Average Air Pollution Concentrations Research Articles

Associations of exposure to surrounding green, air pollution and traffic noise with mortality in a large national health survey

PDS 70: Green space, Johan Friso Foyer, Floor 1, August 28, 2019, 1:30 PM - 3:00 PM Background/Aim: Most previous studies investigated associations of mortality with air pollution, traffic noise and surrounding green focusing on single exposures. We evaluated associations of combined long-term residential exposures to surrounding green, air pollution and traffic noise with non-accidental mortality in a large health survey. Methods: A national health survey, conducted in 2012, covering personal characteristics, lifestyle and socio-economic status (SES), was linked to the Dutch longitudinal mortality database. Subjects of the survey who were 30 years or older on 1 January 2013 (n=339,633) were followed from 1 January 2013 to 31 December 2017. Subjects with missing lifestyle data were more likely to die during follow-up. We therefore applied multiple imputation of missing values using chained equations to impute missing values for potential confounders. We used Cox proportional hazard models to evaluate associations of residential surrounding green (the average Normalized Difference Vegetation Index (NDVI) in buffers of 300m and 1000m), annual average air pollutant concentrations (including particulate matter (PM10, PM2.5), NO2) and traffic noise with mortality. Results: In the 5-year follow-up, we observed 26,886 non-accidental deaths. After adjustment for individual SES, lifestyle factors, such as physical activity and smoking status, and neighborhood SES, we found no significant associations with non-accidental mortality for any of the exposures. For example, an interquartile range (IQR, 0.13) increase in NDVI 300m was associated with a HR of 0.99 (95% CI: 0.98, 1.01) in natural mortality, an IQR (7.6 µg/m3) increase in NO2 was associated with a HR of 0.99 (95% CI: 0.97, 1.01) and an IQR (7.4 dB) increase in road-traffic noise was associated with a HR of 0.99 (95% CI: 0.97, 1.01). Conclusion: We found no evidence for effects of long-term residential exposures to surrounding green, air pollution and traffic noise with non-accidental mortality in a large population based survey in the Netherlands.

Ambient air pollution exposure and obesity-related traits in Korean adults.

PurposeAlthough some studies have tried to determine the impact of long-term air pollution exposure on obesity, they have mainly focused on body mass index (BMI) and the results are inconsistent. Therefore, we investigated the association of annual ambient air pollution exposure with various obesity traits, including computed tomography-measured abdominal fatness, in a large Korean adult population.Patients and methodsA total of 5,114 participants who underwent routine health check-ups at Seoul National University Hospital were included in the analysis. We calculated the annual average concentrations of ambient air pollutants, such as particulate matter ≤10 μm in diameter (PM10) and nitrogen dioxide (NO2), using the individual’s zip code. Obesity-related indicators included the BMI, waist circumference (WC), percent body fat (PBF), total adipose tissue (TAT), visceral adipose tissue (VAT), and subcutaneous adipose tissue (SAT).ResultsThe mean age of the population was 53.5 and 70.9% were men. The mean annual concentrations of PM10 and NO2 were 49.4 μg/m3 and 30.3 ppb, respectively. In the full covariates model, adjusted for demographic and clinical variables, interquartile range increase in annual average concentration of PM10 and NO2 was not associated with any obesity-related phenotypes including BMI, WC, PBF, TAT, VAT, and SAT (all P>0.05). Likewise, no significant association between air pollutants and obesity-related traits was observed in any subgroups, stratified by sex and age (all P>0.05).ConclusionAnnual exposure to ambient air pollution is not associated with any obesity-related traits in Korean adults.

