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  1. Latif MT, Dominick D, Ahamad F, Khan MF, Juneng L, Hamzah FM, et al.
    Sci Total Environ, 2014 Jun 1;482-483:336-48.
    PMID: 24662202 DOI: 10.1016/j.scitotenv.2014.02.132
    Rural background stations provide insight into seasonal variations in pollutant concentrations and allow for comparisons to be made with stations closer to anthropogenic emissions. In Malaysia, the designated background station is located in Jerantut, Pahang. A fifteen-year data set focusing on ten major air pollutants and four meteorological variables from this station were analysed. Diurnal, monthly and yearly pollutant concentrations were derived from hourly continuous monitoring data. Statistical methods employed included principal component regression (PCR) and sensitivity analysis. Although only one of the yearly concentrations of the pollutants studied exceeded national and World Health Organisation (WHO) guideline standards, namely PM10, seven of the pollutants (NO, NO2, NOx, O3, PM10, THC and CH4) showed a positive upward trend over the 15-year period. High concentrations of PM10 were recorded during severe haze episodes in this region. Whilst, monthly concentrations of most air pollutants, such as: PM10, O3, NOx, NO2, CO and NmHC were recorded at higher concentrations between June and September, during the southwest monsoon. Such results correspond with the mid-range transport of pollutants from more urbanised and industrial areas. Diurnal patterns, rationed between major air pollutants and sensitivity analysis, indicate the influence of local traffic emissions on air quality at the Jerantut background station. Although the pollutant concentrations have not shown a rapid increase, an alternative background station will need to be assigned within the next decade if development projects in the surrounding area are not halted.
    Matched MeSH terms: Air Pollution/statistics & numerical data*
  2. Soyiri IN, Reidpath DD, Sarran C
    Chron Respir Dis, 2013 May;10(2):85-94.
    PMID: 23620439 DOI: 10.1177/1479972313482847
    Health forecasting can improve health service provision and individual patient outcomes. Environmental factors are known to impact chronic respiratory conditions such as asthma, but little is known about the extent to which these factors can be used for forecasting. Using weather, air quality and hospital asthma admissions, in London (2005-2006), two related negative binomial models were developed and compared with a naive seasonal model. In the first approach, predictive forecasting models were fitted with 7-day averages of each potential predictor, and then a subsequent multivariable model is constructed. In the second strategy, an exhaustive search of the best fitting models between possible combinations of lags (0-14 days) of all the environmental effects on asthma admission was conducted. Three models were considered: a base model (seasonal effects), contrasted with a 7-day average model and a selected lags model (weather and air quality effects). Season is the best predictor of asthma admissions. The 7-day average and seasonal models were trivial to implement. The selected lags model was computationally intensive, but of no real value over much more easily implemented models. Seasonal factors can predict daily hospital asthma admissions in London, and there is a little evidence that additional weather and air quality information would add to forecast accuracy.
    Matched MeSH terms: Air Pollution/statistics & numerical data*
  3. Vadrevu KP, Lasko K, Giglio L, Justice C
    Environ Pollut, 2014 Dec;195:245-56.
    PMID: 25087199 DOI: 10.1016/j.envpol.2014.06.017
    In this study, we assess the intense pollution episode of June 2013, in Riau province, Indonesia from land clearing. We relied on satellite retrievals of aerosols and Carbon monoxide (CO) due to lack of ground measurements. We used both the yearly and daily data for aerosol optical depth (AOD), fine mode fraction (FMF), aerosol absorption optical depth (AAOD) and UV aerosol index (UVAI) for characterizing variations. We found significant enhancement in aerosols and CO during the pollution episode. Compared to mean (2008-2012) June AOD of 0.40, FMF-0.39, AAOD-0.45, UVAI-1.77 and CO of 200 ppbv, June 2013 values reached 0.8, 0.573, 0.672, 1.77 and 978 ppbv respectively. Correlations of fire counts with AAOD and UVAI were stronger compared to AOD and FMF. Results from a trajectory model suggested transport of air masses from Indonesia towards Malaysia, Singapore and southern Thailand. Our results highlight satellite-based mapping and monitoring of pollution episodes in Southeast Asia.
    Matched MeSH terms: Air Pollution/statistics & numerical data*
  4. Wahid NB, Latif MT, Suan LS, Dominick D, Sahani M, Jaafar SA, et al.
    Bull Environ Contam Toxicol, 2014 Mar;92(3):317-22.
    PMID: 24435135 DOI: 10.1007/s00128-014-1201-1
