Correlation Of Air Pollution Research Articles

Weighting analysis on air pollution among recent years in China via grey theory models

The priority of national development has always been economic development during the recent decades in China, so a lot of energy consumption was needed to support rapid economic development, accompanied by a number of different air pollution emissions, including SO2, NO2 CO, and PM2.5. Based on awareness of environmental protection and sustainable energy development, researchers aimed to propose strategies to reduce the amount of air pollution generation in China derived from intense industrialization. The calculation of the correlation coefficient showed high values, so the correlation of air pollutants is highly related. This study aimed to realize the outcome of the implementation of air pollution reduction acts. Therefore, the grey relational grade and grey entropy were used to analyze the air pollution trend from 2013 to 2020 due to the specific function of the grey theory. The results of this study showed that the curves of SO2, PM2.5, and CO showed a clear decreasing trend. Furthermore, the SO2 curve still decreased in the periods of 2018–2020. The most important index for air pollutants is SO2 after the calculation of grey entropy, which corresponds to the fact that the coal resource is the main energy source in the fossil fuel industries for economic growth. However, the effects of these implementations regarding air pollution reduction sufficiently control air pollution emission, especially SO2 emission.

Correlation of Air Pollution and Prevalence of Acute Pulmonary Embolism in Northern Thailand.

Background: The relationship between the level of air pollution and acute pulmonary embolism (APE) has had inconsistent results. Objective: This study aimed to analyze the relationship between the high level of air pollution exposure and APE. Methods: A ten-year retrospective cohort, single-center study was performed on patients diagnosed with APE from October 2010 to December 2020. The association between air pollution and monthly APE case diagnosis was analyzed. Results: A total number of 696 patients was included. The effect of every 10 µg/m3 increment of particulate matters with an aerodynamic diameter < 10 µm (PM10) on total monthly APE cases (unprovoked PE and provoked PE) was increased significantly at lag 4, 5 and 6 months with adjusted RR (95% CI) of 1.06 (1.01, 1.12), p = 0.011, 1.07 (1.01, 1.13), p = 0.021 and 1.06 (1.01, 1.12), p = 0.030, respectively. Adjusted RR for APE was significantly increased for PM10 in the second tertile ((adjusted RR (95% CI) 1.76 (1.12, 2.77)), p = 0.014. Conclusions: We conclude that PM10 is associated with an increased prevalence of APE cases. The policy for tighter control of air pollution in our country is needed to reduce the impact of air pollutants on people’s health.

Retraction Note to: Correlation of air pollutants and prediction of physical fitness index based on wireless sensor network

Retraction Note to: Correlation of air pollutants and prediction of physical fitness index based on wireless sensor network

Air pollution increased risk of reproductive system diseases: a 5-year outcome analysis of different pollutants in different seasons, ages, and genders.

Air pollution remains a serious environmental problem worldwide, and the effects of air pollutants with reproductive system diseases have already attracted extensive attention. The present study investigated the risk of air pollutants on reproductive system diseases, based on daily medical visits (DMV) of the past 5 years in central China. Data of DMV outpatients with reproductive system diseases were obtained from a general hospital in Zhengzhou, October 28, 2013 to May 31, 2018, as well as atmospheric pollutants data. Correlation of air pollutants and DMV was analyzed with distributed lag nonlinear model (DLNM), including total cases of reproductive system diseases, and in different seasons (spring, summer, autumn, and winter), genders (male and female), and age groups (<26, 26-35, and >35 years old). A total of 374,558 visits were included. NO2 was most closely relevant to incidence risk of total cases analysis with each increased interquartile ranges (IQRs) in the 6 pollutants, with 30-day lag. Relationship to pollutants was more sensitive in fall, >35 years old, and male groups than in other seasons, ages, and females, and NO2 had the highest risk on reproductive diseases. Air pollution increased risk of reproductive system diseases, and different pollutants played different roles in different seasons, ages, and genders. The results of this study will provide evidence for effective air quality controlling and human reproductive protection.

