations of PM2.5 concentrations within the confines of Wroclaw, a city situated in Poland, Central Europe. The model's output was evaluated through comparison with collected data from two government-operated monitoring stations within the city. For this study, we used the ADMS-Urban model and tested two different sources of background data (low-cost sensors and the EMEP MSC-W atmospheric chemistry transport model). The statistical analysis conducted in the paper indicates that the model reproduces the temporal variability of PM2.5. The conclusions from this research indicate that the average annual PM2.5 concentration within Wroclaw is 13.8 μg/m3, with the concentration peaking in the month of March. The spatial distribution reveals the highest PM2.5 concentrations primarily in the southern and western zones of the city, with additional elevated concentrations observed sporadically throughout the city. The study unveils that 1.3 % of Wroclaw's area experiences PM2.5 concentrations exceeding the EU's annual limit of 20 μg/m3. When considered in relation to the WHO's suggested annual average level of 5 μg/m3, Wroclaw city experiences exceedances throughout. When background concentrations are excluded from the model, the annual average PM2.5 concentration across the city is noted to be reduced by >50 %. A thorough investigation of the city's emission structure, taking into account only emissions from the city without background, indicates that the residential sector contributes about 77.3 % of the total annual average PM2.5 concentration in Wroclaw. The transportation and industrial sectors account for nearly 19.5 % and 3.2 %, respectively.

Modelling the dispersion of particle number concentrations in the West Midlands, UK using the ADMS-Urban model

Ultrafine particles (UFPs) are respirable particles with a diameter less than 100 nm, which some studies have associated with adverse effects upon health. UFPs are currently not regulated as the health evidence is insufficient and very few observational data are available in most cities. The 2021 WHO Global Air Quality Guidelines highlighted the pressing issue of UFPs and provided a good practice statement for UFPs, which recommends that more measurement and modelling studies are implemented in future. Particle number concentrations (PNC) are the most common metric for UFPs as this fraction normally dominates the total ambient PNC in urban environments. This study simulates the dispersion of particle number concentrations in the West Midlands (a metropolitan area), UK using the local scale ADMS-Urban model, which is an advanced quasi-Gaussian plume dispersion modelling system. ADMS-Urban implements a physics-based approach to represent the characteristics of the atmospheric boundary layer and has been widely used in the dispersion modelling of air pollutants. It can represent a variety of source types (such as road and grid emissions) occurring in urban environments and requires a range of input data. Particle number was used as a passive scalar, with no inclusion of aerosol microphysics within the model, as a first implementation in the ADMS-Urban model for the West Midlands, UK. Evaluation was conducted by comparing the modelled (from a receptor run) and measured data at the Birmingham Air Quality Supersite. Overall, the model performed well although there was a slight underestimation for PNC. Based on the modelling output from a contour run, PNC maps at a variety of spatial scales (i.e. street scale, ward level and local authority level) and temporal resolutions (i.e. annual, 24-hour, and 1-hour) were generated. PNC mapping could be linked to local population and health data for potential epidemiological studies.

Modelling spatiotemporal variations of the canopy layer urban heat island in Beijing at the neighbourhood scale