    This study aims to determine the composition and sources of particulate matter with an aerodynamic diameter of 10 μm or less (PM10) in a semi-urban area. PM10 samples were collected using a high volume sampler. Heavy metals (Fe, Zn, Pb, Mn, Cu, Cd and Ni) and cations (Na(+), K(+), Ca(2+) and Mg(2+)) were detected using inductively coupled plasma mass spectrometry, while anions (SO4 (2-), NO3 (-), Cl(-) and F(-)) were analysed using Ion Chromatography. Principle component analysis and multiple linear regressions were used to identify the source apportionment of PM10. Results showed the average concentration of PM10 was 29.5 ± 5.1 μg/m(3). The heavy metals found were dominated by Fe, followed by Zn, Pb, Cu, Mn, Cd and Ni. Na(+) was the dominant cation, followed by Ca(2+), K(+) and Mg(2+), whereas SO4 (2-) was the dominant anion, followed by NO3 (-), Cl(-) and F(-). The main sources of PM10 were the Earth's crust/road dust, followed by vehicle emissions, industrial emissions/road activity, and construction/biomass burning.
    Matched MeSH terms: Air Pollution/statistics & numerical data*
  5. Abushammala MF, Basri NE, Elfithri R
    Environ Monit Assess, 2013 Dec;185(12):9967-78.
    PMID: 23797636
    Methane (CH₄) emissions and oxidation were measured at the Air Hitam sanitary landfill in Malaysia and were modeled using the Intergovernmental Panel on Climate Change waste model to estimate the CH₄ generation rate constant, k. The emissions were measured at several locations using a fabricated static flux chamber. A combination of gas concentrations in soil profiles and surface CH₄ and carbon dioxide (CO₂) emissions at four monitoring locations were used to estimate the CH₄ oxidation capacity. The temporal variations in CH₄ and CO₂ emissions were also investigated in this study. Geospatial means using point kriging and inverse distance weight (IDW), as well as arithmetic and geometric means, were used to estimate total CH₄ emissions. The point kriging, IDW, and arithmetic means were almost identical and were two times higher than the geometric mean. The CH₄ emission geospatial means estimated using the kriging and IDW methods were 30.81 and 30.49 gm(−2) day(−1), respectively. The total CH₄ emissions from the studied area were 53.8 kg day(−1). The mean of the CH₄ oxidation capacity was 27.5 %. The estimated value of k is 0.138 year(−1). Special consideration must be given to the CH₄ oxidation in the wet tropical climate for enhancing CH₄ emission reduction.
    Matched MeSH terms: Air Pollution/statistics & numerical data
  6. Abushammala MF, Basri NE, Basri H, Kadhum AA, El-Shafie AH
    Environ Monit Assess, 2013 Jun;185(6):4919-32.
    PMID: 23054277 DOI: 10.1007/s10661-012-2913-5
    Methane (CH₄) is one of the most relevant greenhouse gases and it has a global warming potential 25 times greater than that of carbon dioxide (CO₂), risking human health and the environment. Microbial CH₄ oxidation in landfill cover soils may constitute a means of controlling CH₄ emissions. The study was intended to quantify CH₄ and CO₂ emissions rates at the Sungai Sedu open dumping landfill during the dry season, characterize their spatial and temporal variations, and measure the CH₄ oxidation associated with the landfill cover soil using a homemade static flux chamber. Concentrations of the gases were analyzed by a Micro-GC CP-4900. Two methods, kriging values and inverse distance weighting (IDW), were found almost identical. The findings of the proposed method show that the ratio of CH₄ to CO₂ emissions was 25.4 %, indicating higher CO₂ emissions than CH₄ emissions. Also, the average CH₄ oxidation in the landfill cover soil was 52.5 %. The CH₄ and CO₂ emissions did not show fixed-pattern temporal variation based on daytime measurements. Statistically, a negative relationship was found between CH₄ emissions and oxidation (R(2) = 0.46). It can be concluded that the variation in the CH₄ oxidation was mainly attributed to the properties of the landfill cover soil.
    Matched MeSH terms: Air Pollution/statistics & numerical data
  7. Abdullah MZ, Saat AB, Hamzah ZB
    Environ Monit Assess, 2012 Jun;184(6):3959-69.
    PMID: 21822578 DOI: 10.1007/s10661-011-2236-y
    Biomonitoring of multi-element atmospheric deposition using terrestrial moss is a well-established technique in Europe. Although the technique is widely known, there were very limited records of using this technique to study atmospheric air pollution in Malaysia. In this present study, the deposition of 11 trace metals surrounding the main petroleum refinery plant in Kerteh Terengganu (eastern part of peninsular Malaysia) has been evaluated using two local moss species, namely Hypnum plumaeforme and Taxithelium instratum as bioindicators. The study was also done by means of observing whether these metals are attributed to work related to oil exploration in this area. The moss samples have been collected at 30 sampling stations in the vicinity of the petrochemical industrial area covering up to 15 km to the south, north, and west in radius. The contents of heavy metal in moss samples were analyzed by energy dispersive x-ray fluorescence technique. Distribution of heavy metal content in all mosses is portrayed using Surfer software. Areas of the highest level of contaminations are highlighted. The results obtained using the principal components analysis revealed that the elements can be grouped into three different components that indirectly reflected three different sources namely anthropogenic factor, vegetation factor, and natural sources (soil dust or substrate) factor. Heavy metals deposited mostly in the distance after 9 km onward to the western part (the average direction of wind blow). V, Cr, Cu, and Hg are believed to have originated from local petrochemical-based industries operated around petroleum industrial area.
