Association between air pollution and chronic rhinosinusitis: a nested case-control study using meteorological data and national health screening cohort data

2021 ◽  
Vol 0 (0) ◽  
pp. 0-0
Author(s):  
J.H. Wee ◽  
C. Min ◽  
H.J. Jung ◽  
M.W. Park ◽  
H.G. Choi
Keyword(s):  
Air Pollution ◽  
Chronic Rhinosinusitis ◽  
National Health ◽  
Sunshine Duration ◽  
Meteorological Data ◽  
Health Screening ◽  
Meteorological Conditions ◽  
Control Group ◽  
Temperature Range ◽  
The Impact

Background: Inconsistent results about the effect of air pollution on chronic rhinosinusitis (CRS) have been reported. This study aimed to evaluate the impact of meteorological conditions/air pollution on the prevalence of CRS in adult Koreans. Methodology: The data from the Korean National Health Insurance Service-Health Screening Cohort from 2002 through 2015 were used. A CRS group (defined as ICD-10 codes J32, n=6159) was matched with a control group (n=24,636) in 1:4 ratios by age, sex, income, and region of residence. The meteorological conditions and air pollution data included the daily mean, highest, and lowest temperature (°C), daily temperature range (°C), relative humidity (%), ambient atmospheric pressure (hPa), sunshine duration (hr), and the rainfall (mm), SO2 (ppm), NO2 (ppm), O3 (ppm), CO (ppm), and PM10 (μg/m3) levels before the CRS diagnosis. Crude and adjusted odds ratios (ORs) and 95% confidence intervals (CIs) for CRS were analyzed using logistic regression analyses. Results: When the NO2 level increased by 0.1 ppm, the odds for CRS increased 5.40 times, and when the CO level increased by 1 ppm and PM10 increased by 10 μg/m3, the odds for CRS decreased 0.75 times and 0.93 times, respectively. Other meteorological conditions, such as the mean/highest/lowest temperature, temperature range, rainfall and other air pollution, such as SO2 and O3, were not statistically significant. NO2 for 90 days before the index date increased the risk of CRS in all subgroups, except for the nasal polyp and older age subgroups. Conclusion: CRS is related to high concentrations of NO2.

scholarly journals Application of the Data Mining Methods to Assess the Impact of Meteorological Conditions on the Episodes of High Concentrations of PM10 along the Polish – Czech Border

2018 ◽  
Vol 28 ◽  
pp. 01027
Author(s):  
Leszek Ośródka ◽  
Ewa Krajny ◽  
Marek Wojtylak
Keyword(s):  
Data Mining ◽  
Air Pollution ◽  
Particulate Matter ◽  
Concentration Level ◽  
Meteorological Data ◽  
Meteorological Conditions ◽  
Mining Methods ◽  
High Concentrations ◽  
The Impact ◽  
Derivatives Of

The paper presents an attempt to use selected data mining methods to determine the influence of a complex of meteorological conditions on the concentrations of PM10 (PM2.5) proffering the example of the regions of Silesia and Northern Moravia. The collection of standard meteorological data has been supplemented by increments and derivatives of measurable weather elements such as vertical pseudo-gradient of air temperature. The main objective was to develop a universal methodology for the assessment of these impacts, i.e. one that would be independent of the analysed pollution. The probability of occurrence (at a given location) of the assumed concentration level as exceeding the value of the specified distributional quintile was adopted as the discriminant of the incidence. As a result of the analyses conducted, incidences of elevated concentrations of air pollution particulate matter PM10 have been identified and the types of weather responsible for the emergence of such situations have also been determined.

Increased risk of chronic otitis media in chronic rhinosinusitis patients: a longitudinal follow-up study using a national health screening cohort

2020 ◽  
Vol 0 (0) ◽  
pp. 0-0
Author(s):  
S.K. Kim ◽  
M-W. Park ◽  
c. Min ◽  
I-S. Park ◽  
B. Park ◽  
...  
Keyword(s):  
Chronic Rhinosinusitis ◽  
Otitis Media ◽  
National Health ◽  
Age Groups ◽  
Chronic Otitis Media ◽  
Health Screening ◽  
Control Group ◽  
Increased Risk ◽  
National Patient ◽  
Icd 10