A comparison of linear regression, regularization, and machine learning algorithms to develop Europe-wide spatial models of fine particles and nitrogen dioxide

Empirical spatial air pollution models have been applied extensively to assess exposure in epidemiological studies with increasingly sophisticated and complex statistical algorithms beyond ordinary linear regression. However, different algorithms have rarely been compared in terms of their predictive ability.This study compared 16 algorithms to predict annual average fine particle (PM2.5) and nitrogen dioxide (NO2) concentrations across Europe. The evaluated algorithms included linear stepwise regression, regularization techniques and machine learning methods. Air pollution models were developed based on the 2010 routine monitoring data from the AIRBASE dataset maintained by the European Environmental Agency (543 sites for PM2.5 and 2399 sites for NO2), using satellite observations, dispersion model estimates and land use variables as predictors. We compared the models by performing five-fold cross-validation (CV) and by external validation (EV) using annual average concentrations measured at 416 (PM2.5) and 1396 sites (NO2) from the ESCAPE study. We further assessed the correlations between predictions by each pair of algorithms at the ESCAPE sites.For PM2.5, the models performed similarly across algorithms with a mean CV R2 of 0.59 and a mean EV R2 of 0.53. Generalized boosted machine, random forest and bagging performed best (CV R2~0.63; EV R2 0.58–0.61), while backward stepwise linear regression, support vector regression and artificial neural network performed less well (CV R2 0.48–0.57; EV R2 0.39–0.46). Most of the PM2.5 model predictions at ESCAPE sites were highly correlated (R2 > 0.85, with the exception of predictions from the artificial neural network). For NO2, the models performed even more similarly across different algorithms, with CV R2s ranging from 0.57 to 0.62, and EV R2s ranging from 0.49 to 0.51. The predicted concentrations from all algorithms at ESCAPE sites were highly correlated (R2 > 0.9). For both pollutants, biases were low for all models except the artificial neural network. Dispersion model estimates and satellite observations were two of the most important predictors for PM2.5 models whilst dispersion model estimates and traffic variables were most important for NO2 models in all algorithms that allow assessment of the importance of variables.Different statistical algorithms performed similarly when modelling spatial variation in annual average air pollution concentrations using a large number of training sites.

NOx-VOC-O3 Sensitivity in Urban Environments of Sri Lanka

Physical phenomenon of the relation among the ground level O3, NOx and VOC governed by complex nonlinear photochemistry in urban environments is explained in detail using the ambient pollutant concentration data of eleven cities in Sri Lanka. The time-series analysis was conducted using the 24-hour average ambient concentrations of PM10, NO2, CO, O3 and SO2 air pollutants obtained from fixed air pollution monitoring station located in Colombo since 2008. Further analysis was carried out from the mobile air pollution monitoring station for eleven cities. The hourly averaged ambient real time air quality data i.e. VOC, NO2, NO, O3 pollutants and the corresponding meteorological parameters were analyzed and presented in weekly results for the base year 2013, 2014 and 2015. It was identified that there exist two regimes of NOx-VOC-O3 sensitivity among these cities. Colombo, Kurunegala, Jaffna, Matara, Badulla, Pollonnaruwa, and Gampaha are the NOx-sensitive regime. While Rathnapura, Anuradhapura, Kandy and Nuwaraelliya are the VOC-sensitive regime. In the NOx-sensitive regime (with relatively low NOx and high VOC), O3 increases with the increasing NOx and slightly changes in response to the increasing VOC levels. In the NOx-saturated or VOC-sensitive regime, O3 decreases with increasing NOx level and increases with increasing VOC levels. In the immediate vicinity of very large emissions of NO, O3 concentrations are depressed through the process of NOx titration. Mathematical relationships were developed to calculate the steady state ozone concentration (O3ss) that gives the values for both NOx-sensitive regime and the VOC-sensitive regime. Establishment of these relationships are essential for Sri Lanka to develop the appropriate interventions for controlling O3 pollution in each city.