    Matched MeSH terms: Air Pollution/statistics & numerical data
  8. Abdullah L, Khalid ND
    Environ Monit Assess, 2012 Nov;184(11):6957-65.
    PMID: 22160435 DOI: 10.1007/s10661-011-2472-1
    Proper identification of environment's air quality based on limited observations is an essential task to meet the goals of environmental management. Various classification methods have been used to estimate the change of air quality status and health. However, discrepancies frequently arise from the lack of clear distinction between each air quality, the uncertainty in the quality criteria employed and the vagueness or fuzziness embedded in the decision-making output values. Owing to inherent imprecision, difficulties always exist in some conventional methodologies when describing integrated air quality conditions with respect to various pollutants. Therefore, this paper presents two fuzzy multiplication synthetic techniques to establish classification of air quality. The fuzzy multiplication technique empowers the max-min operations in "or" and "and" in executing the fuzzy arithmetic operations. Based on a set of air pollutants data carbon monoxide, sulfur dioxide, nitrogen dioxide, ozone, and particulate matter (PM(10)) collected from a network of 51 stations in Klang Valley, East Malaysia, Sabah, and Sarawak were utilized in this evaluation. The two fuzzy multiplication techniques consistently classified Malaysia's air quality as "good." The findings indicated that the techniques may have successfully harmonized inherent discrepancies and interpret complex conditions. It was demonstrated that fuzzy synthetic multiplication techniques are quite appropriate techniques for air quality management.
    Matched MeSH terms: Air Pollution/statistics & numerical data*
  9. Sansuddin N, Ramli NA, Yahaya AS, Yusof NF, Ghazali NA, Madhoun WA
    Environ Monit Assess, 2011 Sep;180(1-4):573-88.
    PMID: 21136287 DOI: 10.1007/s10661-010-1806-8
    Malaysia has experienced several haze events since the 1980s as a consequence of the transboundary movement of air pollutants emitted from forest fires and open burning activities. Hazy episodes can result from local activities and be categorized as "localized haze". General probability distributions (i.e., gamma and log-normal) were chosen to analyze the PM(10) concentrations data at two different types of locations in Malaysia: industrial (Johor Bahru and Nilai) and residential (Kota Kinabalu and Kuantan). These areas were chosen based on their frequently high PM(10) concentration readings. The best models representing the areas were chosen based on their performance indicator values. The best distributions provided the probability of exceedances and the return period between the actual and predicted concentrations based on the threshold limit given by the Malaysian Ambient Air Quality Guidelines (24-h average of 150 μg/m(3)) for PM(10) concentrations. The short-term prediction for PM(10) exceedances in 14 days was obtained using the autoregressive model.
    Matched MeSH terms: Air Pollution/statistics & numerical data*
  10. Sulong NA, Latif MT, Khan MF, Amil N, Ashfold MJ, Wahab MIA, et al.
    Sci Total Environ, 2017 Dec 01;601-602:556-570.
    PMID: 28575833 DOI: 10.1016/j.scitotenv.2017.05.153
    This study aims to determine PM2.5concentrations and their composition during haze and non-haze episodes in Kuala Lumpur. In order to investigate the origin of the measured air masses, the Numerical Atmospheric-dispersion Modelling Environment (NAME) and Global Fire Assimilation System (GFAS) were applied. Source apportionment of PM2.5was determined using Positive Matrix Factorization (PMF). The carcinogenic and non-carcinogenic health risks were estimated using the United State Environmental Protection Agency (USEPA) method. PM2.5samples were collected from the centre of the city using a high-volume air sampler (HVS). The results showed that the mean PM2.5concentrations collected during pre-haze, haze and post-haze periods were 24.5±12.0μgm-3, 72.3±38.0μgm-3and 14.3±3.58μgm-3, respectively. The highest concentration of PM2.5during haze episode was five times higher than World Health Organisation (WHO) guidelines. Inorganic compositions of PM2.5, including trace elements and water soluble ions were determined using inductively coupled plasma-mass spectrometry (ICP-MS) and ion chromatography (IC), respectively. The major trace elements identified were K, Al, Ca, Mg and Fe which accounted for approximately 93%, 91% and 92% of the overall metals' portions recorded during pre-haze, haze and post-haze periods, respectively. For water-soluble ions, secondary inorganic aerosols (SO42-, NO3-and NH4+) contributed around 12%, 43% and 16% of the overall PM2.5mass during pre-haze, haze and post-haze periods, respectively. During haze periods, the predominant source identified using PMF was secondary inorganic aerosol (SIA) and biomass burning where the NAME simulations indicate the importance of fires in Sumatra, Indonesia. The main source during pre-haze and post-haze were mix SIA and road dust as well as mineral dust, respectively. The highest non-carcinogenic health risk during haze episode was estimated among the infant group (HI=1.06) while the highest carcinogenic health risk was estimated among the adult group (2.27×10-5).