RETRACTED ARTICLE: Correlation of air pollutants and prediction of physical fitness index based on wireless sensor network

This paper studies the correlation between different data of urban pollutants. It is pointed out that the identification way of the relationship between the environmental pollutants in the surrounding area and the urban environmental pollutants, the geography of the surrounding area, the location and the corresponding wind direction correspond to each other. In addition, it is judged and studied according to the wireless sensor network and physical indicators, so as to understand the correlation between the environmental pollution sources. The information gathered through fitness trackers assumes a significant job in improving wellbeing and the prosperity of the individuals who wear them. There is additionally a progression of direct correlations of a few wellbeing markers from various wellness trackers. In this investigation, we thought about the quantity of steps, calories consumed, and 3 miles of wellness tracker gathered in a free day to day environment over a time of 14 days. It shows our work that the quantity of steps announced by wearing distinctive specialized gadgets at the same time can vary by as much as 26%. At the same time, the same trend is based on counting steps in the mutants seen at a distance traveled. A small correlation is found between the number of calories burned and the observed change in the number of steps between multiple devices. Our findings show their health indicators as calorie burn reports and miles run and rely heavily on the manufacturer’s proprietary algorithms for the device itself and data such as calculations and inferences.

Ambient air pollution and cardiovascular disease rate an ANN modeling: Yazd-Central of Iran

This study was aimed to investigate the air pollutants impact on heart patient's hospital admission rates in Yazd for the first time. Modeling was done by time series, multivariate linear regression, and artificial neural network (ANN). During 5 years, the mean concentrations of PM10, SO2, O3, NO2, and CO were 98.48 μg m−3, 8.57 ppm, 19.66 ppm, 18.14 ppm, and 4.07 ppm, respectively. The total number of cardiovascular disease (CD) patients was 12,491, of which 57% and 43% were related to men and women, respectively. The maximum correlation of air pollutants was observed between CO and PM10 (R = 0.62). The presence of SO2 and NO2 can be dependent on meteorological parameters (R = 0.48). Despite there was a positive correlation between age and CD (p = 0.001), the highest correlation was detected between SO2 and CD (R = 0.4). The annual variation trend of SO2, NO2, and CO concentrations was more similar to the variations trend in meteorological parameters. Moreover, the temperature had also been an effective factor in the O3 variation rate at lag = 0. On the other hand, SO2 has been the most effective contaminant in CD patient admissions in hospitals (R = 0.45). In the monthly database classification, SO2 and NO2 were the most prominent factors in the CD (R = 0.5). The multivariate linear regression model also showed that CO and SO2 were significant contaminants in the number of hospital admissions (R = 0.46, p = 0.001) that both pollutants were a function of air temperature (p = 0.002). In the ANN nonlinear model, the 14, 12, 10, and 13 neurons in the hidden layer were formed the best structure for PM, NO2, O3, and SO2, respectively. Thus, the Rall rate for these structures was 0.78–0.83. In these structures, according to the autocorrelation of error in lag = 0, the series are stationary, which makes it possible to predict using this model. According to the results, the artificial neural network had a good ability to predict the relationship between the effect of air pollutants on the CD in a 5 years' time series.

Seasonal variation in indoor concentrations of air pollutants in residential buildings

