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Air pollution represents a continuous hazard to human health. Administration, companies and population need efficient indicators of the possible effects given by a change in decision, strategy or habit. The monetary quantification of health effects of air pollution through the definition of external costs is increasingly recognized as a useful indicator to support decision and information at all levels. The development of modelling tools for the calculation of external costs can provide support to analysts in the development of consistent and comparable assessments. In this paper, the DIATI Dispersion and Externalities Model (DIDEM) is presented. The DIDEM model calculates the delta-external costs of air pollution comparing two alternative emission scenarios. This tool integrates CALPUFF's advanced dispersion modelling with the latest WHO recommendations on concentration-response functions. The model is based on the impact pathway method. It was designed to work with a fine spatial resolution and a local or national geographic scope. The modular structure allows users to input their own data sets. The DIDEM model was tested on a real case study, represented by a comparative analysis of the district heating system in Turin, Italy. Additional advantages and drawbacks of the tool are discussed in the paper. A comparison with other existing models worldwide is reported. [ABSTRACT FROM AUTHOR]

Abstract: Using the data on all live births (∼400,000) and criteria pollutants from the Chicago Metropolitan Statistical Area (MSA) between 2000 and 2004, this paper empirically demonstrates how mismatches in the spatiotemporal scales of health and air pollution data can result in inconsistency and uncertainty in the linkages between air pollution and birth outcomes. This paper suggests that the risks of low birth weight associated with air pollution exposure changes significantly as the distance interval (around the monitoring stations) used for exposure estimation changes. For example, when the analysis was restricted within 3 miles distance of the monitoring stations the odds of LBW (births <2500 g) increased by a factor of 1.045 (±0.0285 95% CI) with a unit increase in the average daily exposure to PM10 (in μg m−3) during the gestation period; the value dropped to 1.028 when the analysis was restricted within 6 miles distance of air pollution monitoring stations. The effect of PM10 exposure on LBW became null when controlled for confounders. But PM2.5 exposure showed a significant association with low birth weight when controlled for confounders. These results must be interpreted with caution, because the distance to monitoring station does not influence the risks of adverse birth outcomes, but uncertainty in exposure increases with the increase in distance from the monitoring stations, especially for coarse particles such as PM10 that settle with gravity within short distance and time interval. The results of this paper have important implications for the research design of environmental epidemiological studies, and the way air pollution (and potentially other environmental) and health data are collocated to compute exposure. While this paper challenges the findings of pervious epidemiological studies that have relied on coarse resolution air pollution data (such as county level aggregated data), the paper also calls for time-space resolved estimate of air pollution to minimize uncertainty in exposure estimation. [Copyright &y& Elsevier]

Concentration of fine particulate matter (PM 2.5 ) is one of the most important environmental parameters to estimate health impacts attributable to air pollution. Despite the fact there are many studies regarding PM 2.5 effects on human health, most of them were performed under conditions that do not simulate the natural particles interaction with the organism. In the present paper, we studied the effects of mammals’ sub-chronic exposure to PM 2.5 on the lower respiratory tract, addressing realistic exposure conditions to normal urban air. Thus, we exposed Wistar rats under controlled settings to the same normal urban air, with and without particles. Next, we analyzed chemical composition of PM 2.5 and lungs samples, performed a histologic examination and run the comet assay to assess genotoxic effects. We found a strong agreement between lung tissues and PM 2.5 elemental composition suggesting that metals found in lungs came from the particles inhaled. Histological analysis showed a mild to moderate infiltration, with a reduction of alveoli lumen and increment of alveolar macrophages and periodic acid-Schiff (PAS) (+) cells in treated animals. We also observed an increase in the number of nuclei with comets, mostly comets type 3, with a high DNA fragmentation as well. These results provide strong evidence that sub-chronic exposure to low particle levels, even below the 24 h WHO standard, can cause injuries in lungs tissues and DNA damage, as well. [ABSTRACT FROM AUTHOR]