    Matched MeSH terms: Air Pollution/statistics & numerical data*
  11. Mirzaei M, Bekri M
    Environ Res, 2017 Apr;154:345-351.
    PMID: 28161426 DOI: 10.1016/j.envres.2017.01.023
    Climate change and global warming as the key human societies' threats are essentially associated with energy consumption and CO2 emissions. A system dynamic model was developed in this study to model the energy consumption and CO2 emission trends for Iran over 2000-2025. Energy policy factors are considered in analyzing the impact of different energy consumption factors on environmental quality. The simulation results show that the total energy consumption is predicted to reach 2150 by 2025, while that value in 2010 is 1910, which increased by 4.3% yearly. Accordingly, the total CO2 emissions in 2025 will reach 985million tonnes, which shows about 5% increase yearly. Furthermore, we constructed policy scenarios based on energy intensity reduction. The analysis show that CO2 emissions will decrease by 12.14% in 2025 compared to 2010 in the scenario of 5% energy intensity reduction, and 17.8% in the 10% energy intensity reduction scenario. The results obtained in this study provide substantial awareness regarding Irans future energy and CO2 emission outlines.
    Matched MeSH terms: Air Pollution/statistics & numerical data*
  12. Khan MF, Hamid AH, Bari MA, Tajudin ABA, Latif MT, Nadzir MSM, et al.
    Sci Total Environ, 2019 Feb 10;650(Pt 1):1195-1206.
    PMID: 30308807 DOI: 10.1016/j.scitotenv.2018.09.072
    Equatorial warming conditions in urban areas can influence the particle number concentrations (PNCs), but studies assessing such factors are limited. The aim of this study was to evaluate the level of size-resolved PNCs, their potential deposition rate in the human respiratory system, and probable local and transboundary inputs of PNCs in Kuala Lumpur. Particle size distributions of a 0.34 to 9.02 μm optical-equivalent size range were monitored at a frequency of 60 s between December 2016 and January 2017 using an optical-based compact scanning mobility particle sizer (SMPS). Diurnal and correlation analysis showed that traffic emissions and meteorological confounding factors were potential driving factors for changes in the PNCs (Dp ≤1 μm) at the modeling site. Trajectory modeling showed that a PNC <100/cm3 was influenced mainly by Indo-China region air masses. On the other hand, a PNC >100/cm3 was influenced by air masses originating from the Indian Ocean and Indochina regions. Receptor models extracted five potential sources of PNCs: industrial emissions, transportation, aged traffic emissions, miscellaneous sources, and a source of secondary origin coupled with meteorological factors. A respiratory deposition model for male and female receptors predicted that the deposition flux of PM1 (particle mass ≤1 μm) into the alveolar (AL) region was higher (0.30 and 0.25 μg/h, respectively) than the upper airway (UA) (0.29 and 0.24 μg/h, respectively) and tracheobronchial (TB) regions (0.02 μg/h for each). However, the PM2.5 deposition flux was higher in the UA (2.02 and 1.68 μg/h, respectively) than in the TB (0.18 and 0.15 μg/h, respectively) and the AL regions (1.09 and 0.91 μg/h, respectively); a similar pattern was also observed for PM10.
    Matched MeSH terms: Air Pollution/statistics & numerical data*
  13. Ng KY, Awang N
    Environ Monit Assess, 2018 Jan 06;190(2):63.
    PMID: 29306973 DOI: 10.1007/s10661-017-6419-z
    Frequent haze occurrences in Malaysia have made the management of PM10 (particulate matter with aerodynamic less than 10 μm) pollution a critical task. This requires knowledge on factors associating with PM10 variation and good forecast of PM10 concentrations. Hence, this paper demonstrates the prediction of 1-day-ahead daily average PM10 concentrations based on predictor variables including meteorological parameters and gaseous pollutants. Three different models were built. They were multiple linear regression (MLR) model with lagged predictor variables (MLR1), MLR model with lagged predictor variables and PM10 concentrations (MLR2) and regression with time series error (RTSE) model. The findings revealed that humidity, temperature, wind speed, wind direction, carbon monoxide and ozone were the main factors explaining the PM10 variation in Peninsular Malaysia. Comparison among the three models showed that MLR2 model was on a same level with RTSE model in terms of forecasting accuracy, while MLR1 model was the worst.
    Matched MeSH terms: Air Pollution/statistics & numerical data*
  14. Andersen ZJ, Stafoggia M, Weinmayr G, Pedersen M, Galassi C, Jørgensen JT, et al.
    Environ Health Perspect, 2017 10 13;125(10):107005.
    PMID: 29033383 DOI: 10.1289/EHP1742
    BACKGROUND: Epidemiological evidence on the association between ambient air pollution and breast cancer risk is inconsistent.

