Using a chemistry transport model to account for the spatial variability of exposure concentrations in epidemiologic air pollution studies

Environmental epidemiology and more specifically time-series analysis have traditionally used area-averaged pollutant concentrations measured at central monitors as exposure surrogates to associate health outcomes with air pollution. However, spatial aggregation has been shown to contribute to the overall bias in the estimation of the exposure-response functions. This paper presents the benefit of adding features of the spatial variability of exposure by using concentration fields modeled with a chemistry transport model instead of monitor data and accounting for human activity patterns. On the basis of county-level census data for the city of Paris, France, and a Monte Carlo simulation, a simple activity model was developed accounting for the temporal variability between working and evening hours as well as during transit. By combining activity data with modeled concentrations, the downtown, suburban, and rural spatial patterns in exposure to nitrogen dioxide, ozone, and PM2.5 (particulate matter [PM] < or = 10 microm in aerodynamic diameter) were captured and parametrized. Exposures predicted with this model were used in a time-series study of the short-term effect of air pollution on total nonaccidental mortality for the 4-yr period from 2001 to 2004. It was shown that the time series of the exposure surrogates developed here are less correlated across co-pollutants than in the case of the area-averaged monitor data. This led to less biased exposure-response functions when all three co-pollutants were inserted simultaneously in the same regression model. This finding yields insight into pollutant-specific health effects that are otherwise masked by the high correlation among co-pollutants.     

Comparison of the performances of land use regression modelling and dispersion modelling in estimating small-scale variations in long-term air pollution concentrations in a Dutch urban area

The performance of a Land Use Regression (LUR) model and a dispersion model (URBIS – URBis Information System) was compared in a Dutch urban area. For the Rijnmond area, i.e. Rotterdam and surroundings, nitrogen dioxide (NO₂) concentrations for 2001 were estimated for nearly 70 000 centroids of a regular grid of 100 × 100 m.A LUR model based upon measurements carried out on 44 sites from the Dutch national monitoring network and upon Geographic Information System (GIS) predictor variables including traffic intensity, industry, population and residential land use was developed. Interpolation of regional background concentration measurements was used to obtain the regional background. The URBIS system was used to estimate NO₂ concentrations using dispersion modelling. URBIS includes the CAR model (Calculation of Air pollution from Road traffic) to calculate concentrations of air pollutants near urban roads and Gaussian plume models to calculate air pollution levels near motorways and industrial sources. Background concentrations were accounted for using 1 × 1 km maps derived from monitoring and model calculations.Moderate agreement was found between the URBIS and LUR in calculating NO₂ concentrations (R = 0.55). The predictions agreed well for the central part of the concentration distribution but differed substantially for the highest and lowest concentrations. The URBIS dispersion model performed better than the LUR model (R = 0.77 versus R = 0.47 respectively) in the comparison between measured and calculated concentrations on 18 validation sites. Differences can be understood because of the use of different regional background concentrations, inclusion of rather coarse land use category industry as a predictor variable in the LUR model and different treatment of conversion of NO to NO₂.Moderate agreement was found between a dispersion model and a land use regression model in calculating annual average NO₂ concentrations in an area with multiple sources. The dispersion model explained concentrations at validation sites better.     

A land use regression model for predicting ambient volatile organic compound concentrations in Toronto,Canada

More than 25 studies have employed land use regression (LUR) models to estimate nitrogen oxides and to a lesser extent particulate matter indicators, but these methods have been less commonly applied to ambient concentrations of volatile organic compounds (VOCs). Some VOCs have high plausibility as sources of health effects and others are specific indicators of motor vehicle exhaust. We used LUR models to estimate spatial variability of VOCs in Toronto, Canada. Benzene, n-hexane and total hydrocarbons (THC) were measured from July 25 to August 9, 2006 at 50 locations using the TraceAir organic vapor monitors. Nitrogen dioxide (NO₂) was also sampled to assess its spatial pattern agreement with VOC exposures. Buffers for land use, population density, traffic density, physical geography, and remote sensing measures of greenness and surface brightness were also tested. The remote sensing measures have the highest correlations with VOCs and NO₂ levels (i.e., explains >36% of the variance). Our regression models explain 66–68% of the variance in the spatial distribution of VOCs, compared to 81% for the NO₂ model. The ranks of agreement between various VOCs range from 48 to 63% and increases substantially – up to 75% – for the top and bottom quartile groups. Agreements between NO₂ and VOCs are much smaller with an average rank of 36%. Future epidemiologic studies may therefore benefit from using VOCs as potential toxic agents for traffic-related pollutants.     