Particulate air pollution has had a significant impact on human health in China and it is associated with cardiovascular and respiratory diseases and high mortality and morbidity. These health impacts could be translated to reduced labor availability and time. This paper utilized a supply-driven input-output (I-O) model to estimate the monetary value of total output losses resulting from reduced working time caused by diseases related to air pollution across 30 Chinese provinces in 2007. Fine particulate matter (PM 2.5 ) pollution was used as an indicator to assess impacts to health caused by air pollution. The developed I-O model is able to capture both direct economic costs and indirect cascading effects throughout inter-regional production supply chains and the indirect effects greatly outnumber the direct effects in most Chinese provinces. Our results show the total economic losses of 346.26 billion Yuan (approximately 1.1% of the national GDP) based on the number of affected Chinese employees (72 million out of a total labor population of 712 million) whose work time in years was reduced because of mortality, hospital admissions and outpatient visits due to diseases resulting from PM 2.5 air pollution in 2007. The loss is almost the annual GDP of Vietnam in 2010. The proposed modelling approach provides an alternative method for health-cost measurement with additional insights on inter-industrial and inter-regional linkages along production supply chains. [ABSTRACT FROM AUTHOR]

Protecting the health of growing urban populations from air pollution remains a challenge for planners and requires detailed understanding of air flow and pollutant transport in the built environment. In recent years, the work undertaken on passive methods of reducing air pollution has been examined to address the question: “how can the built environment work to alter natural dispersion patterns to improve air quality for nearby populations?” This review brings together a collective of methods that have demonstrated an ability to influence air flow patterns to reduce personal exposure in the built environment. A number of passive methods exists but, in the context of this paper, are split into two distinct categories: porous and solid barriers. These methods include trees and vegetation (porous) as well as noise barriers, low boundary walls and parked cars (solid); all of which have gained different levels of research momentum over the past decade. Experimental and modelling studies have provided an understanding of the potential for these barriers to improve air quality under varying urban geometrical and meteorological conditions. However, differences in results between these studies and real-world measurements demonstrate the challenges and complexities of simulating pollutant transport in urban areas. These methods provide additional benefits to improving air quality through altering dispersion patterns; avenue trees and vegetation are aesthetically pleasing and provides cooling and shade from direct sunlight. Additionally, real-world case studies are considered an important direction for further verification of these methods in the built environment. Developing design guidelines is an important next stage in promoting passive methods for reducing air pollution and ensuring their integration into future urban planning strategies. In addition, developing channels of communication with urban planners will enhance the development and uptake of design guidelines to improve air quality in the built environment. [ABSTRACT FROM AUTHOR]

It has been well documented that air pollution adversely affects health, and epidemiological pollution-health studies utilise pollution data from automatic monitors. However, these automatic monitors are small in number and hence spatially sparse, which does not allow an accurate representation of the spatial variation in pollution concentrations required for these epidemiological health studies. Nitrogen dioxide (NO 2 ) diffusion tubes are also used to measure concentrations, and due to their lower cost compared to automatic monitors are much more prevalent. However, even combining both data sets still does not provide sufficient spatial coverage of NO 2 for epidemiological studies, and modelled concentrations on a regular grid from atmospheric dispersion models are also available. This paper proposes the first modelling approach to using all three sources of NO 2 data to make fine scale spatial predictions for use in epidemiological health studies. We propose a geostatistical fusion model that regresses combined NO 2 concentrations from both automatic monitors and diffusion tubes against modelled NO 2 concentrations from an atmospheric dispersion model in order to predict fine scale NO 2 concentrations across our West Central Scotland study region. Our model exhibits a 47% improvement in fine scale spatial prediction of NO 2 compared to using the automatic monitors alone, and we use it to predict NO 2 concentrations across West Central Scotland in 2006. [ABSTRACT FROM AUTHOR]

Abstract: The environment of the aircraft cabin greatly influences the comfort and health of passengers and crew members. Contaminant transport has a strong effect on disease spreading in the cabin environment. To obtain the complex cabin contaminant distribution fields accurately and completely, which is also essential to provide solid and precise data for computational fluid dynamics (CFD) model validation, this paper aimed to investigate and improve the method for simultaneous particle and gaseous contaminant fields measurement. The experiment was conducted in a functional MD-82 aircraft. Sulfur hexafluoride (SF6) was used as tracer gas, and Di-Ethyl-Hexyl-Sebacat (DEHS) was used as particulate contaminant. The whole measurement was completed in a part of the economy-class cabin without heating manikins or occupied with heating manikins. The experimental method, in terms of pollutant source setting, sampling points and schedule, was investigated. Statistical analysis showed that appropriately modified sampling grid was able to provide reasonable data. A small difference in the source locations can lead to a significant difference in cabin contaminant fields. And the relationship between gaseous and particulate pollutant transport was also discussed through tracking behavior analysis. [Copyright &y& Elsevier]