    OBJECTIVE: We examined the association between long-term exposure to ambient air pollution and incidence of postmenopausal breast cancer in European women.

    METHODS: In 15 cohorts from nine European countries, individual estimates of air pollution levels at the residence were estimated by standardized land-use regression models developed within the European Study of Cohorts for Air Pollution Effects (ESCAPE) and Transport related Air Pollution and Health impacts – Integrated Methodologies for Assessing Particulate Matter (TRANSPHORM) projects: particulate matter (PM) ≤2.5μm, ≤10μm, and 2.5–10μm in diameter (PM2.5, PM10, and PMcoarse, respectively); PM2.5 absorbance; nitrogen oxides (NO2 and NOx); traffic intensity; and elemental composition of PM. We estimated cohort-specific associations between breast cancer and air pollutants using Cox regression models, adjusting for major lifestyle risk factors, and pooled cohort-specific estimates using random-effects meta-analyses.

    RESULTS: Of 74,750 postmenopausal women included in the study, 3,612 developed breast cancer during 991,353 person-years of follow-up. We found positive and statistically insignificant associations between breast cancer and PM2.5 {hazard ratio (HR)=1.08 [95% confidence interval (CI): 0.77, 1.51] per 5 μg/m3}, PM10 [1.07 (95% CI: 0.89, 1.30) per 10 μg/m3], PMcoarse[1.20 (95% CI: 0.96, 1.49 per 5 μg/m3], and NO2 [1.02 (95% CI: 0.98, 1.07 per 10 μg/m3], and a statistically significant association with NOx [1.04 (95% CI: 1.00, 1.08) per 20 μg/m3, p=0.04].