Evaluation of land-use regression models used to predict air quality concentrations in an urban area

Cohort studies designed to estimate human health effects of exposures to urban pollutants require accurate determination of ambient concentrations in order to minimize exposure misclassification errors. However, it is often difficult to collect concentration information at each study subject location. In the absence of complete subject-specific measurements, land-use regression (LUR) models have frequently been used for estimating individual levels of exposures to ambient air pollution. The LUR models, however, have several limitations mainly dealing with extensive monitoring data needs and challenges involved in their broader applicability to other locations. In contrast, air quality models can provide high-resolution source–concentration linkages for multiple pollutants, but require detailed emissions and meteorological information. In this study, first we predicted air quality concentrations of PM_2.5, NO_x, and benzene in New Haven, CT using hybrid modeling techniques based on CMAQ and AERMOD model results. Next, we used these values as pseudo-observations to develop and evaluate the different LUR models built using alternative numbers of (training) sites (ranging from 25 to 285 locations out of the total 318 receptors). We then evaluated the fitted LUR models using various approaches, including: 1) internal “Leave-One-Out-Cross-Validation” (LOOCV) procedure within the “training” sites selected; and 2) “Hold-Out” evaluation procedure, where we set aside 33–293 tests sites as independent datasets for external model evaluation. LUR models appeared to perform well in the training datasets. However, when these LUR models were tested against independent hold out (test) datasets, their performance diminished considerably. Our results confirm the challenges facing the LUR community in attempting to fit empirical response surfaces to spatially- and temporally-varying pollution levels using LUR techniques that are site dependent. These results also illustrate the potential benefits of enhancing basic LUR models by utilizing air quality modeling tools or concepts in order to improve their reliability or transferability.     

A land use regression model for predicting ambient concentrations of nitrogen dioxide in Hamilton, Ontario, Canada

This paper reports on the development of a land use regression (LUR) model for predicting the intraurban variation of traffic-related air pollution in Hamilton, Ontario, Canada, an industrial city at the western end of Lake Ontario. Although land use regression has been increasingly used to characterize exposure gradients within cities, research to date has yet to test whether this method can produce reliable estimates in an industrialized location. Ambient concentrations of nitrogen dioxide (NO2) were measured for a 2-week period in October 2002 at > 100 locations across the city and subsequently at 30 of these locations in May 2004 to assess seasonal effects. Predictor variables were derived for land use types, transportation, demography, and physical geography using geographic information systems. The LUR model explained 76% of the variation in NO2. Traffic density, proximity to a highway, and industrial land use were all positively correlated with NO2 concentrations, whereas open land use and distance from the lake were negatively correlated with NO2. Locations downwind of a major highway resulted in higher NO2 levels. Cross-validation of the results confirmed model stability over different seasons. Our findings demonstrate that land use regression can effectively predict NO2 variation at the intraurban scale in an industrial setting. Models predicting exposure within smaller areas may lead to improved detection of health effects in epidemiologic studies.     

Personal exposure of children to air pollution

Changes over recent decades in outdoor concentrations of air pollutants are well documented. However, the impacts of air pollution on an individual's health actually relate not to these outdoor concentrations but to their personal exposure in the different locations in which they spend time. Assessing how personal exposures differ from outdoor concentrations, and how they have changed over recent decades, is challenging. This review focuses on the exposure of children, since they are a particularly sensitive group. Much of children's time is spent indoors, and childhood exposure is closely related to concentrations in the home, at school, and in transport. For this reason, children's personal exposures to air pollutants differ significantly from both those of adults and from outdoor concentrations. They depend on a range of factors, including urbanisation, energy use, building design, travel patterns, and activity profiles; analysis of these factors can identify a wider range of policy measures to reduce children's exposure than direct emission control. There is a very large variation in personal exposure between individual children, caused by differences in building design, indoor and outdoor sources, and activity patterns. Identifying groups of children with high personal exposure, and their underlying causes, is particularly important in regions of the world where emissions are increasing, but there are limited resources for environmental and health protection. Although the science of personal exposure assessment, with the associated measurement and modelling techniques, has developed to maturity in North America and western Europe over the last 50 years, there is an urgent need to apply this science in other parts of the world where the effects of air pollution are now much more serious.     

