GIS-based Association Between PM10 and Allergic Diseases in Seoul: Implications for Health and Environmental Policy

Abstract

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. Purpose:The role of PM10 in the development of allergic diseases remains controversial among epidemiological studies, partly due to the inability to control for spatial variations in large-scale risk factors. This study aims to investigate spatial correspondence between the level of PM10 and allergic diseases at the sub-district level in Seoul, Korea, in order to evaluate whether the impact of PM10 is observable and spatially varies across the subdistricts. Methods: PM10 measurements at 25 monitoring stations in the city were interpolated to 424 sub-districts where annual inpatient and outpatient count data for 3 types of allergic diseases (atopic dermatitis, asthma, and allergic rhinitis) were collected. We estimated multiple ordinary least square regression models to examine the association of the PM10 level with each of the allergic diseases, controlling for various sub-district level covariates. Geographically weighted regression (GWR) models were conducted to evaluate how the impact of PM10 varies across the sub-districts. Results: PM10 was found to be a significant predictor of atopic dermatitis patient count (P<0.01), with greater association when spatially interpolated at the sub-district level. No significant effect of PM10 was observed on allergic rhinitis and asthma when socioeconomic factors were controlled for. GWR models revealed spatial variation of PM10 effects on atopic dermatitis across the sub-districts in Seoul. The relationship of PM10 levels to atopic dermatitis patient counts is found to be significant only in the Gangbuk region (P<0.01), along with other covariates including average land value, poverty rate, level of education and apartment rate (P<0.01). Conclusions: Our findings imply that PM10 effects on allergic diseases might not be consistent throughout Seoul. GIS-based spatial modeling techniques could play a role in evaluating spatial variation of air pollution impacts on allergic diseases at the sub-district level, which could provide valuable guidelines for environmental and public health policymakers.

Machine Learning-Based Asthma Risk Prediction Using IoT and Smartphone Applications

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Systematic Review and Meta-Analysis of Human Skin Diseases Due to Particulate Matter

“…Recently, ambient PM 2.5 has become increasingly present in the surrounding air and significantly involved in human health, particularly in regard to respiratory tract diseases, as it can reach the bronchial tubes and deep regions of the lung [ 7 ]. Reference cohort studies in which participants are monitored for decades have discovered that smaller particles such as PM 2.5 have more adverse effects on human health than larger particles [ 4 , 8 , 9 , 10 ]. Additionally, epidemiological investigations into contamination, especially ambient air pollution, indicated that the PM is not only correlative with the exacerbation of cardiovascular diseases and respiratory systemic inflammation impacts but also the progression of inflammatory skin diseases [ 11 ] such as atopic dermatitis (AD) [ 12 , 13 , 14 ], acne, psoriasis, and allergic reactions [ 9 , 15 , 16 , 17 ].…”

Spatiotemporal variations of asthma admission rates and its relationship with environmental factors in Guangxi, China

“…Among the air pollution factors, PM 2.5 , NO 2 and CO were positively correlated with asthma in multiple regions. To our knowledge, there was no consensus among various studies on the air pollutants that affect the onset of asthma [11,15,18,[48][49][50]. Although the relationship between asthma and exposure to PM 2.5 has been frequently measured, reported conclusions have not been consistent [42].…”

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