Abstract. Information on the spatiotemporal characteristics of Beijing's urban–rural near-surface air temperature difference, known as the canopy layer urban heat island (UHI), is important for future urban climate management strategies. This paper investigates the variation of near-surface air temperatures within Beijing at a neighbourhood-scale resolution (∼ 100 m) during winter 2016 and summer 2017. We perform simulations using the urban climate component of the ADMS-Urban model with land surface parameters derived from both local climate zone classifications and OpenStreetMap land use information. Through sensitivity simulations, the relative impacts of surface properties and anthropogenic heat emissions on the temporal variation of Beijing's UHI are quantified. Measured UHI intensities between central Beijing (Institute of Atmospheric Physics) and a rural site (Pinggu) during the Atmospheric Pollution and Human Health in a Chinese Megacity (APHH-China) campaigns, peak during the evening at ∼ 4.5 ∘C in both seasons. In winter, the nocturnal UHI is dominated by anthropogenic heat emissions but is underestimated by the model. Higher-resolution anthropogenic heat emissions may capture the effects of local sources (e.g. residential buildings and adjacent major roads). In summer, evening UHI intensities are underestimated, especially during heatwaves. The inability to fully replicate the prolonged release of heat stored in the urban fabric may explain this. Observed negative daytime UHI intensities in summer are more successfully captured when surface moisture levels in central Beijing are increased. However, the spatial correlation between simulated air temperatures and satellite-derived land surface temperatures is stronger with a lower urban moisture scenario. This result suggests that near-surface air temperatures at the urban meteorological site are likely influenced by fine-scale green spaces that are unresolved by the available land cover data and demonstrates the expected differences between surface and air temperatures related to canopy layer advection. This study lays the foundations for future studies of heat-related health risks and UHI mitigation strategies across Beijing and other megacities.

Combining Chemical Composition Data and Numerical Modelling for the Assessment of Air Quality in a Mediterranean Port City

The aim of this study is to characterize the air quality in a Mediterranean port city. The impact of ship emissions on both gaseous and particulate pollutants has been investigated through an integrated methodology which includes atmospheric flow and dispersion numerical modelling as well as chemical composition and statistical analyses. Specifically, chemical compositional data (ionic fraction, carbonaceous compounds, and metals) of PM2.5 were acquired during an experimental field campaign carried out in the port city of Brindisi (Apulia Region, Southern Italy). The sampling site was located on the roof of a building (ASI) within the port area. Given the complexity of the site in which both domestic buildings and a large industrial area are present, analyses were done by selecting different wind sectors to test different techniques to discriminate between sources. Linear Discriminant Analysis (LDA) and Principal Component Analysis (PCA) were applied to evaluate statistical differences in the composition of PM2.5 sampled within the area when the sampling site was downwind to the port or to the urban-industrial area. Only LDA allowed to discriminate the separation between urban-industrial and port macroareas. Those results were further confirmed in terms of PM2.5 concentrations directly associated to ship emissions using a coupled modelling approach. The mesoscale model BOLCHEM was used to investigate the contribution of ship emissions both on primary and secondary PM2.5 concentration in the area surrounding the port, as well as on PM10, NOX and O3 concentrations. Then, the model was coupled offline with the local dispersion model ADMS-Urban. The adopted approach was crucial to evaluate the spatial distribution of the impact of ship emissions. BOLCHEM results showed that in the cell of the port the average impact of ship emissions on NOX was 37.6%, and −11.7% on O3. The average impact on PM2.5 was 6.1%, distributed between primary (2.7%) and secondary fraction (3.4%). At local scale, the analysis of high-resolution modelling results obtained from ADMS-Urban highlighted that, at ASI position, the impact of ship emissions on PM2.5 was 6.8% when the sampling site was positioned downwind to the port area and reduced to lower than 3.0% at about 2 km from the sources.

Seasonal and site-specific variation in particulate matter pollution in Lithuania

The levels of particulate matter tend to exceed the limit values in many countries across the world. Epidemiological studies have found the associations between particulate matter pollution and human health effects. Therefore, the accurate assessment of particulate matter pollution in urban areas is essential for evaluating the risk to human health. Particulate matter concentration varies between different seasons and it is important to understand the differences in pollution sources in each season and their contributions to the total particulate matter pollution in the city or in the different areas of the city. Better understanding of these variations of particulate matter would lead to an improvement of the assessment of exposure to air pollution. In present study, particles less than 10 μm in aerodynamic diameter (PM10) were seasonally modelled using the ADMS-Urban model and compared with a measurement campaign to estimate seasonal differences in site-specific concentrations of PM10 in Kaunas city, Lithuania. The statistical evaluation of model performance was calculated for seasonal and annual concentrations of PM10 for 20 measurement sites. The results of the study showed that ADMS-Urban model performed well in predicting PM10 concentrations, with annual mean measured and modelled concentrations of 30.3 and 25.7 μg/m3, respectively. The RMSE was 5.0 μg/m3, the index of agreement was 0.72, the correlation coefficient was 0.87, and the relative percentile error 15%. The results of the site-specific analysis of PM10 indicated that ADMS-Urban model considers different characteristics of sites relatively well and can be applied for exposure estimates in cohort studies.

