Pollen exposure is associated with risk of respiratory symptoms during the first year of life

Early life exposure to pollens and increased risks of childhood asthma: a prospective cohort study in Ontario children

Asthma is a disease characterised by wheeze, cough and shortness of breath, and constitutes the most prevalent chronic disease among children [1]. Various phenotypes have been specifically identified in the paediatric population, and include early transient wheeze, current wheeze/asthma, and mild or moderate asthma [2]. Lifestyle behaviours, genetics, maternal and paternal factors, and environment exposures have been identified as risk factors in the multifactorial aetiology of childhood asthma [3].

Increased exposure to tree canopy around the place of residence at birth prevented the risk of childhood asthma development, but this protective effect can be reduced when exposure to weed and tree pollen increases https://bit.ly/3Tboabo

Estimation of historical daily airborne pollen concentrations across Switzerland using a spatio temporal random forest model

High concentrations of airborne pollen trigger seasonal allergies and possibly more severe adverse respiratory and cardiovascular health events. Predicting pollen concentration accurately is valuable for epidemiological studies, in order to study the effects of pollen exposure. We aimed to develop a spatiotemporal machine learning model predicting daily pollen concentrations at a spatial resolution of 1 × 1 km across Switzerland between 2000 and 2019. Daily pollen concentrations for five common, highly allergenic pollen types (hazel, alder, birch ash, and grasses) were available from fourteen measurement sites across Switzerland. We considered several predictors such as elevation, species distribution, wind speed, wind direction, temperature, precipitation, relative humidity, satellite-observed Normalized Difference Vegetation Index, and land-use (CORINE, Landsat satellite) to explain variation in pollen concentration. We employed feature engineering techniques to encode categorical variables and fill in missing values. We applied a random forest machine learning model with 5-fold cross-validation. The 5th-99th percentiles for concentrations of hazel, alder, birch, ash, and grass pollen at the pollen monitoring stations were 0-298, 0-306, 0-1153, 0-800, and 0-290 pollen grains/m3, respectively. The results of a predictive model for these concentrations yielded overall R2 values of 0.87, 0.84, 0.89, 0.88, and 0.91, and temporal root mean squared errors (RMSEs) of 16.07, 16.72, 69.04, 41.50, and 22.45 pollen grains/m3. An analysis of predictor variable importance indicates that the average national daily pollen concentration is the most important predictor of pollen concentrations for all pollen types. Furthermore, meteorological variables including temperature, total precipitation, humidity, boundary layer height, wind speed, and wind direction, as well as date and satellite features, are important factors in pollen concentration prediction. These spatiotemporal pollen models will serve to estimate individual residential pollen exposure for epidemiological studies. Resulting estimates will enable us to study respiratory and cardiovascular mortality and hospital admissions in Switzerland.

Effect of altered human exposome on the skin and mucosal epithelial barrier integrity

Pollution in the world and exposure of humans and nature to toxic substances is continuously worsening at a rapid pace. In the last 60 years, human and domestic animal health has been challenged by continuous exposure to toxic substances and pollutants because of uncontrolled growth, modernization, and industrialization. More than 350,000 new chemicals have been introduced to our lives, mostly without any reasonable control of their health effects and toxicity. A plethora of studies show exposure to these harmful substances during this period with their implications on the skin and mucosal epithelial barrier and increasing prevalence of allergic and autoimmune diseases in the context of the "epithelial barrier hypothesis". Exposure to these substances causes an epithelial injury with peri-epithelial inflammation, microbial dysbiosis and bacterial translocation to sub-epithelial areas, and immune response to dysbiotic bacteria. Here, we provide scientific evidence on the altered human exposome and its impact on epithelial barriers.