Long-term effects of elemental composition of particulate matter on inflammatory blood markers in European cohorts

Hampel, Regina; Peters, Annette; Beelen, Rob; Brunekreef, Bert; Cyrys, Josef; Fairé, Ulf dé; Hoogh, Kees de; Fuks, Kateryna; Hoffmann, Barbara; Hüls, Anke; Imboden, Medea; Jedyńska, Aleksandra; Kooter, Ingeborg M.; Köenig, Wolfgang; Künzli, Nino; Leander, Karin; Magnusson, Patrik K. E.; Männistö, Satu; Penell, Johanna; Pershagen, Göran; Phuleria, Harish C.; Probst‐Hensch, Nicole; Pundt, Noreen; Schaffner, Emmanuel; Schikowski, Tamara; Sugiri, Dorothea; Tiittanen, Pekka; Tsai, Ming-Yi; Wang, Meng; Wolf, Kathrin; Lanki, Timo; groups, Escape Transphorm study

doi:10.1016/j.envint.2015.05.008

Cited by 86 publications

(61 citation statements)

References 45 publications

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“…2 shows the average concentrations as a boxplot on the log scale. In general, measurements had similar ranges to elemental PM2.5 components from previous studies (de Hoogh et al, 2013; Zhang et al, 2015; Beelen, 2015; Eeftens et al, 2014; Hampel et al, 2015). More specific descriptive numbers along with the variance are listed in Table 2.…”

Section: Resultssupporting

confidence: 79%

“…Recently, successful LUR models have been developed for PM components in twenty areas in Europe as a part of the ESCAPE study (de Hoogh et al, 2013) and for an urban area in Canada (Zhang et al, 2015). These land use models have allowed for assessment of exposure to individual components of PM and the study of their association with health outcomes (Beelen, 2015; Eeftens et al, 2014; Hampel et al, 2015). Although some models have been developed, limited information on PM components has impeded progress in identifying their health effects (Bell et al, 2007).…”

Section: Introductionmentioning

confidence: 99%

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Exposure assessment models for elemental components of particulate matter in an urban environment: A comparison of regression and random forest approaches

Brokamp

Jandarov

Rao

et al. 2017

Atmospheric Environment

207

124

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Exposure assessment for elemental components of particulate matter (PM) using land use modeling is a complex problem due to the high spatial and temporal variations in pollutant concentrations at the local scale. Land use regression (LUR) models may fail to capture complex interactions and non-linear relationships between pollutant concentrations and land use variables. The increasing availability of big spatial data and machine learning methods present an opportunity for improvement in PM exposure assessment models. In this manuscript, our objective was to develop a novel land use random forest (LURF) model and compare its accuracy and precision to a LUR model for elemental components of PM in the urban city of Cincinnati, Ohio. PM smaller than 2.5 μm (PM2.5) and eleven elemental components were measured at 24 sampling stations from the Cincinnati Childhood Allergy and Air Pollution Study (CCAAPS). Over 50 different predictors associated with transportation, physical features, community socioeconomic characteristics, greenspace, land cover, and emission point sources were used to construct LUR and LURF models. Cross validation was used to quantify and compare model performance. LURF and LUR models were created for aluminum (Al), copper (Cu), iron (Fe), potassium (K), manganese (Mn), nickel (Ni), lead (Pb), sulfur (S), silicon (Si), vanadium (V), zinc (Zn), and total PM2.5 in the CCAAPS study area. LURF utilized a more diverse and greater number of predictors than LUR and LURF models for Al, K, Mn, Pb, Si, Zn, TRAP, and PM2.5 all showed a decrease in fractional predictive error of at least 5% compared to their LUR models. LURF models for Al, Cu, Fe, K, Mn, Pb, Si, Zn, TRAP, and PM2.5 all had a cross validated fractional predictive error less than 30%. Furthermore, LUR models showed a differential exposure assessment bias and had a higher prediction error variance. Random forest and other machine learning methods may provide more accurate exposure assessment.