Associations of combined exposures to surrounding green, air pollution and traffic noise on mental health

BackgroundEvidence is emerging that poor mental health is associated with the environmental exposures of surrounding green, air pollution and traffic noise. Most studies have evaluated only associations of single exposures with poor mental health. ObjectivesTo evaluate associations of combined exposure to surrounding green, air pollution and traffic noise with poor mental health. MethodsIn this cross-sectional study, we linked data from a Dutch national health survey among 387,195 adults including questions about psychological distress, based on the Kessler 10 scale, to an external database on registered prescriptions of anxiolytics, hypnotics & sedatives and antidepressants. We added data on residential surrounding green in a 300 m and a 1000 m buffer based on the Normalized Difference Vegetation Index (NDVI) and a land-use database (TOP10NL), modeled annual average air pollutant concentrations (including particulate matter (PM10, PM2.5), and nitrogen dioxide (NO2)) and modeled road- and rail-traffic noise (Lden and Lnight) to the survey. We used logistic regression to analyze associations of surrounding green, air pollution and traffic noise exposure with poor mental health. ResultsIn single exposure models, surrounding green was inversely associated with poor mental health. Air pollution was positively associated with poor mental health. Road-traffic noise was only positively associated with prescription of anxiolytics, while rail-traffic noise was only positively associated with psychological distress. For prescription of anxiolytics, we found an odds ratio [OR] of 0.88 (95% CI: 0.85, 0.92) per interquartile range [IQR] increase in NDVI within 300 m, an OR of 1.14 (95% CI: 1.10, 1.19) per IQR increase in NO2 and an OR of 1.07 (95% CI: 1.03, 1.11) per IQR increase in road-traffic noise. In multi exposure analyses, associations with surrounding green and air pollution generally remained but attenuated. Joint odds ratios [JOR], based on the Cumulative Risk Index (CRI) method, of combined exposure to air pollution, traffic noise and decreased surrounding green were higher than the ORs of single exposure models. Associations of environmental exposures with poor mental health differed somewhat by age. ConclusionsStudies including only one of these three correlated exposures may overestimate the influence of poor mental health attributed to the studied exposure, while underestimating the influence of combined environmental exposures.

Comparison of Statistical Methods, Neural Network, and Fuzzy Neural for Particular Matter Prediction: A Case Study in Mashhad, Iran

Background: In the recent era, air pollution is a major global concern that affects human health. The emission proportion of air pollutants is increased in many cities of Iran such as Mashhad. Particular matters (i.e. PM2.5) are one of the five air pollutants known to be responsible for polluting the air in Mashhad. Nowadays, fuzzy neural intelligent systems, which are capable of solving nonlinear and complex problems, are widely used in the air pollution problem to determine the amount of the particles and dust in the air. Methods: In the current study, the air quality data consisting of daily average concentrations of air pollutants and the meteorological data including the minimum temperature, precipitation, humidity, wind direction, and daily wind speed recorded by city monitoring stations from 2011 to 2017. The daily average pollutants concentration was used to study the relationship between PM2.5 and the other air pollutants such SO2, O3, NO2, PM10, and CO. SPSS was used for data analysis. Linear regression, multilayer perceptron (MLP) neural network, and fuzzy neural network using MATLAB 2017 software were employed for modeling. Performance of the models was evaluated using root mean square error (RMSE) and coefficient of determination (R2). Results: Based on the obtained results, among the employed models, MLP neural network with R2 = 0.598, RMSE = 0.088, and MSE = 0.0079 was better than linear regression, and the ANFIS model combining particle swarm optimization (PSO) algorithm with R2 = 0.804, RMSE = 0.055, and MSE = 0.0031 had the best performance in the prediction of PM2.5. Conclusions: The ANFIS network correctly fitted more than 80% of total data; given that there were non-linear and complicated models in meteorological systems, this figure can indicate the high strength of ANFIS network through PSO-based combinational training methods for modeling nonlinear data.

Is Long-term Ambient Air Pollutant Exposure a Risk Factor for Irritable Bowel Syndrome in Children? A 12-year Longitudinal Cohort Study.