Epidemiological population-based cohort study on mortality and hospitalization in the area near the waste incinerator plant of San Zeno, Arezzo (Tuscany Region, Central Italy)

to evaluate whether exposure to an urban waste incinerator plant operating in Arezzo (Tuscany Region, Central Italy) since the 2000 is associated with mortality and morbidity. a population-based cohort study of inhabitants living close to the incinerator in the period 2001-2010 was conducted. The individual exposure histories to the incinerator and other sources in the area were estimated using a dispersion model producing PM10 concentration maps for each source (ADMS-URBAN model). Hazard Ratios (HR) with 95% Confidence Interval (95%CI) adjusted for the other environmental exposures, age and socioeconomic status were estimated for the highest class of exposure using the lowest one as reference. Trends of HR were evaluated too. mortality and hospitalization causes with induction-latent period overlapping the incinerator activity period were analysed. morbidity analysis showed an increased risk for cardiovascular diseases (No. 732; HR: 1.18; 95%CI 1.06-1.32; trend of HR: 1.08, p=0.006) and a trend of HRs for urinary diseases (trend: 1.13; p=0.063). Mortality analysis showed a trend of HRs for general mortality in males (trend: 1.10; p=0.014), for cardiovascular diseases in males (trend: 1.15; p=0.036), for respiratory diseases in females (trend: 1.30; p=0.098), and an excess of acute respiratory diseases in females with the highest exposure (No. 11; HR=2.54; 95%CI 0.84-7.68). the cohort study allowed to characterise the environmental exposures considering the most relevant pollution sources and providing estimation of individual environmental exposures. Residual confounding due to the overlapping of concentration maps cannot be excluded and further investigations are needed. However, the increased risks for cardiovascular and respiratory diseases reinforce the limited epidemiological evidence on health effects of incinerators.

The statistical evaluation and comparison of ADMS-Urban model for the prediction of nitrogen dioxide with air quality monitoring network.

In many countries, road traffic is one of the main sources of air pollution associated with adverse effects on human health and environment. Nitrogen dioxide (NO2) is considered to be a measure of traffic-related air pollution, with concentrations tending to be higher near highways, along busy roads, and in the city centers, and the exceedances are mainly observed at measurement stations located close to traffic. In order to assess the air quality in the city and the air pollution impact on public health, air quality models are used. However, firstly, before the model can be used for these purposes, it is important to evaluate the accuracy of the dispersion modelling as one of the most widely used method. The monitoring and dispersion modelling are two components of air quality monitoring system (AQMS), in which statistical comparison was made in this research. The evaluation of the Atmospheric Dispersion Modelling System (ADMS-Urban) was made by comparing monthly modelled NO2 concentrations with the data of continuous air quality monitoring stations in Kaunas city. The statistical measures of model performance were calculated for annual and monthly concentrations of NO2 for each monitoring station site. The spatial analysis was made using geographic information systems (GIS). The calculation of statistical parameters indicated a good ADMS-Urban model performance for the prediction of NO2. The results of this study showed that the agreement of modelled values and observations was better for traffic monitoring stations compared to the background and residential stations.