Background: Chronic rhinosinusitis (CRS) and chronic otitis media (COM) share pathophysiological mechanisms such as bacterial infection, biofilm, and persistence of the obstruction state of ventilation routes. However, only a few studies have investigated the relationship between these two diseases nationwide and in the general population. The purpose of this study was to determine whether the incidence of COM in patients with CRS differed from that of a matched control from the national health screening cohort. Methods: Data from the Korean Health Insurance Review and Assessment Service-National Patient Samples were collected from 2002 to 2015. Participants who were treated ≥2 times and underwent head and neck computed tomography evaluation were selected. A 1:4 matched CRS group (n=8,057) and a control group (n=32,228) were selected. The control group included partici- pants who were never treated with the ICD-10 code J32 from 2002 to 2015. The CRS group included CRS patients with/without nasal polyps. Results: The incidence of COM was significantly higher in the CRS group than in the control group. In a subgroup analysis, the incidence of COM in all age groups and in men and women was significantly higher in the CRS group than in the control group. More, CRS increased the risk of COM. Conclusions: A significant association was observed between CRS and COM. This indicates that CRS patients have a high risk of developing COM.

scholarly journals The modification effect of temperature on the relationship between air pollutants and daily incidence of influenza in Ningbo, China

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Rui Zhang ◽  
Yujie Meng ◽  
Hejia Song ◽  
Ran Niu ◽  
Yu Wang ◽  
...  
Keyword(s):  
Air Pollution ◽  
High Temperature ◽  
Air Pollutants ◽  
Meteorological Data ◽  
Effect Of Temperature ◽  
Distributed Lag ◽  
Modification Effect ◽  
Incidence Risk ◽  
The Impact ◽  
The Relationship

Abstract Background Although exposure to air pollution has been linked to many health issues, few studies have quantified the modification effect of temperature on the relationship between air pollutants and daily incidence of influenza in Ningbo, China. Methods The data of daily incidence of influenza and the relevant meteorological data and air pollution data in Ningbo from 2014 to 2017 were retrieved. Low, medium and high temperature layers were stratified by the daily mean temperature with 25th and 75th percentiles. The potential modification effect of temperature on the relationship between air pollutants and daily incidence of influenza in Ningbo was investigated through analyzing the effects of air pollutants stratified by temperature stratum using distributed lag non-linear model (DLNM). Stratified analysis by sex and age were also conducted. Results Overall, a 10 μg/m3 increment of O3, PM2.5, PM10 and NO2 could increase the incidence risk of influenza with the cumulative relative risk of 1.028 (95% CI 1.007, 1.050), 1.061 (95% CI 1.004, 1.122), 1.043 (95% CI 1.003, 1.085), and 1.118 (95% CI 1.028, 1.216), respectively. Male and aged 7–17 years were more sensitive to air pollutants. Through the temperature stratification analysis, we found that temperature could modify the impacts of air pollution on daily incidence of influenza with high temperature exacerbating the impact of air pollutants. At high temperature layer, male and the groups aged 0–6 years and 18–64 years were more sensitive to air pollution. Conclusion Temperature modified the relationship between air pollution and daily incidence of influenza and high temperature would exacerbate the effects of air pollutants in Ningbo.

NESTED HIGH-RESOLUTION NOx AND PM SIMULATIONS OVER ZÜRICH

2021 ◽  
Author(s):  
Ivo Suter ◽  
Lukas Emmenegger ◽  
Dominik Brunner
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Boundary Conditions ◽  
Meteorological Conditions ◽  
Model Systems ◽  
Multi Scale ◽  
Wide Range ◽  
The Impact ◽  
The City ◽  
Concentration Levels