Background/AimsRecent studies suggest that air pollution may play a role in gastrointestinal disorders. However, the effect of long-term exposure to air pollution on childhood irritable bowel syndrome (IBS) is unclear. Hence, we conducted a nationwide cohort study to investigate the association between long-term air pollution exposure and the incidence and risk of IBS in Taiwanese children during 2000–2012.MethodsWe collected data from the Taiwan National Health Insurance Research Database, linked to the Taiwan Air Quality-Monitoring Database according to the insurant living area and the air quality-monitoring station locations. Children < 18 years old, identified from January 1st, 2000, were followed-up until IBS diagnosis or December 31st, 2012. The daily average air pollutant concentrations were categorized into 4 quartile-based groups (Q1–Q4). We measured the incidence rate, hazard ratios (HRs), and 95% confidence intervals for IBS stratified by the quartiles of air pollutant concentration.ResultsA total of 3537 children (1.39%) were diagnosed with IBS within the cohort during the follow-up period. The incidence rate for IBS increased from 0.84 to 1.76, from 0.73 to 1.68, from 0.85 to 1.98, and from 0.52 to 3.22 per 1000 person-years, with increase in the carbon monoxide, nitrogen dioxide, non-methane hydrocarbon, and methane quartile (from Q1 to Q4) exposure concentration, respectively. The adjusted HR for IBS increased with elevated carbon monoxide, nitrogen dioxide, non-methane hydrocarbon, and methane exposure in Q4 to 1.98, 2.14, 2.19, and 5.87, respectively, compared with Q1.ConclusionLong-term ambient air pollutant exposure is an environmental risk factor for childhood IBS.

Interactions between ambient air pollutants and temperature on emergency department visits: Analysis of varying-coefficient model in Guangzhou, China

BackgroundAt present, there are few studies on the effect of short-term interactions between ambient air pollutants and temperature on cause-specific emergency department visits in China. This study aimed to explore their short-term interactions on cause-specific emergency department visits using data collected from a total of 65 public hospitals in Guangzhou city, south China. Material and methodsWe included a total of 226,443 emergency department visits which were diagnosed as neurological, respiratory and circulatory disease in Guangzhou from January 1, 2014 to December 31, 2017. Average daily concentrations of air pollutants including carbon monoxide (CO), particulate matter having a median diameter of 2.5 μm or less (PM2.5), sulfur dioxide (SO2), nitrogen dioxide (NO2) and ozone (O3) were collected from the Guangzhou Environmental Protection Bureau. We employed quasi-Poisson varying coefficient regression models to assess the interaction effects between air pollutants and daily temperature levels (DTLs) on emergency department visits for neurological, respiratory and circulatory diseases, respectively. ResultsAverage number of emergency department visits for neurological, respiratory and circulatory diseases were 92, 26 and 38, respectively. After controlling for other pollutants, meteorological factors and other time-varying confounders, we found the interactions between NO2 and the 1st DTL (3.4–17.1 °C), NO2 and the 2nd DTL (17.1–23.5 °C) for neurological emergency department visits were statistically significant, displaying a nonlinear relationship. Additionally, we found that the interactions between SO2 and the 4th DTL (27.4–31.1 °C) also had a significantly adverse effect on respiratory emergency department visits. ConclusionsOur findings provide novel evidence on SO2-by-temperature interactions, and NO2-by-temperature interactions for emergency department visits of cause-specific diseases.

High resolution annual average air pollution concentration maps for the Netherlands

Long-term exposure to air pollution is considered a major public health concern and has been related to overall mortality and various diseases such as respiratory and cardiovascular disease. Due to the spatial variability of air pollution concentrations, assessment of individual exposure to air pollution requires spatial datasets at high resolution. Combining detailed air pollution maps with personal mobility and activity patterns allows for an improved exposure assessment. We present high-resolution datasets for the Netherlands providing average ambient air pollution concentration values for the year 2009 for NO2, NOx, PM2.5, PM2.5absorbance and PM10. The raster datasets on 5×5 m grid cover the entire Netherlands and were calculated using the land use regression models originating from the European Study of Cohorts for Air Pollution Effects (ESCAPE) project. Additional datasets with nationwide and regional measurements were used to evaluate the generated concentration maps. The presented datasets allow for spatial aggregations on different scales, nationwide individual exposure assessment, and the integration of activity patterns in the exposure estimation of individuals.

