Leena Kangas

Comparing land use regression and dispersion modelling to assess residential exposure to ambient air pollution for epidemiological studies

BACKGROUND Land-use regression (LUR) and dispersion models (DM) are commonly used for estimating individual air pollution exposure in population studies. Few comparisons have however been made of the performance of these methods. OBJECTIVES Within the European Study of Cohorts for Air Pollution Effects (ESCAPE) we explored the differences between LUR and DM estimates for NO2, PM10 and PM2.5. METHODS The ESCAPE study developed LUR models for outdoor air pollution levels based on a harmonised monitoring campaign. In thirteen ESCAPE study areas we further applied dispersion models. We compared LUR and DM estimates at the residential addresses of participants in 13 cohorts for NO2; 7 for PM10 and 4 for PM2.5. Additionally, we compared the DM estimates with measured concentrations at the 20-40 ESCAPE monitoring sites in each area. RESULTS The median Pearson R (range) correlation coefficients between LUR and DM estimates for the annual average concentrations of NO2, PM10 and PM2.5 were 0.75 (0.19-0.89), 0.39 (0.23-0.66) and 0.29 (0.22-0.81) for 112,971 (13 study areas), 69,591 (7) and 28,519 (4) addresses respectively. The median Pearson R correlation coefficients (range) between DM estimates and ESCAPE measurements were of 0.74 (0.09-0.86) for NO2; 0.58 (0.36-0.88) for PM10 and 0.58 (0.39-0.66) for PM2.5. CONCLUSIONS LUR and dispersion model estimates correlated on average well for NO2 but only moderately for PM10 and PM2.5, with large variability across areas. DM predicted a moderate to large proportion of the measured variation for NO2 but less for PM10 and PM2.5.

Model Calculations of Sulphur and Nitrogen Deposition in Finland

The deposition and origins of sulphur and nitrogen in Finland have been calculated using LRT models developed within the ECE/EMEP project. In the sulphur calculations, new emission data from the Soviet Union were included. The results show that about 260 Gg of S was deposited in 1980 and 210 Gg a−1 in 1987–88. The indigenous fraction decreased from about 50 to 35% of the source- attributable deposition between 1980–88, mainly due to a national reduction in S emissions. There are two maxima in the large-scale S deposition field, one connected with the general south-north gradient, the other being due to the emissions in the Kola Peninsula. Nitrogen deposition was calculated at less than 50 Gg(N) both for NOy and NHz, the highest deposition flux occuring in southeast Finland. The USSR was found to be the major source of the N deposited. A comparison of calculations and observations revealed greater discrepancies at Finnish measurement sites than on the average in Europe, calculated concentrations being systematically lower than observed ones. The reasons underlying this were sought, but no definite answer found.

Application of nitrogen transfer matrices for integrated assessment

Abstract Linear transfer matrices for nitrogen compounds were constructed from the results of the regional air quality model of the Finnish Meteorological Institute (FMI-RM). The matrices are implemented into the integrated assessment model DAIQUIRI used at the Finnish Environment Institute. The deposition fields of oxidised and reduced nitrogen calculated with both models for year 1990 are compared with each other and against measured deposition. Both models give higher depositions than the measurements, and DAIQUIRI often more than FMI-RM. DAIQUIRI currently tends to overestimate nearby deposition of oxidised and reduced nitrogen in areas where land use types other than forest dominate.

Regional nitrogen deposition model for integrated assessment of acidification and eutrophication

Abstract To complement the continent-scale integrated assessment of emission abatement strategies with regional studies, tools with high resolution are needed. For this purpose, the regional nitrogen transport and deposition model DAIQUIRI using linear transfer matrices, i.e. source-receptor relationships, was developed at the Finnish Environment Institute. The model is based on results of a regional dispersion and deposition model of the Finnish Meteorological Institute (FMI-RM). This paper describes the second phase of model development and validation. The transfer matrices constructed for years 1993 and 1995 were used for generating deposition fields, which were then compared with the results of FMI-RM and continent-scale EMEP model, and with measurements. In addition, the model performance in applications was evaluated: the impacts of regional deposition modelling on exceedances of ecosystem critical loads for acidification were calculated and compared with those using the EMEP model. Both regional models gave comparable results, although some differences existed especially for areas where other land-use types than forest dominate. Also the comparison with measurements showed that estimation of deposition for coastal and sea areas is fraught with uncertainties. For reduced nitrogen, the resolution of the models was insufficient to describe the fine-resolution deposition pattern close to emissions. The comparison with results of the EMEP model, however, showed the ability of regional models to represent the spatial deposition pattern in more detail in areas near emissions. The use of regional modelling resulted in larger estimates of areas at risk of acidification than when using only the continent-scale EMEP model.

Sensitivity analysis of the meteorological pre-processor MPP-FMI 3.0 usingalgorithmic differentiation

The meteorological input parameters for urban and local scale dispersion models can be evaluated by pre-processing meteorological observations, using a boundary-layer parametrization model. This study presents a sensitivity analysis of a meteorological pre-processor model (MPPFMI) that utilises readily available meteorological data as input. The sensitivity of the pre-processor to meteorological input was analysed using algorithmic differentiation (AD). The AD tool used was TAPENADE. The AD method numerically evaluates the partial derivatives of functions that are implemented in a computer program. In this study, we focus on the evaluation of vertical fluxes in the atmosphere, and in particular on the sensitivity of the predicted inverse Obukhov length and friction velocity on the model input parameters. The study shows that the estimated inverse Obukhov length and friction velocity are most sensitive to wind speed, and second most sensitive to solar irradiation. The dependency on wind speed is most pronounced at low wind speeds. The presented results have implications for improving the meteorological pre-processing models. AD is shown to be an efficient tool for studying the ranges of sensitivities of the predicted parameters on the model input values quantitatively. A wider use of such advanced sensitivity analysis methods could potentially be very useful in analysing and improving the models used in atmospheric sciences.