    CONCLUSIONS: We found suggestive evidence of an association between ambient air pollution and incidence of postmenopausal breast cancer in European women. https://doi.org/10.1289/EHP1742.

    Matched MeSH terms: Air Pollution/statistics & numerical data*
  15. Mudway IS, Dundas I, Wood HE, Marlin N, Jamaludin JB, Bremner SA, et al.
    Lancet Public Health, 2019 Jan;4(1):e28-e40.
    PMID: 30448150 DOI: 10.1016/S2468-2667(18)30202-0
    BACKGROUND: Low emission zones (LEZ) are an increasingly common, but unevaluated, intervention aimed at improving urban air quality and public health. We investigated the impact of London's LEZ on air quality and children's respiratory health.

    METHODS: We did a sequential annual cross-sectional study of 2164 children aged 8-9 years attending primary schools between 2009-10 and 2013-14 in central London, UK, following the introduction of London's LEZ in February, 2008. We examined the association between modelled pollutant exposures of nitrogen oxides (including nitrogen dioxide [NO2]) and particulate matter with a diameter of less than 2·5 μm (PM2·5) and less than 10 μm (PM10) and lung function: postbronchodilator forced expiratory volume in 1 s (FEV1, primary outcome), forced vital capacity (FVC), and respiratory or allergic symptoms. We assigned annual exposures by each child's home and school address, as well as spatially resolved estimates for the 3 h (0600-0900 h), 24 h, and 7 days before each child's assessment, to isolate long-term from short-term effects.

    FINDINGS: The percentage of children living at addresses exceeding the EU limit value for annual NO2 (40 μg/m3) fell from 99% (444/450) in 2009 to 34% (150/441) in 2013. Over this period, we identified a reduction in NO2 at both roadside (median -1·35 μg/m3 per year; 95% CI -2·09 to -0·61; p=0·0004) and background locations (-0·97; -1·56 to -0·38; p=0·0013), but not for PM10. The effect on PM2·5 was equivocal. We found no association between postbronchodilator FEV1 and annual residential pollutant attributions. By contrast, FVC was inversely correlated with annual NO2 (-0·0023 L/μg per m3; -0·0044 to -0·0002; p=0·033) and PM10 (-0·0090 L/μg per m3; -0·0175 to -0·0005; p=0·038).