Characterizing population exposure to coal emissions sources in the United States using the HyADS model

In anticipation of the expanding appreciation for air quality models in health outcomes studies, we develop and evaluate a reduced-complexity model for pollution transport that intentionally sacrifices some of the sophistication of full-scale chemical transport models in order to support applicability to a wider range of health studies. Specifically, we introduce the HYSPLIT average dispersion model, HyADS, which combines the HYSPLIT trajectory dispersion model with modern advances in parallel computing to estimate ZIP code level exposure to emissions from individual coal-powered electricity generating units in the United States. Importantly, the method is not designed to reproduce ambient concentrations of any particular air pollutant; rather, the primary goal is to characterize each ZIP code's exposure to these coal power plants specifically. We show adequate performance towards this goal against observed annual average air pollutant concentrations (nationwide Pearson correlations of 0.88 and 0.73 with SO42− and PM2.5, respectively) and coal-combustion impacts simulated with a full-scale chemical transport model and adjusted to observations using a hybrid direct sensitivities approach (correlation of 0.90). We proceed to provide multiple examples of HyADS's single-source applicability, including to show that 22% of the population-weighted coal exposure comes from 30 coal-powered electricity generating units.

Bacterial Community Specification in PM2.5 in Different Seasons in Xinxiang, Central China

In China, air pollution has become a significant environmental threat to human health in recent years. Airborne bacteria are critical constituents of microbial aerosols, which contain numerous pathogens. However, the effects of seasonal variations, environmental factors such as air pollution, and meteorological factors on microbial diversity are poorly understood. In this study, fine particulate matter (PM2.5) samples (n = 12) were collected using a high-volume air sampler over 24-hour periods during all four seasons from April 2017 to January 2018. Concurrently, the average daily concentrations of various air pollutants and the meteorological conditions were monitored. High-throughput sequencing of 16s rRNA was then employed to profile PM2.5 bacterial communities. The results showed that the bacterial communities varied significantly by season. Proteobacteria (35.5%), Firmicutes (23.0%), and Actinobacteria (16.2%) were the most abundant bacterial phyla in the PM2.5 samples. At the genus level, the diversity of the bacterial communities was significantly correlated with the ozone (O3) concentration (r = 0.920, p = 0.001) and air temperature (T) (r = 0.534, p = 0.023). The results of this study can be used as a reference by other bioaerosol research that focuses on the health effects of atmospheric particulate matter.

The impact of outdoor air pollutants on outpatient visits for respiratory diseases during 2012\u20132016 in Jinan, China

BackgroundFew studies have investigated the associations between outdoor air pollution and outpatient visits for respiratory diseases (RDs) in general population.MethodsWe collected daily outpatient data of primary RDs from five hospitals in Jinan during January 2012 and December 2016, as well as daily measurements of air pollutants from the Jinan Environmental Monitoring Center and daily meteorological variables from the China Meteorological Data Sharing Service System. A generalized additive model (GAM) with quasi-Poisson regression was constructed to estimate the associations between daily average concentrations of outdoor air pollutants (PM2.5,PM10, SO2, NO2, CO and O3) and daily outpatient visits of RDs after adjusting for long-time trends, seasonality, the “day of the week” effect, and weather conditions. Subgroup analysis stratified by gender, age group and the type of RDs was conducted.ResultsA total of 1,373,658 outpatient visits for RDs were identified. Increases of 10 μg/m3 in PM2.5, PM10, NO2, CO and O3 were associated with0.168% (95% CI, 0.072–0.265%), 0.149% (95% CI, 0.082–0.215%), 0.527% (95% CI, 0.211–0.843%), 0.013% (95% CI, 0.003–0.023%), and 0.189% (95% CI, 0.032–0.347%) increases in daily outpatient visits for RDs, respectively. PM2.5 and PM10 showed instant and continuous effects, while NO2, CO and O3 showed delayed effects on outpatient visits for RDs. In stratification analysis, PM2.5 and PM10 were associated with acute RDs only.ConclusionsExposure to outdoor air pollutants including PM2.5, PM10, NO2, CO and O3 associated with increased risk of outpatient visits for RDs.