<p>Reducing air pollution, which is the world's largest single environmental health risk, demands better-informed air quality policies. Consequently, multi-scale air quality models are being developed with the goal to resolve cities. One of the major challenges in such model systems is to accurately represent all large- and regional-scale processes that may critically determine the background concentration levels over a given city. This is particularly true for longer-lived species such as aerosols, for which background levels often dominate the concentration levels, even within the city. Furthermore, the heterogeneous local emissions, and complex dispersion in the city have to be considered carefully.</p><p>In this study, the impact of processes across a wide range of scales on background concentrations over Switzerland and the city of Zurich was modelled by performing one year of nested European and Swiss national COSMO-ART simulations to obtain adequate boundary conditions for gas-phase chemical, aerosol and meteorological conditions for city-resolving simulations. The regional climate chemistry model COSMO-ART (Vogel et al. 2009) was used in a 1-way coupled mode. The outer, European, domain, which was driven by chemical boundary conditions from the global MOZART model, had a 6.6 km horizontal resolution and the inner, Swiss, domain one of 2.2 km. For the city scale, a catalogue of more than 1000 mesoscale flow patterns with 100 m resolution was created with the model GRAMM, based on a discrete set of atmospheric stabilities, wind speeds and directions, accounting for the influence of land-use and topography. Finally, the flow around buildings was solved with the CFD model GRAL forced at the boundaries by GRAMM. Subsequently, Lagrangian dispersion simulations for a set of air pollutants and emission sectors (traffic, industry, ...) based on extremely detailed building and emission data was performed in GRAL. The result of this nested procedure is a library of 3-dimensional air pollution maps representative of hourly situations in Zurich (Berchet et al. 2017). From these pre-computed situations, time-series and concentration maps can be obtained by selecting situations according to observed or modelled meteorological conditions.</p><p>The results were compared to measurements from air quality monitoring network stations. Modelled concentrations of NO<sub>x</sub> and PM compared well to measurements across multiple locations, provided background conditions were considered carefully. The nested multi-scale modelling system COSMO-ART/GRAMM/GRAL can adequately reproduce local air quality and help understanding the relative contributions of local versus distant emissions, as well as fill the space between precise point measurements from monitoring sites. This information is useful for research, policy-making, and epidemiological studies particularly under the assumption that exceedingly high concentrations become more and more localised phenomenon in the future.</p>

scholarly journals Evaluation of the impact of meteorological conditions on the amount of air pollution in Krakow

2019 ◽  
Vol 108 ◽  
pp. 02012
Author(s):  
Małgorzata Piaskowska-Silarska ◽  
Krzysztof Pytel ◽  
Stanisław Gumuła ◽  
Wiktor Hudy
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Measurement Data ◽  
Ecological Monitoring ◽  
Meteorological Conditions ◽  
Pollution Problem ◽  
Air Quality Improvement ◽  
Meteorological Elements ◽  
The Impact ◽  
The Relationship

Abstract. The publication presents an assessment of the impact of meteorological conditions on air quality in a given location. The subject matter of the work is related to problem-review issues in the field of environmental protection and energy management. The publication draws attention to the fact that despite several decades of ecological monitoring of air pollution, only in recent years attention has been paid to the scale of air pollution problem. The study examined the relationship between meteorological elements (wind velocity, relative humidity on the amount of air pollution immissions. Significant impact of precipitation, atmospheric pressure and thermal braking layer was indicated. The possibilities of air quality improvement were presented based on the measurement data concerning the immission of impurities.

scholarly journals Risk Analysis of Air Pollution and Meteorological Factors Affecting the Incidence of Diabetes in the Elderly Population in Northern China

2020 ◽  
Vol 2020 ◽  
pp. 1-20
Author(s):  
Yao Lin ◽  
Saijun Zhou ◽  
Hongyan Liu ◽  
Zhuang Cui ◽  
Fang Hou ◽  
...  
Keyword(s):  
Air Pollution ◽  
Meteorological Factors ◽  
Sunshine Duration ◽  
Additive Model ◽  
Northern China ◽  
The Elderly ◽  
The United States ◽  
Diabetes Incidence ◽  
Lag Effects ◽  
The Impact

Background. Research investigating the effect of air pollution on diabetes incidence is mostly conducted in Europe and the United States and often produces conflicting results. The link between meteorological factors and diabetes incidence remains to be explored. We aimed to explore associations between air pollution and diabetes incidence and to estimate the nonlinear and lag effects of meteorological factors on diabetes incidence. Methods. Our study included 19,000 people aged ≥60 years from the Binhai New District without diabetes at baseline. The generalized additive model (GAM) and the distributed lag nonlinear model (DLNM) were used to explore the effect of air pollutants and meteorological factors on the incidence of diabetes. In the model combining the GAM and DLNM, the impact of each factor (delayed by 30 days) was first observed separately to select statistically significant factors, which were then incorporated into the final multivariate model. The association between air pollution and the incidence of diabetes was assessed in subgroups based on age, sex, and body mass index (BMI). Results. We found that cumulative RRs for diabetes incidence were 1.026 (1.011-1.040), 1.019 (1.012-1.026), and 1.051 (1.019-1.083) per 10 μg/m3 increase in PM2.5, PM10, and NO2, respectively, as well as 1.156 (1.058-1.264) per 1 mg/m3 increase in CO in a single-pollutant model. Increased temperature, excessive humidity or dryness, and shortened sunshine duration were positively correlated with the incidence of diabetes in single-factor models. After adjusting for temperature, humidity, and sunshine, the risk of diabetes increased by 9.2% (95% confidence interval (CI):2.1%-16.8%) per 10 μg/m3 increase in PM2.5. We also found that women, the elderly (≥75 years), and obese subjects were more susceptible to the effect of PM2.5. Conclusion. Our data suggest that PM2.5 is positively correlated with the incidence of diabetes in the elderly, and the relationship between various meteorological factors and diabetes in the elderly is nonlinear.