    INTERPRETATION: Within London's LEZ, a smaller lung volume in children was associated with higher annual air pollutant exposures. We found no evidence of a reduction in the proportion of children with small lungs over this period, despite small improvements in air quality in highly polluted urban areas during the implementation of London's LEZ. Interventions that deliver larger reductions in emissions might yield improvements in children's health.

    FUNDING: National Institute for Health Research Biomedical Research Centre at Guy's and St Thomas' National Health Service (NHS) Foundation Trust and King's College London, NHS Hackney, Lee Him donation, and Felicity Wilde Charitable Trust.

    Matched MeSH terms: Air Pollution/statistics & numerical data*
  16. Othman J, Sahani M, Mahmud M, Ahmad MK
    Environ Pollut, 2014 Jun;189:194-201.
    PMID: 24682070 DOI: 10.1016/j.envpol.2014.03.010
    This study assessed the economic value of health impacts of transboundary smoke haze pollution in Kuala Lumpur and adjacent areas in the state of Selangor, Malaysia. Daily inpatient data from 2005, 2006, 2008, and 2009 for 14 haze-related illnesses were collected from four hospitals. On average, there were 19 hazy days each year during which the air pollution levels were within the Lower Moderate to Hazardous categories. No seasonal variation in inpatient cases was observed. A smoke haze occurrence was associated with an increase in inpatient cases by 2.4 per 10,000 populations each year, representing an increase of 31 percent from normal days. The average annual economic loss due to the inpatient health impact of haze was valued at MYR273,000 ($91,000 USD).
    Matched MeSH terms: Air Pollution/statistics & numerical data*
  17. Opstelten JL, Beelen RMJ, Leenders M, Hoek G, Brunekreef B, van Schaik FDM, et al.
    Dig Dis Sci, 2016 Oct;61(10):2963-2971.
    PMID: 27461060 DOI: 10.1007/s10620-016-4249-4
    BACKGROUND: Industrialization has been linked to the etiology of inflammatory bowel disease (IBD).

    AIM: We investigated the association between air pollution exposure and IBD.

    METHODS: The European Prospective Investigation into Cancer and Nutrition cohort was used to identify cases with Crohn's disease (CD) (n = 38) and ulcerative colitis (UC) (n = 104) and controls (n = 568) from Denmark, France, the Netherlands, and the UK, matched for center, gender, age, and date of recruitment. Air pollution data were obtained from the European Study of Cohorts for Air Pollution Effects. Residential exposure was assessed with land-use regression models for particulate matter with diameters of <10 μm (PM10), <2.5 μm (PM2.5), and between 2.5 and 10 μm (PMcoarse), soot (PM2.5 absorbance), nitrogen oxides, and two traffic indicators. Conditional logistic regression analyses were performed to calculate odds ratios (ORs) with 95 % confidence intervals (CIs).

    RESULTS: Although air pollution was not significantly associated with CD or UC separately, the associations were mostly similar. Individuals with IBD were less likely to have higher exposure levels of PM2.5 and PM10, with ORs of 0.24 (95 % CI 0.07-0.81) per 5 μg/m(3) and 0.25 (95 % CI 0.08-0.78) per 10 μg/m(3), respectively. There was an inverse but nonsignificant association for PMcoarse. A higher nearby traffic load was positively associated with IBD [OR 1.60 (95 % CI 1.04-2.46) per 4,000,000 motor vehicles × m per day]. Other air pollutants were positively but not significantly associated with IBD.

    CONCLUSION: Exposure to air pollution was not found to be consistently associated with IBD.

    Matched MeSH terms: Air Pollution/statistics & numerical data*
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