Abstract 17321: Long-Term Exposures to Air Pollutants and Risk of Peripheral Arterial Occlusive Disease, a Nationwide 10-Year Follow-Up Cohort Study in Taiwan

Objective: Many studies had reported the association between air pollution and cardiovascular diseases. In this current study, we aimed to evaluate the risk of long-term exposure to air pollution (PM2.5, CO and NO2) to the development of PAOD. Methods: We linked two nation-scale databases, the national health insurance database and the air quality-monitoring database of Taiwan to conduct this cohort study between 2003 and 2013. Cox proportional regression with time-dependent model was used to evaluate the hazard ratio (HR) of air pollutants to PAOD. The residential areas were divided into four categories according to the daily average concentration of air pollutants, Q1 to Q4 (Q4 = the worst). The cumulative incidence of PAOD was examined by the Kaplain-Meier analysis with log-rank test. Results: A total of 1,598 PAOD cases were identified during the follow-up period with 98,540 non-PAOD ones (demographic characteristics were showed in Table 1). In the multivariate analysis, after adjusting for age, gender, urbanization level, residential area, baseline comorbidities, and drug use, the adjusted HRs were that PM2.5 = 1.14 (95% CI 1.13-1.16), NO2 = 1.03 (95% CI 1.02-1.04), and CO = 2.35 (95% CI 1.95-2.84), respectively (Table 2). The Kaplain-Meier analysis showed that CO concentration was strongly associated with cumulative incidence rate of PAOD during the follow-up period (Figure 1). Conclusion: Besides of well-known risk factors, air pollutants may also play a potential role in PAOD pathogenesis.

Exposure to ambient air pollution and blood lipids in adults: The 33 Communities Chinese Health Study

BackgroundLittle information exists on the lipidemic effects of air pollution, particularly in developing countries. We aimed to investigate the associations of long-term exposure to ambient air pollutants with lipid levels and dyslipidemias in China. MethodsIn 2009, a total of 15,477 participants aged 18–74 years were recruited from the 33 Communities Chinese Health Study conducted in three Northeastern China cities. Total cholesterol (TC), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), and low-density lipoprotein cholesterol (LDL-C) were measured in participants' blood specimens. Three year (2006–08) average air pollution concentrations were assessed using data from 33 communities (particles with diameters ≤1.0 μm (PM1) and ≤2.5 μm (PM2.5) were predicted using a spatial statistical model) or 11 air monitoring stations (particles with diameters ≤10 μm (PM10), sulfur dioxide (SO2), nitrogen dioxide (NO2), and ozone (O3)). Associations were evaluated by two-level logistic and generalized linear regression models. ResultsWe detected many significant associations between exposure to air pollutants (especially for PM1 and PM2.5) and blood lipid levels. Most of the associations suggested deleterious effects on blood lipid markers (e.g., a 10 μg/m3 increase in PM1 was associated with 1.6% (95% confidence interval (CI): 1.1, 2.0), 2.9% (95% CI: −3.3, 9.3), and 3.2% (95% CI: 2.6, 3.9) higher levels of TC, TG, and LDL-C, respectively, but 1.4% (95% CI: −1.8, −0.9) lower HDL-C levels), although beneficial associations were found for O3. In analysis with dyslipidemias, all the observed associations suggested deleterious lipidemic effects of air pollutants, and no significant beneficial association was observed for O3. Stratified analyses showed that the associations were stronger in overweight or obese participants; sex and age modified the associations, but the pattern of effects was mixed. ConclusionsLong-term ambient air pollution was associated with both altered lipid profiles and dyslipidemias, especially among overweight or obese participants.