A land use regression for predicting fine particulate matter concentrations in the New York City region

We developed regression equations to predict fine particulate matter (PM_2.5) at air monitoring locations in the New York City region using data on nearby traffic and land use patterns. Three-year averages (1999–2001) of PM_2.5 at US Environmental Protection Agency (EPA) monitors in the 28 counties including and surrounding New York City were calculated using daily data from the EPA's Air Quality Subsystem. As the secondary contribution to PM_2.5 concentrations is lowest in the winter, we also calculated and modeled average winter 2000 PM_2.5 to conduct a preliminary evaluation of model sensitivity to source contribution. Candidate predictor variables included traffic, land use, census and emissions data from local, state and national sources and were tabulated for a series of circular buffer regions at varying distances around the monitors using a geographic information system. In total, more than 25 variables at 5 different buffer distances were considered for inclusion in the model. Before evaluating the variables we removed several samples from the modeling for validation. For comparison and validation purposes we computed both a model using data for the full 28-county region as well as a more urbanized 9-county region. We found that traffic within a buffer of 300 or 500 m explains the greatest proportion of variance (37–44%) in all 3 models. Measures of urbanization, specifically population density, explain a significant amount of the residual variation (7–18%) after including a traffic variable. Finally, a measure of industrial land use further improves the 28-county and 9-county models based on the 3-yr annual averages, explaining an additional 4% and 11% of the variation, respectively, while vegetative land use improves the winter model explaining an additional 6%. The final models predicted well at validation locations. In total, the final land use regression models explain between 61% and 64% of the variation in PM_2.5.     

Estimated long-term outdoor air pollution concentrations in a cohort study

Several recent studies associated long-term exposure to air pollution with increased mortality. An ongoing cohort study, the Netherlands Cohort Study on Diet and Cancer (NLCS), was used to study the association between long-term exposure to traffic-related air pollution and mortality. Following on a previous exposure assessment study in the NLCS, we improved the exposure assessment methods.Long-term exposure to nitrogen dioxide (NO₂), nitrogen oxide (NO), black smoke (BS), and sulphur dioxide (SO₂) was estimated. Exposure at each home address (N=21 868) was considered as a function of a regional, an urban and a local component. The regional component was estimated using inverse distance weighed interpolation of measurement data from regional background sites in a national monitoring network. Regression models with urban concentrations as dependent variables, and number of inhabitants in different buffers and land use variables, derived with a Geographic Information System (GIS), as predictor variables were used to estimate the urban component. The local component was assessed using a GIS and a digital road network with linked traffic intensities. Traffic intensity on the nearest road and on the nearest major road, and the sum of traffic intensity in a buffer of 100 m around each home address were assessed. Further, a quantitative estimate of the local component was estimated.The regression models to estimate the urban component explained 67%, 46%, 49% and 35% of the variances of NO₂, NO, BS, and SO₂ concentrations, respectively. Overall regression models which incorporated the regional, urban and local component explained 84%, 44%, 59% and 56% of the variability in concentrations for NO₂, NO, BS and SO₂, respectively.We were able to develop an exposure assessment model using GIS methods and traffic intensities that explained a large part of the variations in outdoor air pollution concentrations.     

Human health risks in megacities due to air pollution

This study evaluates the health risks in megacities in terms of mortality and morbidity due to air pollution. A new spreadsheet model, Risk of Mortality/Morbidity due to Air Pollution (Ri-MAP), is used to estimate the excess numbers of deaths and illnesses. By adopting the World Health Organization (WHO) guideline concentrations for the air pollutants SO₂, NO₂ and total suspended particles (TSP), concentration-response relationships and a population attributable-risk proportion concept are employed. Results suggest that some megacities like Los Angeles, New York, Osaka Kobe, Sao Paulo and Tokyo have very low excess cases in total mortality from these pollutants. In contrast, the approximate numbers of cases is highest in Karachi (15,000/yr) characterized by a very high concentration of total TSP (～670 μg m^?3). Dhaka (7000/yr), Beijing (5500/yr), Karachi (5200/yr), Cairo (5000/yr) and Delhi (3500/yr) rank highest with cardiovascular mortality. The morbidity (hospital admissions) due to Chronic Obstructive Pulmonary Disease (COPD) follows the tendency of cardiovascular mortality. Dhaka and Karachi lead the rankings, having about 2100/yr excess cases, while Osaka-Kobe (～20/yr) and Sao Paulo (～50/yr) are at the low end of all megacities considered. Since air pollution is increasing in many megacities, and our database of measured pollutants is limited to the period up to 2000 and does not include all relevant components (e.g. O₃), these numbers should be interpreted as lower limits. South Asian megacities most urgently need improvement of air quality to prevent excess mortality and morbidity due to exceptionally high levels of air pollution. The risk estimates obtained from Ri-MAP present a realistic baseline evaluation for the consequences of ambient air pollution in comparison to simple air quality indices, and can be expanded and improved in parallel with the development of air pollution monitoring networks.     