Assessment of Population Exposure to Particulate Matter for London and Helsinki

Most epidemiological studies have been conducted based on relations between pollution concentrations measured at fixed ambient air quality monitoring sites, or modelled values using land-use regression models, and various health indicators. However, such simplistic modelling ignores several crucial factors, such as, (i) the activity patterns of individuals, i.e. people’s day-to-day movements, and (ii) the differences between indoor and outdoor air. We have developed a mathematical model for the determination of human exposure to ambient air pollution in an urban area, called EXPAND (EXposure model for Particulate matter And Nitrogen oxiDes). The model combines (i) predicted concentrations, and (ii) information on people’s activities and location of the population, to evaluate the spatial and temporal variation of average exposure of the urban population to ambient air pollution in different microenvironments. In particular, the model takes into account the movements of the population and the infiltration from outdoor to indoor air. We present fine-resolution numerical results on annual spatial concentration, time activity and population exposures to PM2.5 in London and in the Helsinki Metropolitan Area, for 2008 and 2009. We have shown that the effect of neglecting the movements of the population, which is the currently commonly applied procedure, can result in an underprediction of exposure by several tens of per cent.

A Model Evaluation Strategy Applied to Modelling of PM in the Helsinki Metropolitan Area

We have developed a deterministic urban scale dispersion modelling system further by adding a road dust suspension model. The system includes both vehicular exhaust emissions and suspended road dust. The modelling system was combined with a regional scale chemical transport model for calculations of concentrations in an urban area for the year 2008, and for the year 2010 measured regional background concentration was used. The time series’ were modelled for a spatial area more extensive than before using the FORE road dust suspension model. The predictions were compared against observed concentrations of PM2.5 and PM10. The use of the index of determination (r2) is discussed. We criticize the use of r2 alone as well as in addition to an index of agreement—type measure of agreement, and review the underlying data assumptions for the use of both measures. We then suggest a strategy to develop model evaluation statistical understanding, practice and nomenclature.

The Sensitivity of the Predictions of a Roadside Dispersion Model to Meteorological Variables: Evaluation Using Algorithmic Differentiation

Dispersion and transformation of air pollution originated from a network of vehicular sources can be evaluated using the CAR-FMI model, combined with a meteorological pre-processor, MPP-FMI. The aim of this study is to analyse the sensitivities of both the meteorological pre-processor and the roadside dispersion model to the variations of model input values, taking especially into account the meteorological variables. Comprehensive and systematic analyses of the sensitivities of atmospheric dispersion models have been scarce in the literature. Such sensitivity analyses can be used in the refinement of both categories of models. The sensitivity analyses have been performed using an algorithmic differentiation (AD) tool called TAPENADE. We present selected illustrative results on the sensitivities of the meteorological pre-processing model MPP-FMI and the roadside dispersion model CAR-FMI on the model input variables. However, the AD method in general could also be applied for analysing the sensitivities of any other atmospheric modelling system.

Validation of PM2.5 Concentrations Based on Finnish Emission—Source-Receptor Scenario Model

Atmospheric fine particulate matter (PM2.5) is a major health risk in both developing and developed countries. Health impact assessments utilize often air quality models, consisting of emission and atmospheric dispersion and meteorological models. For policy purposes, there is often a need to assess the air quality impact of large number of alternative emission reduction measures. For such assessments at high spatial resolution for regional scale domains, e.g. the area of a whole country, simplified linear source-receptor relationships can be used to substitute more laborious atmospheric models. In this study we compared PM2.5 concentrations calculated with our policy analysis emission model with available measurement data. The PM2.5 concentrations were modelled using the Finnish Regional Emission Scenario (FRES) model coupled with source-receptor matrices at various resolutions. The measurement data for comparisons were taken from several monitoring stations across Finland, and represented different site types i.e. rural and urban background and traffic dominated environments. In general the model overestimated the PM2.5 concentrations in urban locations and underestimated in rural stations. One possible reason for the overestimation is that emissions from some sectors may have inaccurate spatial disaggregation. Especially the use of population density as a spatial proxy for the distribution of emissions often poorly represents the polluting activity and results in too high modelled concentrations in densely populated areas. In rural regions the omission of sea traffic emissions and natural sources might explain some of the underestimation. The results highlight the importance of the quality of the emission data used as input in dispersion modelling and the need for reliable spatial representation of emissions in the model.

Intake Fraction for Benzene Traffic Emissions in Helsinki

Benzene is well known for its toxicity (haema and genotoxicity) and the carcinogenic effect associated with long time exposure, mainly by inhalation. In urban environment traffic is an important source for ambient air benzene (Bz) concentrations. In order to quantify emission-to-intake relationships independently of intake or emission units, the concept intake fraction became relevant.