Exposure to emissions from municipal solid waste incinerators and miscarriages: A multisite study of the MONITER Project

BackgroundMiscarriages are an important indicator of reproductive health but only few studies have analyzed their association with exposure to emissions from municipal solid waste incinerators.This study analyzed the occurrence of miscarriages in women aged 15–49years residing near seven incinerators of the Emilia-Romagna Region (Northern Italy) in the period 2002–2006. MethodsWe considered all pregnancies occurring in women residing during the first trimester of pregnancy within a 4km radius of each incinerator. Addresses were geocoded and exposures were characterized by a dispersion model (ADMS Urban model) producing pollution maps for incinerators based on PM10 stack measurements and for other pollution sources based on NOx ground measurements. Information on pregnancies and their outcomes was obtained from the Hospital Discharge Database. Simplified True Abortion Risks (STAR)×100 estimated pregnancies were calculated. We ran logistic regressions adjusting for maternal characteristics, exposure to other sources of pollution, and sites, considering the whole population and stratifying by miscarriage history. ResultsThe study analyzed 11,875 pregnancies with 1375 miscarriages. After adjusting for confounders, an increase of PM10 due to incinerator emissions was associated with an increased risk of miscarriage (test for trend, p=0.042). The odds ratio for the highest quartile of exposed versus not exposed women was 1.29, 95% CI 0.97–1.72. The effect was present only for women without previous miscarriages (highest quartile of exposed versus not exposed women 1.44, 95% CI 1.06–1.96; test for trend, p=0.009). ConclusionExposure to incinerator emissions is associated with an increased risk of miscarriage. This result should be interpreted with those of a previous study on reproductive health conducted in the same area that observed an association between incinerator exposure and preterm births.

Estimation of inter-seasonal differences in NO2 concentrations using a dispersion ADMS-Urban model and measurements

Air pollution dispersion modeling is one of the methods for the ambient air quality assessment. The mathematical models are widely used tools for predicting air pollution and accurately assessing the spatial distribution of air pollutants in the city. Road transport is one of the main sources of pollution affecting air quality in urban areas, and nitrogen dioxide is considered to be one of the most common with traffic emission-related pollutant, which concentrations are strongly correlated to the distance from the roadways. The study aim was to determine the dispersion of nitrogen dioxide (NO2) concentration and to estimate the inter-seasonal differences in these concentrations at different sites (rural, urban, traffic) using a ADMS-Urban model and measurements in Kaunas city. For the modeling of nitrogen dioxide pollution, the year 2011 was selected. Average concentrations of nitrogen dioxide for the winter, summer, and intermediate spring seasons were modeled. Nitrogen dioxide concentrations calculated by model have been verified against 41 Ogawa passive sampling results. The modeling results showed that the highest average seasonal NO2 concentration was modeled in the winter season (21.79 μg/m3), while the lowest concentration was determined in summer (12.28 μg/m3). Correlation coefficient between modeled NO2 concentrations using the ADMS-Urban model and measured by Ogawa passive samplers was significantly high for all seasons and confirmed the generally good performance of the model.

The application of ADMS-Urban model to estimate nitrogen dioxide concentrations in cold and warm seasons in Kaunas city

Road traffic is one of the main sources of pollution affecting air quality in urban areas which can have impacts on human health. One of the most common traffic-related pollutants is nitrogen dioxide (NO2). Atmospheric dispersion models, such as ADMSUrban, can be used to examine emissions from many various sources (road transport, industrial, domestic) and to estimate or predict the concentration of pollutants emitted from these sources into the atmosphere. The paper examines the performance of ADMS-Urban dispersion model to predict nitrogen dioxide concentrations from emission sources. Modelled concentrations for cold and warm seasons have been compared with measured nitrogen dioxide concentrations at 41 site in Kaunas city. The purpose of this study was to assess the dispersion of nitrogen dioxide pollution in cold and warm seasons comparing modelled with ADMS-Urban model and measured with passive samplers NO2 concentrations in Kaunas city. Modelled average nitrogen dioxide concentration in cold season was 21.79 μg/m3, while measured with passive samp­lers – 19.82 μg/m3. The air pollution was significantly higher during the cold season. Modelled and measured NO2 concentrations in warm season were 12.28 and 11.62 μg/m3, respectively. Results showed that ADMS-Urban dispersion model tends to overpredict nitrogen dioxide concentrations and this is most evident when smaller values are modelled, while model­led maximum concentrations are underestimated compared with measured NO2 values.