Abstract: Land use regression (LUR) models are widely employed in health studies to characterize chronic exposure to air pollution. The LUR is essentially an interpolation technique that employs the pollutant of interest as the dependent variable with proximate land use, traffic, and physical environmental variables used as independent predictors. Two major limitations with this method have not been addressed: (1) variable selection in the model building process, and (2) dealing with unbalanced repeated measures. In this paper, we address these issues with a modeling framework that implements the deletion/substitution/addition (DSA) machine learning algorithm that uses a generalized linear model to average over unbalanced temporal observations. Models were derived for fine particulate matter with aerodynamic diameter of 2.5 microns or less (PM2.5) and nitrogen dioxide (NO2) using monthly observations. We used 4119 observations at 108 sites and 15,301 observations at 138 sites for PM2.5 and NO2, respectively. We derived models with good predictive capacity (cross-validated-R 2 values were 0.65 and 0.71 for PM2.5 and NO2, respectively). By addressing these two shortcomings in current approaches to LUR modeling, we have developed a framework that minimizes arbitrary decisions during the model selection process. We have also demonstrated how to integrate temporally unbalanced data in a theoretically sound manner. These developments could have widespread applicability for future LUR modeling efforts. [Copyright &y& Elsevier]

Abstract: Recent concerns about effects of automobile emissions on the health of people living close to roads have motivated an examination of models to estimate dispersion in the surface boundary layer. During the development of a new line source dispersion model, RLINE (Snyder et al., 2013), analysis of data from a tracer field study led to a re-examination of near-surface dispersion resulting in new formulations for horizontal and vertical plume spread presented in this paper. The equations for vertical spread use the solution of the two-dimensional diffusion equation, in which the eddy diffusivity, based on surface layer similarity, is a function of surface micrometeorological variables such as surface friction velocity and Monin–Obukhov length. The horizontal plume spread equations are based on Eckman's (1994) suggestion that plume spread is governed by horizontal turbulent velocity fluctuations and the vertical variation of the wind speed at mean plume height. Concentration estimates based on the proposed plume spread equations compare well with data from both the Prairie Grass experiment (Barad, 1958) as well as the recently conducted Idaho Falls experiment (Finn et al., 2010). One of the major conclusions of this study is that the plume spreads as well as the wind speed used to estimate concentrations in a dispersion model form a set of coupled variables. [Copyright &y& Elsevier]

Exposure to traffic-related air pollution has been linked to increased risks of cardiopulmonary disease, asthma, and reduced lung function. Ultrafine particles (UFP; aerodynamic diameter < 100 nm), one component of traffic exhaust, may contribute to these risks. This paper describes the development of UFP emission factors, an important input parameter for dispersion models used for exposure assessment. Measurements of particle number concentration (PNC), a proxy for UFP, were performed in the Central Artery Tunnel on Interstate-93 in Boston (MA, USA). The tunnel system consists of two, unidirectional bores, which each carry ∼9 × 104 vehicles per day (diesel vehicles comprise 2–5% of the fleet in the southbound tunnel and 1–3% in the northbound tunnel). A tunnel was chosen for study because it provided an enclosed environment where the effects of lateral and vertical dispersion by ambient air and photochemical reactions would be minimized. Data were collected using a mobile platform equipped with rapid-response instruments for measuring PNC (4–3000 nm) as well as NOx. Because Boston is located in a temperate region (latitude 42° N), we were interested in studying seasonal and diurnal differences in emission factors. To characterize seasonal differences, mobile monitoring was performed on 36 days spaced at 7–14 day intervals over one year (Sept. 2010–Sept. 2011); to characterize diurnal differences intensive mobile monitoring (n = 90 total trips through the tunnels) was performed over the course of two consecutive days in January 2012. All data collected during congested traffic conditions (∼7% of total data set) were removed from the analysis. The median PNC inside the two tunnels for all trips during the 12-month campaign was 3–4-fold higher than on I-93 immediately outside the tunnel and 7–10-fold higher than on I-93 4 km from the tunnel. The median particle number emission factors (EFPN) (±median absolute deviation) for the southbound and northbound tunnels were 5.1 × 1014 (2.3 × 1014) and 1.4 × 1014 (4.2 × 1013) particles vehicle−1 km−1, respectively. EFPN values were ∼2-fold higher in winter and spring (average ambient temperature at the time of monitoring = 6.9 °C) compared to summer and fall (12.9 °C), and ∼2-fold higher in the morning (−7.9 °C) compared to the afternoon/evening (−0.9 °C) on two consecutive winter days. Our results suggest that seasonal and diurnal variations in particulate emissions from highway vehicles may be important to consider in developing EFPN values. [ABSTRACT FROM AUTHOR]