Effect of land use changes on non-carcinogenic health risks due to nitrate exposure to drinking groundwater

Environmental Science and Pollution Research - This study aimed to determine the effect of land-use changes on the non-carcinogenic health risk of nitrate ion exposure of underground drinking water...     

Coupling GEOS-CHEM with a regional air pollution model for Greece

The sensitivity of regional air quality modeling simulations to boundary conditions over Greece is investigated, for various synoptic conditions. For this purpose, a global to mesoscale model-chain is developed and applied, coupling the individual models' simulations. The global chemical transport model GEOS-CHEM, applied in a one-way nested procedure, is used to drive the regional UAM-V chemical dispersion model with time-varying lateral and top boundary conditions. The results of the coupling procedure are compared with the MINOS campaign measurements at Finokalia (Southern Greece) during the period from 1 to 16 August 2001 which is mainly characterized by an interchange of two synoptic types, High-Low and Long Wave trough.The comparison between the simulation results and the measurements reveals that the coupling procedure captures satisfactorily the range of observed CO concentrations at the southern part of Greece. The most severe deviations are observed under strongly variable atmospheric circulation, when no distinct synoptic circulation is allowed to be established in the area. Regarding O₃, the highest, though underestimated, surface concentrations are simulated under Long Wave trough conditions due to the influence of the ozone inflow predicted by GEOS-CHEM at the western boundary of the innermost domain and/or under enhanced NOy emissions arriving at Finokalia from urban and ships plumes.     

A method for mapping the air pollution in cities with the combined use of measured and calculated concentrations

The aim of this paper is to develop the technique for mapping the air pollution in cities using measured and computed concentrations. It is based on the combined use of deterministic and stochastic modelling. Two different approaches are applied to extrapolate the measurements to the regular grid points covering the city. The first uses the method of the principal components, and the second is based on optimal interpolation or 'kriging' of meteorological fields. The results of the application of this model for the mapping of air pollution in the city of Pskov, Russia, are given.     

Using a photochemical air quality model to evaluate emissions and simulate concentrations

The air-quality modelling system was conducted to evaluate emissions inventory and simulate air concentration over Thailand. The coupling model of the Regional Atmospheric Modelling System (RAMS) and the models-3 Community Multi-scale Air Quality (CMAQ) was applied to simulate the concentration distributions of gaseous pollutants (i.e., NO_x, SO₂ and CO) over the Central and Eastern regions of Thailand. CMAQ was run for a summertime episode in a sub-grid scale. Simulated air concentrations were then compared with monitoring data. The evaluating results between modelling simulation and monitoring observation show a good agreement within a factor of 2.0 and relevant trend line, representing the acceptable level of emissions and concentration. This coupling model can be applied to support emission control strategies and clean air acts.     

The impact of the congestion charging scheme on ambient air pollution concentrations in London

On 17th February 2003, a congestion charging scheme (CCS), operating Monday–Friday, 07:00–18:00, was introduced in central London along with a programme of traffic management measures. We investigated the potential impact of the introduction of the CCS on measured pollutant concentrations (oxides of nitrogen (NO_X, NO and NO₂), particles with a median diameter less than 10 microns (PM₁₀), carbon monoxide (CO) and ozone (O₃)) measured at roadside and background monitoring sites across Greater London. Temporal changes in pollution concentrations within the congestion charging zone were compared to changes, over the same time period, at monitors unlikely to be affected by the CCS (the control zone) and in the boundary zone between the two. Similar analyses were done for CCS hours during weekends (when the CCS was not operating).Based on the single roadside monitor with the CCS Zone, it was not possible to identify any relative changes in pollution concentrations associated with the introduction of the scheme. However, using background monitors, there was good evidence for a decrease in NO and increases in NO₂ and O₃ relative to the control zone. There was little change in background concentrations of NO_X. There was also evidence of relative reductions in PM₁₀ and CO. Similar changes were observed during the same hours in weekends when the scheme was not operating.The causal attribution of these changes to the CCS per se is not appropriate since the scheme was introduced concurrently with other traffic and emissions interventions which might have had a more concentrated effect in central London. This study provides important pointers for study design and data requirements for the evaluation of similar schemes in terms of air quality. It also shows that results may be unexpected and that the overall effect on toxicity may not be entirely favourable.     

