Can dispersion modeling of air pollution be improved by land-use regression? An example from Stockholm, Sweden

Korek, Michal; Johansson, Christer; Svensson, Nina; Lind, Tomas; Beelen, Rob; Hoek, Gerard; Pershagen, Göran; Bellander, Tom

doi:10.1038/jes.2016.40

Cited by 34 publications

(13 citation statements)

References 36 publications

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“…Yet, with a very limited number of comparison studies, the extent to which estimates of air pollution effects are affected by the choice of exposure model remains unclear. A combination of the LUR and dispersion models may further improve the exposure assessment estimates, possibly accounting for some of the imperfections in the emission databases [97]. …”

Section: Discussionmentioning

confidence: 99%

Traffic-Related Air Pollution and Childhood Asthma: Recent Advances and Remaining Gaps in the Exposure Assessment Methods

Khreis

Nieuwenhuijsen

2017

IJERPH

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Background: Current levels of traffic-related air pollution (TRAP) are associated with the development of childhood asthma, although some inconsistencies and heterogeneity remain. An important part of the uncertainty in studies of TRAP-associated asthma originates from uncertainties in the TRAP exposure assessment and assignment methods. In this work, we aim to systematically review the exposure assessment methods used in the epidemiology of TRAP and childhood asthma, highlight recent advances, remaining research gaps and make suggestions for further research. Methods: We systematically reviewed epidemiological studies published up until 8 September 2016 and available in Embase, Ovid MEDLINE (R), and “Transport database”. We included studies which examined the association between children’s exposure to TRAP metrics and their risk of “asthma” incidence or lifetime prevalence, from birth to the age of 18 years old. Results: We found 42 studies which examined the associations between TRAP and subsequent childhood asthma incidence or lifetime prevalence, published since 1999. Land-use regression modelling was the most commonly used method and nitrogen dioxide (NO2) was the most commonly used pollutant in the exposure assessments. Most studies estimated TRAP exposure at the residential address and only a few considered the participants’ mobility. TRAP exposure was mostly assessed at the birth year and only a few studies considered different and/or multiple exposure time windows. We recommend that further work is needed including e.g., the use of new exposure metrics such as the composition of particulate matter, oxidative potential and ultra-fine particles, improved modelling e.g., by combining different exposure assessment models, including mobility of the participants, and systematically investigating different exposure time windows. Conclusions: Although our previous meta-analysis found statistically significant associations for various TRAP exposures and subsequent childhood asthma, further refinement of the exposure assessment may improve the risk estimates, and shed light on critical exposure time windows, putative agents, underlying mechanisms and drivers of heterogeneity.

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Section: Discussionmentioning

confidence: 99%

Traffic-Related Air Pollution and Childhood Asthma: Recent Advances and Remaining Gaps in the Exposure Assessment Methods

Khreis

Nieuwenhuijsen

2017

IJERPH

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“…The rate of decrease is specified by an exponential power of the distance. Most commonly used exponent is 2 [72][73][74][75]. de Mesnard [76] mathematically derived that the exponent should be chosen based on the atmospheric condition; it should be close to three for unstable atmospheric conditions, approximately two for moderately unstable conditions and between one and two for neutral conditions.…”

Section: Land Use Regression (Lur) Modelmentioning

confidence: 99%

Impact of Urban Growth on Air Quality in Indian Cities Using Hierarchical Bayesian Approach