ABSTRACT Indoor concentrations of PM10, PM2.5, CO, and CO2 were measured in 25 naturally ventilated urban residences during the winter and summer seasons in Alexandria, Egypt. Ambient air samples were also collected simultaneously for comparison to indoor measurements. Furthermore, data for air exchange rates, home characteristics, and indoor activities during sampling were collected. It was found that the average indoor PM10, PM2.5, CO, and CO2 concentrations for all homes in winter were 119.4 ± 30.9 μg/m3, 85.2 ± 25.8 μg/m3, 1.6 ± 0.8 ppm, and 692.4 ± 144.6 ppm, respectively. During summer, the average indoor levels were 98.8 ± 21.8 μg/m3, 67.8 ± 14.9 μg/m3, 0.5 ± 0.5 ppm, and 558.2 ± 66.2 ppm, respectively. The results indicate that the indoor daily averages of PM10 and PM2.5 concentrations were higher than the World Health Organization (WHO) guidelines for all selected homes in the two sampling periods. For CO and CO2 levels, the indoor daily averages for all monitored homes were less than the WHO guideline and the American National Standards Institute/American Society of Heating, Refrigerating and Air-Conditioning Engineers, Inc. (ANSI/ASHRAE) Standard 62.1, respectively. A strong seasonal variability was observed, with air quality being particularly poor in winter. Due to increased ventilation rates in summer, indoor levels of air pollutants were strongly dependent on ambient levels, while in winter the indoor concentrations were more strongly affected by indoor sources due to increased human activities and poor ventilation. In addition, stronger indoor/outdoor correlation of air pollutants’ levels was found in summer than in winter probably due to higher ventilation and infiltration in the summer. The study also attempted to understand the potential sources and the various determinants that influence indoor PM, CO, and CO2 concentrations in the two seasons. The findings can assist policymakers to better understand the indoor air pollution problem and to provide a sound basis for the development of proper national IAQ standards in Egypt. Implications: Personal exposure is considerably influenced by indoor air pollution which increases health risks. Assessment of indoor air quality has become a more significant issue in Egypt as people tend to spend most of their time inside buildings, especially in their homes. Currently, there is a lack of research on residential indoor air quality in Egyptian cities in terms of the spatial and temporal variation which prevents an accurate assessment of the current situation to develop effective mitigation measures and to establish national indoor air quality standards. This article is considered the first research studying the effect of seasonality on indoor concentrations of PM10, PM2.5, CO, and CO2 in urban residences in Alexandria. It also studies the indoor/outdoor relationship of air pollutants’ levels and identifies their major sources as well as the various determinants that influence their indoor concentrations.

Correlation between air pollution and hospitalization due to myocardial infarction.

BACKGROUNDAir pollution is associated with increased risk of cardiovascular disease (CVD). This study aims to evaluate the correlation between air pollutants and hospitalization due to myocardial infarction (MI) as part of "correlation of air pollution with hospitalization and mortality of CVDs and respiratory diseases (CAPACITY) study".METHODSThis case-crossover study analyzed the data of 319 patients who were admitted with diagnosis of ST-elevation MI (STEMI) or non-ST-elevation MI (NSTEMI) in three main hospitals of Isfahan, Iran. The data of airborne pollutants including particulate matter < 10 µm (PM10), particulate matter < 2.5 µm (PM2.5), nitrogen dioxide (NO2), sulfur dioxide (SO2), carbon monoxide (CO), and ozone (O3) as well as climatic indices (temperature, wind speed, and humidity) at 24 hours, 48 hours, and one week before admission were extracted from CAPACITY study. The conditional logistic regression method was used to evaluate the correlation between air pollutants and MI hospitalization.RESULTS319 patients with mean age of 63.15 ± 28.14 years, including 238 men (74.6%), and 207 patients with STEMI (64.8%) were recruited. The risk of hospitalization significantly increased in patients with STEMI and 10-unit increment in PM2.5 at 48 hours before admission [odds ratio (OR) = 3.70, 95% confidence interval (CI): 1.69-7.69]. Although, majority of air pollutants had positive association with hospitalization in patients with NSTEMI, they were not statistically significant.CONCLUSIONThis study showed significant association between elevated PM2.5 at 48 hours before admission and hospitalization of patients with STEMI. This finding can warn policymakers to design better care services for patients at risk of acute MI during the times of increased air pollution.

Association between ambient air pollution and hospitalization caused by atrial fibrillation.

BACKGROUNDMany studies have shown the worst effects of air pollution on cardiovascular diseases (CVDs). Present study focused on the relationship between atrial fibrillation (AF), as one of the common arrhythmias, and air pollutants in Isfahan, Iran, an industrial city in the Middle East.METHODSA case-crossover design was used to explore the associations between air pollution and AF hospitalized patients with ventricular response (VR) > 90 beats per minute (bpm) (fast response) and those with VR ≤ 90 bpm. All patients' records were extracted from their hospital files. Air pollutants data including particulate matter less than 10 µ (PM10), PM2.5, carbon monoxide (CO), nitrogen dioxide (NO2), sulfur dioxide (SO2), and ozone (O3) were obtained from the Correlation of Air Pollution with Hospitalization and Mortality of Cardiovascular and Respiratory Diseases (CAPACITY) study. Conditional logistic regression test was used to measure the relationship between pollutants and hospitalization due to AF.RESULTSRecords of 369 patients, including 173 men (46.9%) who were hospitalized for AF during the study period and had complete data were extracted. Although a positive but not statistically significant relationship was shown between 10-unit increases in all pollutants (except PM10) and the hospitalization due to AF in patients with rapid VR (RVR), the only significant relationship was observed in case of NO2 [odds ratio (OR) = 1.26, 95% confidence interval (CI) = 1.0-2.1, P = 0.031].CONCLUSIONThis study showed positive significant relationships between NO2 and the hospitalization due to AF in patients with RVR. NO2 is a greenhouse gas whose levels are expected to increase due to global environmental changes. Therefore, relevant strategies should be adopted to decrease its levels, especially in industrial cities like Isfahan.