Analysis of air pollution data at a mixed source location using boosted regression trees

This paper explores the use of boosted regression trees to draw inferences concerning the source characteristics at a location of high source complexity. Models are developed for hourly concentrations of nitrogen oxides (NO_X) close to a large international airport. Model development is discussed and methods to quantify model uncertainties developed. It is shown that good explanatory models can be developed and further, allowing for interactions between model variables significantly improves the model fits compared with non-interacting models. Methods are used to determine which variables exert most influence over predicted concentrations and to explore the NO_X dependency for each. Model predictions are used to estimate aircraft take-off contributions to total concentrations of NO_X and determine how these predictions are affected by annual variations in meteorological conditions and runway use patterns. Furthermore, the results relating to the aircraft contributions to total NO_X concentration are compared with those from a more detailed independent field campaign. Finally, we find empirical evidence that plumes from larger aircraft disperse more rapidly from the point of release compared with smaller aircraft. The reasons for this behaviour and the implications are discussed.     

use of 1970 census of population data for air pollution research

Today, many more communities in the United States are in a better position to undertake the study of the possible health effects of the air pollution in their environment than has been possible before. Further, such study may furnish baseline data for evaluating the efficacy of air pollution control activity in those cities.

Assessment by city and county control officials can be accomplished by examining the relationship between the demographic characteristics of the area as reported in the 1970 Census of Population, the aerometric measurements now being made routinely in many areas, and mortality in the population as reported to the state office of vital statistics. From the census, detailed demographic or population data will be available by census tract in urban areas. These areas include virtually every U. S. city with a population of 50,000 or more.

The procedures used in the Buffalo and Nashville Air Pollution Studies are discussed with a view toward possible replication by other geographic areas. The need to take account of differences in socio-economic status is emphasized, as is the desirability of obtaining smoking histories. Another major type of air pollution research which is again made possible by the 1970 Census is the comparison of mortality experience, on an agespecific and an age-adjusted basis centered around 1970, for the more than 200 Standard Metropolitan Statistical Areas in the United States.     

An interprovincial cooperative game model for air pollution control in China

The noncooperative air pollution reduction model (NCRM) that is currently adopted in China to manage air pollution reduction of each individual province has inherent drawbacks. In this paper, we propose a cooperative air pollution reduction game model (CRM) that consists of two parts: (1) an optimization model that calculates the optimal pollution reduction quantity for each participating province to meet the joint pollution reduction goal; and (2) a model that distribute the economic benefit of the cooperation (i.e., pollution reduction cost saving) among the provinces in the cooperation based on the Shapley value method. We applied the CRM to the case of SO₂ reduction in the Beijing–Tianjin–Hebei region in China. The results, based on the data from 2003–2009, show that cooperation helps lower the overall SO₂ pollution reduction cost from 4.58% to 11.29%. Distributed across the participating provinces, such a cost saving from interprovincial cooperation brings significant benefits to each local government and stimulates them for further cooperation in pollution reduction. Finally, sensitivity analysis is performed using the year 2009 data to test the parameters’ effects on the pollution reduction cost savings.

Implications: China is increasingly facing unprecedented pressure for immediate air pollution control. The current air pollution reduction policy does not allow cooperation and is less efficient. In this paper we developed a cooperative air pollution reduction game model that consists of two parts: (1) an optimization model that calculates the optimal pollution reduction quantity for each participating province to meet the joint pollution reduction goal; and (2) a model that distributes the cooperation gains (i.e., cost reduction) among the provinces in the cooperation based on the Shapley value method. The empirical case shows that such a model can help improve efficiency in air pollution reduction. The result of the model can serve as a reference for Chinese government pollution reduction policy design.