2019

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Several studies have found rising ambient particulate matter (PM 2.5 ) concentrations in urban areas across developing countries. For setting mitigation policies source-contribution is needed, which is calculated mostly through computationally intensive chemical transport models or manpower intensive source apportionment studies. Data based approach that use remote sensing datasets can help reduce this challenge, specially in developing countries which lack spatially and temporally dense air quality monitoring networks. Our objective was identifying relative contribution of urban emission sources to monthly PM 2.5 ambient concentrations and assessing whether urban expansion can explain rise of PM 2.5 ambient concentration from 2001 to 2015 in 15 Indian cities. We adapted the Intergovernmental Panel on Climate Change’s (IPCC) emission framework in a land use regression (LUR) model to estimate concentrations by statistically modeling the impact of urban growth on aerosol concentrations with the help of remote sensing datasets. Contribution to concentration from six key sources (residential, industrial, commercial, crop fires, brick kiln and vehicles) was estimated by inverse distance weighting of their emissions in the land-use regression model. A hierarchical Bayesian approach was used to account for the random effects due to the heterogeneous emitting sources in the 15 cities. Long-term ambient PM 2.5 concentration from 2001 to 2015, was represented by a indicator R (varying from 0 to 100), decomposed from MODIS (Moderate Resolution Imaging Spectroradiometer) derived AOD (aerosol optical depth) and angstrom exponent datasets. The model was trained on annual-level spatial land-use distribution and technological advancement data and the monthly-level emission activity of 2001 and 2011 over each location to predict monthly R. The results suggest that above the central portion of a city, concentration due to primary PM 2.5 emission is contributed mostly by residential areas (35.0 ± 11.9%), brick kilns (11.7 ± 5.2%) and industries (4.2 ± 2.8%). The model performed moderately for most cities (median correlation for out of time validation was 0.52), especially when assumed changes in seasonal emissions for each source reflected actual seasonal changes in emissions. The results suggest the need for policies focusing on emissions from residential regions and brick kilns. The relative order of the contributions estimated by this study is consistent with other recent studies and a contribution of up to 42.8 ± 14.1% is attributed to the formation of secondary aerosol, long-range transport and unaccounted sources in surrounding regions. The strength of this approach is to be able to estimate the contribution of urban growth to primary aerosols statistically with a relatively low computation cost compared to the more accurate but computationally expensive chemical transport based models. This remote sensing based approach is especially useful in locations without emission inventory.

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“…Our exposure measure is only a proxy for actual vehicular traffic-related air pollution exposure during childhood and is subject to exposure misclassification. It does not account for meteorological dispersion and the geophysical features, each of which can affect air pollutant concentrations (Korek et al, 2016). However, modeled estimates may also be subject to misclassification and be limited by the availability and quality of historical monitored estimates.…”

Section: Discussionmentioning

confidence: 99%

Residential exposure to vehicular traffic-related air pollution during childhood and breast cancer risk

Shmuel

White

Sandler

2017

Environmental Research

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Background Some studies have supported an association between traffic-related air pollution exposure and breast cancer risk. However, few studies have considered exposures in early life, which may be a period of increased susceptibility. Objectives To examine the association of childhood residential exposure to traffic-related air pollution with breast cancer development. Methods The Sister Study is a prospective cohort of 50,884 initially breast cancer-free women, of whom 42,934 provided information at enrollment about roads and traffic near their primary childhood residence before age 14 as well as relevant covariates. Adjusted hazard ratios (aHRs) and 95% confidence intervals (CIs) for the association between traffic-related measures at childhood residence and adult incident breast cancer were estimated using Cox regression. Results During follow-up (mean=6.3 years), 2,028 breast cancers were diagnosed. Traffic-related characteristics were not consistently associated with breast cancer risk. However, incidence was elevated among women who reported a median/barrier dividing either their primary childhood residential road (aHR=1.2; 95% CI: 0.9–1.7) or the nearest cross-street (aHR=1.3; 95% CI: 0.9–1.8, if the cross-street was within 100 ft.), and among women whose nearest cross-street had the highest traffic, ≥3 lanes, and/or a median/barrier (aHR= 1.4; 95% CI: 1.0–1.9). Conclusions Measures of potential exposure to vehicular traffic were not consistently associated with breast cancer risk. However, living during childhood on or near a road with a median or other barrier, which may be a more easily remembered road characteristic than the others assessed, was associated with increased breast cancer risk.

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Can dispersion modeling of air pollution be improved by land-use regression? An example from Stockholm, Sweden

Cited by 34 publications

References 36 publications

Traffic-Related Air Pollution and Childhood Asthma: Recent Advances and Remaining Gaps in the Exposure Assessment Methods

Traffic-Related Air Pollution and Childhood Asthma: Recent Advances and Remaining Gaps in the Exposure Assessment Methods

Impact of Urban Growth on Air Quality in Indian Cities Using Hierarchical Bayesian Approach

Residential exposure to vehicular traffic-related air pollution during childhood and breast cancer risk

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