Air pollution and airway resistance at age 8\xa0years \u2013 the PIAMA birth cohort study

BackgroundAir pollution has been found to adversely affect children’s lung function. Forced expiratory volume in 1 s and forced vital capacity from spirometry have been studied most frequently, but measurements of airway resistance may provide additional information. We assessed associations of long-term air pollution exposure with airway resistance.MethodsWe measured airway resistance at age 8 with the interrupter resistance technique (Rint) in participants of the Dutch PIAMA birth cohort study. We linked Rint with estimated annual average air pollution concentrations [nitrogen oxides (NO2, NOx), PM2.5 absorbance (“soot”), and particulate matter < 2.5 μm (PM2.5), < 10 μm (PM10) and 2.5–10 μm (PMcoarse)] at the birth address and current home address (n = 983). Associations between air pollution exposure and interrupter resistance (Rint) were assessed using multiple linear regression adjusting for potential confounders.ResultsWe found that higher levels of NO2 at the current address were associated with higher Rint [adj. mean difference (95% confidence interval) per interquartile range increase in NO2: 0.018 (0.001, 0.035) kPa·s·L− 1]. Similar trends were observed for the other pollutants, except, PM10. No association was found between Rint and exposure at the birth address.ConclusionsOur results support the hypothesis that air pollution exposure is associated with a lower lung function in schoolchildren.

Association of meteorological factors and air NO2 and O3 concentrations with acute exacerbation of elderly chronic obstructive pulmonary disease

We studied the combined effect of air pollutant concentrations and meteorological factors [e.g., temperature and atmospheric pressure (AP)] on the acute exacerbation of coronary obstructive pulmonary disease (COPD) in 277 older patients with COPD (240 men and 37 women; average age, 75.3 ± 9.3 years). Average air pollutant concentrations, AP, temperature, and relative humidity corresponding to each of the 7 days before the date of hospitalisation were identified as the case and the two other weekly averages, 4 and 8 weeks prior to admission, were considered the controls. During the warming-up season, COPD exacerbation more likely occurred on days of temperature increase or AP decrease than on other days. Increments in CO, NO2 and O3 concentrations were significantly associated with 5%, 11% and 4% increases in COPD exacerbation risks, respectively. During the cooling-down season, increments in PM10 concentrations were significant risk factors; the exacerbation likely occurred on days of temperature decreases than on other days. Air pollution with increased NO2, CO, O3 and PM10 concentrations and continual temperature changes (colder during cooling-down seasons or hotter during warning-up seasons) were associated with acute exacerbation of COPD in older patients.

Influence of Air Pollution on Hospital Admissions in Adult Asthma in Northeast China

Background:Asthma is a common chronic respiratory disease and is related to air pollution exposure. However, only a few studies have concentrated on the association between air pollution and adult asthma. Moreover, the results of these studies are controversial. Therefore, the present study aimed to analyze the influence of various pollutants on hospitalization due to asthma in adults.Methods:A total of 1019 unrelated hospitalized adult asthma patients from Northeast China were recruited from 2014 to 2016. Daily average concentrations of air pollutants (particulate matter <2.5 μm [PM2.5], particulate matter <10 μm [PM10], sulfur dioxide [SO2], nitrogen dioxide [NO2], and carbon monoxide [CO]) were obtained from the China National Environmental Monitoring Centre website from 2014 to 2016. Cox logistic regression analysis was used to analyze the relationship between air pollutants and hospital admissions in adult asthma.Results:The maximum odds ratio (OR) value for most air pollutants occurred on lag day 1. Lag day 1 was chosen as the exposure period, and 8 days before onset was chosen as the control period. Three pollutants (PM2.5, CO, and SO2) were entered into the regression equation, and the corresponding OR (95% confidence interval) was 0.995 (0.991–0.999), 3.107 (1.607–6.010), and 0.979 (0.968–0.990), respectively.Conclusions:A positive association between hospital admissions and the daily average concentration of CO was observed. CO is likely to be a risk factor for hospital admissions in adults with asthma.