scholarly journals Fine particulate pollution and asthma exacerbations

2017 ◽  
Vol 103 (9) ◽  
pp. 828-831 ◽  
Author(s):  
Naïm Bouazza ◽  
Frantz Foissac ◽  
Saik Urien ◽  
Romain Guedj ◽  
Ricardo Carbajal ◽  
...  
Keyword(s):  
Air Pollution ◽  
Air Pollutants ◽  
Asthma Exacerbation ◽  
Meteorological Data ◽  
Maximum Effect ◽  
Sigmoid Function ◽  
Asthma Exacerbations ◽  
Fine Particulate ◽  
Syncytial Virus ◽  
The Impact

ObjectiveAs the results from epidemiological studies about the impact of outdoor air pollution on asthma in children are heterogeneous, our objective was to investigate the association between asthma exacerbation in children and exposure to air pollutants.MethodsA database of 1 264 585 paediatric visits during the 2010–2015 period to the emergency rooms from 20 emergency departments (EDs) of ‘Assistance Publique Hôpitaux de Paris (APHP)’, the largest hospital group in Europe, was used. A total of 47 107 visits were classified as asthma exacerbations. Concentration of air pollutants (nitrogen dioxide, ozone, fine particulate matter (PM) with an aerodynamic diameter smaller than 10  µm (PM10) and 2.5 µm (PM2.5)), as well as meteorological data, evolution of respiratory syncytial virus infection and pollen exposition, were collected on an hourly or daily basis for the same period using institutional databases. To assess the association between air pollution and asthma, mixed-effects quasi-Poisson regression modelling was performed.ResultsThe only compound independently associated with ED visits for asthma was PM2.5 (P<10−4). The association between asthma exacerbation and PM2.5 was not linear, and a sigmoid function described the relationshipsatisfactorily. PM2.5 concentration, which gives half the maximum effect, was estimated at 13.5 µg/m3.ConclusionsWe found an association between daily asthma exacerbation in paediatric visits to the ED and fine particulate air pollutants.

scholarly journals The Impact of the Lockdown Caused by the COVID-19 Pandemic on the Fine Particulate Matter (PM2.5) Air Pollution: The Greek Paradigm

2021 ◽  
Vol 18 (13) ◽  
pp. 6748
Author(s):  
Ourania S. Kotsiou ◽  
Georgios K. D. Saharidis ◽  
Georgios Kalantzis ◽  
Evangelos C. Fradelos ◽  
Konstantinos I. Gourgoulianis
Keyword(s):  
Air Pollution ◽  
Wireless Sensors ◽  
Fine Particulate Matter ◽  
Meteorological Conditions ◽  
Significant Drop ◽  
Fine Particulate ◽  
Guideline Value ◽  
The Mean ◽  
The Impact ◽  
Pm2.5 Concentration

Introduction: Responding to the coronavirus pandemic, Greece implemented the largest quarantine in its history. No data exist regarding its impact on PM2.5 pollution. We aimed to assess PM2.5 levels before, during, and after lockdown (7 March 2020–16 May 2020) in Volos, one of Greece’s most polluted industrialized cities, and compare PM2.5 levels with those obtained during the same period last year. Meteorological conditions were examined as confounders. Methods: The study period was discriminated into three phases (pre-lockdown: 7 March–9 March, lockdown: 10 March–4 May, and post-lockdown period: 5 May–16 May). A wireless sensors network was used to collect PM2.5, temperature, relative humidity, rainfall, and wind speed data every 2 s. Results: The lockdown resulted in a significant drop of PM2.5 by 37.4% in 2020, compared to 2019 levels. The mean daily concentrations of PM2.5 exceeded the WHO’s guideline value for 24-h mean levels of PM2.5 35% of the study period. During the strictest lockdown (23 March to 4 May), the mean daily PM2.5 levels exceeded the standard 41% of the time. The transition from the pre-lockdown period into lockdown or post-lockdown periods was associated with lower PM2.5 concentrations. Conclusions: A reduction in the mean daily PM2.5 concentration was found compared to 2019. Lockdown was not enough to avoid severe exceedances of air pollution in Volos.