Reply to Little et al.: dose–responses from multi-model inference for the non-cancer disease mortality of atomic bomb survivors

We appreciate the comment by Little et al. (2013) related to the study of Schollnberger et al. (2012) and thank our colleagues for the time they took to deal with our analysis. Little et al. (2013) state ‘‘... there are biological data suggesting [that] many inflammatory endpoints potentially relevant to circulatory disease may be differentially regulated below and above about 0.5 Gy, emphasizing the importance of assessing risks associated with exposures \0.5 Gy.’’ We agree with the statement and take it as a support of our approach to analyze the available data with a variety of models that take several possible dose dependences into account. Little et al. (2013) express a concern ‘‘... that the method of Schollnberger et al. 2012 (multi-model inference, MMI) may not adequately assess the uncertainties in model parameters’’ without elucidating reasons of their concern. However, seemingly to backup their concern, they cite Wang et al. (2012), a paper describing the development of a novel approach to ‘‘adjustment uncertainty’’ (i.e., the uncertainty about which variables should be included in the model to properly adjust for confounding), called Bayesian adjustment for confounding. We will argue below that the pre-conditions on which Wang et al. (2012) built their interesting, though not uncontested (Gutman and Rubin 2012), methodology do not apply to our analysis. When the effect of exposure on outcome is estimated, a proper adjustment for confounding variables is a general concern in epidemiology. A series of papers by Wang et al. (2012), Crainiceanu et al. (2008) and Dominici et al. (2004) addressed this problem for the correlation of air pollution and mortality. A big challenge in air pollution epidemiology is to control for possible confounding by changes in weather parameters such as temperature or humidity, which determine both the concentration of particulate matter in air and have a direct impact on mortality rates, especially among older persons and those with preexisting health conditions. Hence, such covariables are clearly correlated with exposure and outcome and can be considered as true confounders. In our study of the association between circulatory diseases and ionizing radiation, we have adjusted the risk for the main covariables of city c, sex s, age at exposure e and attained age a, for which the correlation with the radiation dose is generally small: see bottom row of the correlation matrix (Table 1), which was calculated from the raw data of LSS Report 13 with follow-up since January 1, 1968 (Preston et al. 2003), the data set that has been analyzed by Schollnberger et al. (2012). Additional adjustment for other covariables such as smoking, alcohol intake, education, type of household occupation, obesity and diabetes mellitus ‘‘had almost no impact on the associations with radiation’’ (see Table 3 and Discussion in Shimizu et al. (2010)). We have not calculated the correlation with exposure for the latter covariables but we expect again small correlations similar to those for c, s, e and a. H. Schollnberger (&) J. C. Kaiser P. Jacob Helmholtz Zentrum Munchen, Department of Radiation Sciences, Institute of Radiation Protection, 85764 Neuherberg, Germany e-mail: schoellnberger@helmholtz-muenchen.de

Correlation of air pollutants with land use and traffic measures in Tehran, Iran: A preliminary statistical analysis for land use regression modeling

