Financing Costs and Health Effects of Air Pollution in the Tri-City Agglomeration

Machine Learning-Based Analyses of the Effects of Various Types of Air Pollutants on Hospital Visits by Asthma Patients

Asthma is a chronic respiratory disorder defined by airway inflammation, chest pains, wheezing, coughing, and difficulty breathing that affects an estimated 300 million individuals globally. Although various studies have shown an association between air pollution and asthma, few studies have used statistical and machine learning algorithms to investigate the effect of each individual air pollutant on asthma. The purpose of this research was to assess the association between air pollutants and the frequency of hospital visits by asthma patients using three analysis methods: linear correlation analyses were performed by Pearson correlation coefficients, and least absolute shrinkage and selection operator (LASSO) and random forest (RF) models were used for machine learning-based analyses to investigate the effect of air pollutants. This research studied asthma patients using the hospital visit database in Seoul, South Korea, collected between 2013 and 2017. The data set included outpatient hospital visits (n = 17,787,982), hospital admissions (n = 215,696), and emergency department visits (n = 85,482). The daily atmospheric environmental information from 2013 to 2017 at 25 locations in Seoul was evaluated. The three analysis models revealed that NO₂ was the most significant pollutant on average in outpatient hospital visits by asthma patients. For example, NO₂ had the greatest impact on outpatient hospital visits, resulting in a positive association (r=0.331). In hospital admissions of asthma patients, CO was the most significant pollutant on average. It was observed that CO exhibited the most positive association with hospital admissions (I = 3.329). Additionally, a significant time lag was found between both NO₂ and CO and outpatient hospital visits and hospital admissions of asthma patients in the linear correlation analysis. In particular, NO₂ and CO were shown to increase hospital admissions at lag 4 in the linear correlation analysis. This study provides evidence that PM_2.5, PM₁₀, NO₂, CO, SO₂, and O₃ are associated with the frequency of hospital visits by asthma patients.

Influence of Selected Air Pollutants on Mortality and Pneumonia Burden in Three Polish Cities over the Years 2011-2018

Poland has one of the worst air qualities in the European Union, particularly regarding concentrations of particulate matter (PM). This study aimed to evaluate the short-term effects of air pollution and weather conditions on all-cause mortality and pneumonia-related hospitalizations in three Polish agglomerations. We investigated data from 2011 to 2018 on a number of health outcomes, concentrations of PM2.5, PM10, nitrogen dioxide (NO2), ozone (O3), and selected meteorological parameters. To examine the impact of air pollutants and weather conditions on mortality and pneumonia burden, we identified optimal general regression models for each agglomeration. The final models explained <24% of the variability in all-cause mortality. In the models with interactions, O3 concentration in Warsaw, NO2, O3, and PM2.5 concentrations in Cracow and PM10 and O3 concentrations in the Tricity explained >10% of the variability in the number of deaths. Up to 46% of daily variability in the number of pneumonia-related hospitalizations was explained by the combination of both factors, i.e., air quality and meteorological parameters. The impact of NO2 levels on pneumonia burden was pronounced in all agglomerations. We showed that the air pollution profile and its interactions with weather conditions exert a short-term effect on all-cause mortality and pneumonia-related hospitalizations. Our findings may be relevant for prioritizing strategies to improve air quality.

The Relationship between Exposure to Airborne Particulate and DNA Adducts in Blood Cells in an Urban Population of Subjects with an Unhealthy Body Mass Index

Bulky DNA adducts are a combined sign of aromatic chemical exposure, as well as an individual's ability to metabolically activate carcinogens and repair DNA damage. The present study aims to investigate the association between PM exposure and DNA adducts in blood cells, in a population of 196 adults with an unhealthy BMI (≥25). For each subject, a DNA sample was obtained for quantification of DNA adducts by sensitive³²P post-labelling methods. Individual PM₁₀ exposure was derived from daily mean concentrations measured by single monitors in the study area and then assigned to each subject by calculating the mean of the 30 days (short-term exposure), and of the 365 (long-term exposure) preceding enrolment. Multivariable linear regression models were used to study the association between PM₁₀ and DNA adducts. The majority of analysed samples had bulky DNA adducts, with an average value of 3.7 ± 1.6 (mean ± SD). Overall, the findings of the linear univariate and multiple linear regression showed an inverse association between long-term PM₁₀ exposure and adduct levels; this unexpected result might be since the population consists of subjects with an unhealthy BMI, which might show an atypical reaction to airborne urban pollutants; a hermetic response which happens when small amounts of pollutants are present. Pollutants can linger for a long time in the adipose tissue of obese persons, contributing to an increase in oxidative DNA damage, inflammation, and thrombosis when exposure is sustained.