Association of Air Pollutant Exposure and Sinonasal Histopathology Findings in Chronic Rhinosinusitis

2021 ◽  
pp. 194589242199365
Author(s):  
Tirth R. Patel ◽  
Bobby A. Tajudeen ◽  
Hannah Brown ◽  
Paolo Gattuso ◽  
Phillip LoSavio ◽  
...  
Keyword(s):  
Air Pollution ◽  
Chronic Rhinosinusitis ◽  
Air Pollutants ◽  
Ground Level ◽  
Ambient Air ◽  
Ozone Exposure ◽  
Ambient Air Pollution ◽  
Sinus Surgery ◽  
Ground Level Ozone ◽  
The Impact

Background Ambient air pollution is well known to cause inflammatory change in respiratory epithelium and is associated with exacerbations of inflammatory conditions such as asthma and chronic obstructive pulmonary disease. However, limited work has been done on the impact of air pollution on pathogenesis of chronic rhinosinusitis and there are no reports in the literature of how pollutant exposure may impact sinonasal histopathology in patients with chronic rhinosinusitis. Objective This study aims to identify associations between certain histopathologic characteristics seen in sinus tissue of patients with chronic rhinosinusitis (CRS) and levels of particulate air pollution (PM2.5) and ground-level ozone in their place of residence. Methods A structured histopathology report was created to characterize the tissues of CRS patients undergoing sinus surgery. An estimate for each patient’s exposure to air pollutants including small particulate matter (PM2.5) and ground-level ozone was obtained using the Environmental Protection Agency’s (EPA) Environmental Justice Screening and Mapping Tool (EJSCREEN). Mean pollutant exposures for patients whose tissues exhibited varying histopathologic features were compared using logistic regression models. Results Data from 291 CRS patients were analyzed. Higher degree of inflammation was significantly associated with increased ozone exposure (p = 0.031). Amongst the patients with CRSwNP (n=131), presence of eosinophilic aggregates (p = 0.018) and Charcot-Leyden crystals (p = 0.036) was associated with increased ozone exposure. Conclusion Exposure to ambient air pollutants may contribute to pathogenesis of CRS. Increasing ozone exposure was linked to both higher tissue inflammation and presence of eosinophilic aggregates and Charcot-Leyden crystals in CRSwNP patients.

scholarly journals A machine learning-based study of the impact of traffic flow changes on air pollution combined with meteorological conditions: the case of Oslo

2021 ◽  
Author(s):  
Cong Cao
Keyword(s):  
Machine Learning ◽  
Air Pollution ◽  
Support Vector Machine ◽  
Traffic Flow ◽  
Arrival Time ◽  
Meteorological Conditions ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Traffic Volumes ◽  
The Impact

In this paper, we explore the impact of changes in traffic flow on local air pollution under specific meteorological conditions by integrating hourly traffic flow data, air pollution data and meteorological data, using generalized linear regression models and advanced machine learning algorithms: support vector machines and decision trees. The geographical location is Oslo, the capital of Norway, and the time we selected is from February 2020 to September 2020; We also selected 24-hour data for May 11 and 16 of the same year, representing weekday and holiday traffic flow, respectively, as a subset to further explore. Finally, we selected data from July 2020 for robustness testing, and algorithm performance verification.We found that: the maximum traffic flow on holidays is significantly higher than that on weekdays, but the holidays produce less concentration of {NO}_x throughout the month; the peak arrival time of {NO}_x,\ {NO}_2and NO concentrations is later than the peak arrival time of traffic flow. Among them, {NO}_x has a very significant variation, so we choose {NO}_x concentration as an air pollution indicator to measure the effect of traffic flow variation on air pollution; we also find that {NO}_xconcentration is negatively correlated with hourly precipitation, and the variation trend is like that of minimum air temperature. We used multiple imputation methods to interpolate the missing values. The decision tree results yield that when traffic volumes are high (&gt;81%), low temperatures generate more concentrations of {NO}_x than high temperatures (an increase of 3.1%). Higher concentrations of {NO}_x (2.4%) are also generated when traffic volumes are low (no less than 22%) but there is some precipitation ≥ 0.27%.In the evaluation of the prediction accuracy of the machine learning algorithms, the support vector machine has the best prediction performance with high R-squared and small MAE, MSE and RMSE, indicating that the support vector machine has a better explanation for air pollution caused by traffic flow, while the decision tree is the second best, and the generalized linear regression model is the worst.The selected data for July 2020 obtained results consistent with the overall dataset.

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