Effect of Air Pollution on the Cold Disease in Shanghai

This study discusses the changes in the daily average concentrations of the main air pollutants, such as PM10, PM2.5, O3, and NO2, in Shanghai, and the effects of air pollution on cold in Shanghai. For this study, data on air pollutants, meteorological factors, and the number of daily hospital visits from cold in Shanghai were collected from January 1, 2008 to December 31, 2010. Using the time series Poisson semi-parametric generalized additive model, and controlling for the long-term trend, "week" effect, and meteorological factors by smoothing the spline function, the exposure-response relationship between air pollution and human health in Shanghai was analyzed. The study sets up the model according to age, evaluating the impact and the lag effect of air pollution on the number of daily hospital visits. Results show that when PM10, NO2, O3, and PM2.5 increase by an IQR, the relative risk of cold disease is 1.0240 (1.0233-1.0246), 1.0206 (1.0201-1.0212), 0.9393 (0.9384-0.9402), and 1.0080 (1.0069-1.0086), and when PM10, NO2, O3, and PM2.5 increase by 10 μg·m-3, the daily hospital visits increase by 0.5%, 1.0%, -2.0%, and 0.2%. In the multi-polluted model, the results of NO2 and PM2.5 are basically lower compared to the results of the single-pollutant model, the results of PM10 and O3are higher. Air pollution in Shanghai has an impact on the incidence of cold disease.

Statistical Analysis of Spatiotemporal Heterogeneity of the Distribution of Air Quality and Dominant Air Pollutants and the Effect Factors in Qingdao Urban Zones

Air pollution has impacted people’s lives in urban China, and the analysis of the distribution and driving factors behind air quality has become a current research focus. In this study, the temporal heterogeneity of air quality (AQ) and the dominant air pollutants across the four seasons were analyzed based on the Kruskal-Wallis rank-sum test method. Then, the spatial heterogeneity of AQ and the dominant air pollutants across four sites were analyzed based on the Wilcoxon signed-rank test method. Finally, the copula model was introduced to analyze the effect of relative factors on dominant air pollutants. The results show that AQ and dominant air pollutants present significant spatiotemporal heterogeneity in the study area. AQ is worst in winter and best in summer. PM10, O3, and PM2.5 are the dominant air pollutants in spring, summer, and winter, respectively. The average concentration of dominant air pollutants presents significant and diverse daily peaks and troughs across the four sites. The main driving factors are pollutants such as SO2, NO2, and CO, so pollutant emission reduction is the key to improving air quality. Corresponding pollution control measures should account for this heterogeneity in terms of AQ and the dominant air pollutants among different urban zones.

Acute effects of air pollution on respiratory disease mortalities and outpatients in Southeastern China

The objective of this study was to investigate the potential association between air pollutants and respiratory diseases (RDs). Generalized additive models were used to analyze the effect of air pollutants on mortalities or outpatient visits. The average concentrations of air pollutants in Hangzhou (HZ) were 1.6–2.8 times higher than those in Zhoushan (ZS), except for O3. In a single pollutant model, the increased concentrations of PM2.5, NO2, and SO2 were strongly associated with deaths caused by RD in HZ, while PM2.5 and O3 were associated with deaths caused by RD in ZS. All air pollutants (PM2.5, NO2, SO2, and O3) were strongly associated with outpatient visits for RD in both HZ and ZS. In multiple pollutant models, a significant association was only observed between PM2.5 and the mortality rate of RD patients in both HZ and in ZS. Moreover, strong associations between SO2, NO2, and outpatient visits for RD were observed in HZ and ZS. This study has provided evidence that both the mortality rates and outpatient visits for RD were significantly associated with air pollutants. Furthermore, the results showed that different air pollutant levels lead to regional differences between mortality rates and outpatient visits.