Land use regression (LUR) models have been globally used to estimate long-term air pollution exposures. The present study aimed to analyze the association of different land use types and traffic measures with air pollutants in Tehran, Iran, as part of the future development of LUR models. Data of the particulate matter (PM10), sulfur dioxide (SO2), and nitrogen dioxide (NO2) were extracted from 23 Tehran’s air quality monitors for 2010. The data of different land use types and traffic measures within the circular buffer radii 100 to 1000 meters and distances to them were calculated using Geographic Information System (GIS). Thereafter, the association of the mentioned air pollutants was evaluated with land use types and traffic measures. The annual average concentrations of PM10, SO2 and NO2 were 100.8 µg/m3, 38 parts per billion (ppb), and 53.2 ppb, respectively. The PM10 was associated with transportation area, other areas, and with distance to the other nearest land use (P < 0.05). The SO2 concentration was associated with official or commercial land use, and with other area land use (P < 0.05). Noteworthy, the NO2 concentration was associated with official or commercial land use, and with other areas (P < 0.05). The air pollutant concentrations was analyzed with different land use types and traffic measures as a preliminary work for development of LUR models in Tehran. It is hoped these analyses lead to successful development of LUR models in the near future.

Correlation of air pollutants with land use and traffic measures in Tehran, Iran: A preliminary statistical analysis for land use regression modeling

Land use regression (LUR) models have been globally used to estimate long-term air pollution exposures. The present study aimed to analyze the association of different land use types and traffic measures with air pollutants in Tehran, Iran, as part of the future development of LUR models. Data of the particulate matter (PM10), sulfur dioxide (SO2), and nitrogen dioxide (NO2) were extracted from 23 Tehran’s air quality monitors for 2010. The data of different land use types and traffic measures within the circular buffer radii 100 to 1000 meters and distances to them were calculated using Geographic Information System (GIS). Thereafter, the association of the mentioned air pollutants was evaluated with land use types and traffic measures. The annual average concentrations of PM10, SO2 and NO2 were 100.8 µg/m3, 38 parts per billion (ppb), and 53.2 ppb, respectively. The PM10 was associated with transportation area, other areas, and with distance to the other nearest land use (P < 0.05). The SO2 concentration was associated with official or commercial land use, and with other area land use (P < 0.05). Noteworthy, the NO2 concentration was associated with official or commercial land use, and with other areas (P < 0.05). The air pollutant concentrations was analyzed with different land use types and traffic measures as a preliminary work for development of LUR models in Tehran. It is hoped these analyses lead to successful development of LUR models in the near future.

Correlation of Air Pollutants and Thermal Environment Factors in a Confined Pig House in Winter

Optimal management of indoor air quality in a confined pig house, especially in winter, is indispensable for preventing infectious respiratory disease to workers and animals. This study was performed to elucidate the correlation of aerial contaminants and climate factors in a confinement. It was observed that indoor air contaminants in the confinement was the highest at 2:00-5:00 pm in a day, followed by 8:00-11:00 pm and 8:00-11:00 am. This was attributed to the increase of pig activities in the afternoon. The concentration of total dust and total airborne bacteria was found to have a significant correlation with temperature and relative humidity (p<0.05). Correlation of total dust and total airborne bacteria, total dust and ammonia, and total dust and odor were shown statistically significant at 95% confidence level. In conclusion, temperature and total dust concentration correlated significantly with all the parameters except for hydrogen sulfide (H 2 S). This could be explained by the fact the dryness of pig feces by increase of interior temperature and resuspension of feed deposited on the floor by the pig activity, resulted in high generation of dust which adsorbed and carried the airborne bacteria and odor compounds in a confined pig house. It was proved that the adsorptive capacity of dust with ammonia (NH 3 ) was higher than that with hydrogen sulfide (H 2 S).

05/00450 Correlation of air pollution and meteorological data using neural networks: Slini, T. et al. International Journal of Environment and Pollution, 2003, 20, (1–6), 218–229

05/00450 Correlation of air pollution and meteorological data using neural networks: Slini, T. et al. International Journal of Environment and Pollution, 2003, 20, (1–6), 218–229

Correlation of air pollution and meteorological data using neural networks

In order to develop an environmental forecasting tool, the Neural Network method of computational intelligence is investigated. For this purpose, hourly and daily time series of CO, NO2 and O3, as well as a variety of meteorological variables are employed in various multi-layer percepton (MLP) models, in order to provide reliable air quality forecasts, using as a test case the city of Athens, Greece. The performance of the two most satisfactory models are presented thoroughly and compared using certain statistical indices. Results verify both the potential and the complicated nature of the method.