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Section: Resultssupporting

confidence: 79%

Section: Introductionmentioning

confidence: 99%

Exposure assessment models for elemental components of particulate matter in an urban environment: A comparison of regression and random forest approaches

Brokamp

Jandarov

Rao

et al. 2017

Atmospheric Environment

207

124

View full text Add to dashboard Cite

show abstract

“…Thus, it contributes to the symptoms of Sick Building Syndrome (LIN et al, 2016), associated to long-term exposure to ambient particulate matter with increased mortality from cardiovascular and respiratory disorders (ANNESI-MAESANO et al, 2007;HAMPEL et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Impact of human activities on the concentration of indoor air particles in an antarctic research station

et al. 2018

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Abstractne of the main characteristics of Antarctic buildings is the fact that they are designed mostly with a focus on energy efficiency. Although human activity is a major source of pollution, indoor air quality is not a matter of significant concern during building planning. This study examines the relationship between indoor activities in an Antarctic Research Station and the size distribution of particulate matter. Real-time particle size distribution data is used in conjunction with time-activity data. The activity number ratio is calculated using the mean number of particles found in each size range during each activity divided by the average number of particles found during a period characterized by the absence of human activities. Cooking, the use of cosmetics, waste incineration and exhaust from light vehicles were responsible for significant deterioration of indoor air related to the presence of fine and ultrafine particles. Cleaning, physical exercise and the movement of people were responsible for the emission of coarse particles. This article emphasizes the importance of post-occupancy evaluation of buildings, generating results relevant to the planning and layout of new buildings, especially regarding better indoor air quality.Keywords: Particulate matter. Indoor air quality. Human activities. Size distribution. Antarctic Research Station. Resumo Uma das principais características das construções antárticas é o fato que boa parte do esforço projetual é concentrado na eficiência energética. Embora a atividade humana seja a maior fonte de poluição, a qualidade do ar interior não é uma questão com significativa preocupação durante o planejamento da construção. Este estudo investigou a relação entre as atividades internas em uma Estação de Pesquisa Antártica e a distribuição dos tamanhos das partículas. Dados de distribuição do tamanho das partículas são plotados em tempo real em conjunção com os registros das atividades. A taxa de atividade é calculada usando

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“…consuming less foods with trace amounts of metals) (134). Heavy metals present in PM appear to be the main culprits impacting mental health via the respiratory system, cardiovascular system and immune system (69,74,86).…”

Section: Discussionmentioning

confidence: 99%

“…CRP is an indiscriminate marker of inflammation produced by the liver (74). The inflammatory marker CRP has demonstrated a positive association with recent suicide attempts in the psychiatric population, wherein a higher concentration of CRP has been found in recent suicide attempters compared to those who have not recently attempted (75).…”

Section: Respiratory Systemmentioning

confidence: 99%

Air pollution, aeroallergens and suicidality: a review of the effects of air pollution and aeroallergens on suicidal behavior and an exploration of possible mechanisms

Ragguett

Danielle

Subramaniapillai

et al. 2017

Reviews on Environmental Health

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Objective: Risk factors for suicide can be broadly categorized as sociodemographic, clinical and treatment. There is interest in environmental risk and protection factors for suicide. Emerging evidence suggests a link between environmental factors in the form of air pollution and aeroallergens in relation to suicidality. Methods: Herein, we conducted a systematic review of 15 articles which have met inclusion criteria on the aforementioned effects. Results: The majority of the reviewed articles reported an increased suicide risk alongside increased air pollutants or aeroallergens (i.e. pollen) increase; however, not all environmental factors were explored equally. In specific, studies that were delimited to evaluating particulate matter (PM) reported a consistent association with suicidality. We also provide a brief description of putative mechanisms (e.g. inflammation and neurotransmitter dysregulation) that may mediate the association between air pollution, aeroallergens and suicidality. Conclusion:Available evidence suggests that exposure to harmful air quality may be associated with suicidality. There are significant public health implications which are amplified in regions and countries with greater levels of air pollution and aeroallergens. In addition, those with atopic sensitivity may represent a specific subgroup that is at risk.

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Long-term effects of elemental composition of particulate matter on inflammatory blood markers in European cohorts

Cited by 86 publications

References 45 publications

Exposure assessment models for elemental components of particulate matter in an urban environment: A comparison of regression and random forest approaches

Exposure assessment models for elemental components of particulate matter in an urban environment: A comparison of regression and random forest approaches

Impact of human activities on the concentration of indoor air particles in an antarctic research station

Air pollution, aeroallergens and suicidality: a review of the effects of air pollution and aeroallergens on suicidal behavior and an exploration of possible mechanisms

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