Abstract: An understanding of the complexity of spatial-temporal variations in regional air quality and its respective source contributors is one of the priority research areas due to the adverse effects of air pollution on human health and the environment. In this paper, we integrate air dispersion modeling and Geographic Information System (GIS) based spatial analysis methods to characterize regional ambient air quality at a relatively fine geographical scale (1km×1km) while ascertaining source contributors. The temporal variation analysis shows that sulfur dioxide (SO2) pollution in Dallas County, Texas did not consistently increase or decrease from 1996 to 2002. The lowest and highest mean levels of annual SO2 concentrations at all the receptors (n =2000) were 0.39μgm−3 and 2.32μgm−3 in 2001 and 2002, respectively. Meanwhile, analysis results suggest that the annual SO2 concentrations in a small part of Dallas County slightly declined with the highest value of −1.00μgm−3 over the 1996–2002 period, while most of the county experienced increased SO2 concentration levels from 0.00 to 0.25μgm−3. In addition, the source apportionment analysis demonstrated that the variations in total annual SO2 concentrations in Dallas County from 1996 to 2002 were significantly different from those by source classification. That is, compared to industrial emission sources, on-road vehicle emission sources caused variations in annual SO2 concentrations with relatively larger extents (power of determinant=0.42). However, extreme variations in concentrations were due to industrial emission sources (3.45% vs. 0.00%). Based on these observations, it can be concluded that the combination of air dispersion modeling and GIS-based spatial analysis shows promise to overcome the drawbacks of sparse intraurban air quality monitoring in characterizing the spatial-temporal micro-variations in regional ambient air quality and ascertaining roles of source contributors over long-term periods. [Copyright &y& Elsevier]

Abstract: Semivolatile organic compounds (SVOCs) are a major class of indoor pollutants. Understanding SVOC partitioning between the gas phase and settled dust is important for characterizing the fate of these species indoors and the pathways by which humans are exposed to them. Such knowledge also helps in crafting measurement programs for epidemiological studies designed to probe potential associations between exposure to these compounds and adverse health effects. In this paper, we analyze published data from nineteen studies that cumulatively report measurements of dustborne and airborne SVOCs in more than a thousand buildings, mostly residences, in seven countries. In aggregate, measured median data are reported in these studies for 66 different SVOCs whose octanol-air partition coefficients (K oa) span more than five orders of magnitude. We use these data to test a simple equilibrium model for estimating the partitioning of an SVOC between the gas phase and settled dust indoors. The results demonstrate, in central tendency, that a compound’s octanol-air partition coefficient is a strong predictor of its abundance in settled dust relative to its gas phase concentration. Using median measured results for each SVOC in each study, dustborne mass fractions predicted using K oa and gas-phase concentrations correlate reasonably well with measured dustborne mass fractions (R 2 =0.76). Combined with theoretical understanding of SVOC partitioning kinetics, the empirical evidence also suggests that for SVOCs with high K oa values, the mass fraction in settled dust may not have sufficient time to equilibrate with the gas phase concentration. [ABSTRACT FROM AUTHOR]

Abstract: Valuation of health effects of air pollution is becoming a critical component of the performance of cost–benefit analysis of pollution control measures, which provides a basis for setting priorities for action. Beijing has focused on control of transport emission as vehicular emissions have recently become an important source of air pollution, particularly during Olympic games and Post-games. In this paper, we conducted an estimation of health effects and economic cost caused by road transport-related air pollution using an integrated assessment approach which utilizes air quality model, engineering, epidemiology, and economics. The results show that the total economic cost of health impacts due to air pollution contributed from transport in Beijing during 2004–2008 was 272, 297, 310, 323, 298 million US$ (mean value), respectively. The economic costs of road transport accounted for 0.52, 0.57, 0.60, 0.62, and 0.58% of annual Beijing GDP from 2004 to 2008. Average cost per vehicle and per ton of PM10 emission from road transport can also be estimated as 106 US $/number and 3584 US $ t−1, respectively. These findings illustrate that the impact of road transport contributed particulate air pollution on human health could be substantial in Beijing, whether in physical and economic terms. Therefore, some control measures to reduce transport emissions could lead to considerable economic benefit. [Copyright &y& Elsevier]