Bayesian spatiotemporal modelling for the assessment of short-term exposure to particle pollution in urban areas

This paper describes a Bayesian hierarchical approach to predict short-term concentrations of particle pollution in an urban environment, with application to inhalable particulate matter (PM10) in Greater London. We developed and compared several spatiotemporal models that differently accounted for factors affecting the spatiotemporal properties of particle concentrations. We considered two main source contributions to ambient measurements: (i) the long-range transport of the secondary fraction of particles, which temporal variability was described by a latent variable derived from rural concentrations; and (ii) the local primary component of particles (traffic- and non-traffic-related) captured by the output of the dispersion model ADMS-Urban, which site-specific effect was described by a Bayesian kriging. We also assessed the effect of spatiotemporal covariates, including type of site, daily temperature to describe the seasonal changes in chemical processes affecting local PM10 concentrations that are not considered in local-scale dispersion models and day of the week to account for time-varying emission rates not available in emissions inventories. The evaluation of the predictive ability of the models, obtained via a cross-validation approach, revealed that concentration estimates in urban areas benefit from combining the city-scale particle component and the long-range transport component with covariates that account for the residual spatiotemporal variation in the pollution process.

Spatially-varying surface roughness and ground-level air quality in an operational dispersion model

Urban form controls the overall aerodynamic roughness of a city, and hence plays a significant role in how air flow interacts with the urban landscape. This paper reports improved model performance resulting from the introduction of variable surface roughness in the operational air-quality model ADMS-Urban (v3.1). We then assess to what extent pollutant concentrations can be reduced solely through local reductions in roughness. The model results suggest that reducing surface roughness in a city centre can increase ground-level pollutant concentrations, both locally in the area of reduced roughness and downwind of that area. The unexpected simulation of increased ground-level pollutant concentrations implies that this type of modelling should be used with caution for urban planning and design studies looking at ventilation of pollution. We expect the results from this study to be relevant for all atmospheric dispersion models with urban-surface parameterisations based on roughness.

Comparative Assessment of Particulate Air Pollution Exposure from Municipal Solid Waste Incinerator Emissions

Background. Research to date on health effects associated with incineration has found limited evidence of health risks, but many previous studies have been constrained by poor exposure assessment. This paper provides a comparative assessment of atmospheric dispersion modelling and distance from source (a commonly used proxy for exposure) as exposure assessment methods for pollutants released from incinerators. Methods. Distance from source and the atmospheric dispersion model ADMS-Urban were used to characterise ambient exposures to particulates from two municipal solid waste incinerators (MSWIs) in the UK. Additionally an exploration of the sensitivity of the dispersion model simulations to input parameters was performed. Results. The model output indicated extremely low ground level concentrations of PM10, with maximum concentrations of <0.01 μg/m3. Proximity and modelled PM10 concentrations for both MSWIs at postcode level were highly correlated when using continuous measures (Spearman correlation coefficients ~ 0.7) but showed poor agreement for categorical measures (deciles or quintiles, Cohen's kappa coefficients ≤ 0.5). Conclusion. To provide the most appropriate estimate of ambient exposure from MSWIs, it is essential that incinerator characteristics, magnitude of emissions, and surrounding meteorological and topographical conditions are considered. Reducing exposure misclassification is particularly important in environmental epidemiology to aid detection of low-level risks.

ADMS-Urban: developments in modelling dispersion from the city scale to the local scale

Many countries perform national air quality assessments using grid-based numerical air dispersion models, generally referred to as ‘regional’ models. Advantages of these models include the ability to use temporally and spatially varying meteorology and model chemical reactions over large temporal and spatial scales. These models usually perform reasonably well against rural and urban background monitors, but predictions at roadside monitors are underestimated. City-scale air dispersion models have been developed to give high spatial resolution, but are usually restricted to use spatially homogeneous meteorological data and to model simplified chemical reactions over short time scales. Thus, regional and city-scale air dispersion models have complementary strengths and a system where a city-scale model is nested within a regional model allows accurate air dispersion modelling over a range of spatial scales. This paper presents preliminary modelling results from a system where the local model ADMS-Urban is nested within the regional model, CMAQ.

INTEGRATED HEALTH IMPACT ASSESSMENT OF WASTE MANAGEMENT IN LAZIO (ITALY)

Background and Aims: the management of municipal solid waste (MSW) may entail health effects difficult to be quantified. We evaluated potential health impacts arising from MSW management under a 2008 baseline scenario (BS) and an alternative 2016 green scenario (GS) which includes decrease in waste production, increase in recycling, clean transportation, and no landfilling without pre-treatment. Methods: we evaluated the population of the Lazio region in Italy (5.5 million inhabitants). Pollutants concentrations from transport, incineration, and combustion of landfills biogas were modelled using the ADMS-Urban model. Population-weighted exposure levels were calculated. Concentration-response functions were derived from systematic reviews of the literature. Cancer incidence (CI), low-birth weight (LBW) newborns, congenital anomalies (CA), respiratory symptoms (RS) and odour annoyance (OD), Years of Life Lost (YLL), and Disability Adjusted Life Years (DALYs) were estimated for the period 2016-2050. Results: at the BS, 36,191 people lived nearby MSW facilities, while 14.606 were estimated under the GS. Waste transport health impact was estimated in 561 YLL at BS, 29 under the GS. For incinerators, CI was 7.5 (BS) and 2.5 (GS) cases. 10 YLLs were estimated at BS whereas 9.6 for GS. For landfills, LBW cumulative incidence was 8.3 cases (BS) and 2.8 (GS), respectively. The cumulative incidence of CA was 0.3 (BS) and 0.1 (GS) cases. The health impact as YLL was 17.9 (NO2) at BS and 12.4 with GS. At the BS the prevalence of severe OD and RS were the same, 54 and 424, respectively while a decrease to 19 and 147 were predicted with the GS. About 3000 and 1600 DALYs were estimated under the two scenarios. The largest contribution to DALYs was from RS (about 90%) and OD. Conclusions: although MSW management impact on health is moderate, a substantial improvement can be realized with a green waste policy.

MUST experiment simulations using CFD and integral models

This paper looks at the application of Computational Fluid Dynamics (CFD) and integral approaches to the study of effects of obstacles on pollutant dispersion from a point source placed within an idealised urban area (MUST). This study is part of a modelling exercise within the COST Action 732. Numerical results are compared with wind tunnel data. We use the CFD code FLUENT and the dispersion model ADMS-Urban. The CFD model predicts concentrations more accurately than the integral model. However, both models results satisfy accepted statistical criteria, showing that those criteria should not be the only way of evaluating a model.

Statistical and diagnostic evaluation of the ADMS-Urban model compared with an urban air quality monitoring network

The present study examines the behaviour of the ADMS-Urban air quality forecasting model in predicting dispersion of traffic-related pollutants in urban areas. The study has been carried out in Ravenna (NE Italy), a medium-sized town where pollution produced by vehicle traffic accounts for most of the emissions. ADMS-Urban performances have been assessed through statistical analysis, by comparing carbon monoxide concentrations (vehicle traffic tracing pollutant) estimated by the model with concentrations measured by stations of the air quality monitoring network. Although the correspondence of values estimated by ADMS-Urban with measured values turns out to be satisfactory, the study shows that the model tends to produce an underestimated value compared with the actual situation, and identifies a corrective method that makes it possible to improve the relevant performances. Furthermore, the diagnostic analysis highlights that the model performances depend upon some meteorological parameters.