Abstract: A study of personal exposures and indoor and outdoor concentrations of particles was carried out in 2000–2001 for 37 health-impaired residents of North Carolina. Earlier papers have dealt with the 24-h integrated gravimetric samples; this report adds the continuous data (1-min resolution) using optical scattering devices (personal data RAMs, or pDRs) for personal and indoor measurements. Subjects and their households were sampled for 7 consecutive days in each of four seasons, although not all subjects completed all four seasons. Each subject completed an activity diary with 15-min resolution on each day giving his or her presence in one of six locations and describing activities with the potential for increased exposure to particles. More than 800 person-days and 1.1×106 person-min of valid personal exposures and indoor air concentrations were collected. The pDRs compared well with the PM2.5 gravimetric devices, with an R 2 of 87% compared to the indoor Harvard impactor (HI), and 70% compared to the personal exposure monitor (PEM). As found in previous studies, (and as expected due to the calibration of these devices with an aerosol of higher density than normal ambient and indoor aerosols), the pDRs overestimated the gravimetric concentrations by 20–50%. Because the correction factor varied by season and by type of sample (indoor vs. personal), no overall correction factor could be applied. The estimated mean increases in exposure during three common activities (cooking, cleaning, and personal care) were 56, 28, and 20μgm−3, respectively. Several less common activities, such as burning food and using a fireplace were found to result occasionally in very high (>1500μgm−3) short-term peaks of exposure. Although households with smoking were ineligible for participation, two households did have significant smoking, and had the highest average personal exposures and indoor concentrations of all the study homes. The diurnal variation of personal exposures and indoor concentrations was bimodal, with peaks occurring between 10 and 11 AM and 5 and 6 PM. The “damping effect” of the home due to air exchange caused the infiltrated outdoor particles to show only a unimodal variation, with a single broad and mild peak occurring between 6 and 10 AM. A method for identifying peak concentrations due to indoor sources and personal activities was developed. Peaks due to personal activities and indoor sources occurred about 23% and 14% of the time, respectively. On average, outdoor particles contributed about half of the total personal exposures and indoor concentrations, but there was wide variation across subjects. These findings using the MIE data were then compared to previously published estimates using measurements of sulfur from the gravimetric data; good agreement (R 2 values from 50% to 65%) was found, providing a measure of validation to both methods. Personal exposures exceeded co-located indoor concentrations by 2–3μgm−3, confirming findings of a small but statistically significant “personal cloud” for PM2.5 made in previous studies using gravimetric 24-h integrating monitors. [Copyright &y& Elsevier]

Abstract: Epidemiological studies typically utilise one or few central monitoring stations as a proxy for personal exposure to particulate matter air pollution. However, recent research indicates that central monitoring sites may not accurately characterize the spatial complexities of the particulate field across an urban area. Consequently, intraurban assessment of exposure to air pollution has become a priority area of study. This paper reviews particulate air pollution exposure studies with a focus on monitored-data studies at the intraurban spatial scale. Portions of the literature provide contradictory conclusions as to the homogeneity of intraurban particulate concentrations, due in part to local conditions such as source composition, meteorology, locations of monitoring sites and topography, but which may also be a result of the methods and definitions used to quantify relative and absolute spatial concentration variations. Comparative analyses of the literature by particle size fraction, method of determining heterogeneity, and sampling characteristics were performed. We find that particular attention should be given to local conditions and methods when using one or few monitoring sites to characterise wider population exposures. The utilisation of absolute and relative measures of homogeneity such as the coefficient of divergence which are based on data from several monitoring sites (e.g., ) in combination with an appropriate sample size (e.g., ) may reduce the possibility of misclassification based on incorrect assumptions about heterogeneity. The errors in exposure misclassification based on these assumptions about intraurban concentration variations are especially critical in long-term cohort epidemiological analyses that assess the effects of exposure variations in air pollution upon health. [Copyright &y& Elsevier]