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Air Pollution - Wiley Online Library

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Last Updated: 08 August 2022

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Associations of air pollution with peripheral inflammation and cardiac autonomic physiology in children

Climate changeu2010 related tragedies have heightened concern about the effects of air pollution on health. The EPA obtained particulate matter near participants'u2019 homes. Autonomic balance, physiology, was indexed by pre-u2010ejection period, respiratory sinus arrhythmia, cardiac autonomic control, and cardiac autonomic regulation. PM2. 5 had a negative effect on ILu20108, according to a survey carried out by monthly PM2. 5. CAR was adversely affected by daily PM2. 5. Children's asthma may rise as a result of air pollution.

Source link: https://onlinelibrary.wiley.com/doi/10.1002/cad.20474


Applying machine learning for large scale field calibration of low‐cost PM2.5 and PM10 air pollution sensors

Low-cost air quality monitoring networks can significantly improve the availability of high-quality monitoring networks to provide analytic and evidence-u2010informed methods to better monitor air quality. However, low-cost air quality sensors are subjected to ambient conditions that can result in over-u2010 or underestimation of pollution levels, which may result in over-u2010 or underestimation of pollution levels, which would require field calibration to improve their accuracy and reliability. With an average BAM PM2. 5 concentration of 51. 1 g/m3 and an average BAM PM10 density of 51. 1 g/m3, the lasso regression model decreased RMSE of raw data from 18. 6 udf07g/m3 to 7. 9 g/m3 with an average BAM PM2. 5 gradient of 51. 1 g/m3 on a BAM PM10 ratio of 51. 1 g/m3 with an average BAM PM10 concentration of 51. 1 udf07g/m3udf07g/m3 udf07g/m3 udf07g/m3udf07g/m3 to 7. 9 udf07g/m3 to 7. 9 udf07g/m3udf07g/m3udf07g/m3udf07g/m3udf07g The resulting calibration models were applied to the entire large scale air quality monitoring network, which consisted of over 120 AirQo units, showing how machine learning techniques can be used to solve practical problems in a developing world context.

Source link: https://onlinelibrary.wiley.com/doi/10.1002/ail2.76


Association between air pollution and COVID‐19 disease severity via Bayesian multinomial logistic regression with partially missing outcomes

According to new scientific studies, air pollution exposure may raise susceptibility to and severity of coronavirus disease in 2019. To clarify the connection between air pollution exposure and COVID 201019 findings, individualu2010level studies are required. To deny partially inaccurate health outcomes, we develop a Bayesian multinomial logistic regression model with a multiple imputation strategy. In our review of 55,273 COVIDu201019 cases in Denver, Colorado, an increased risk of severe COVIDu201019 results was found. We also found COVID u201019 disease severity to be related to exposures.

Source link: https://onlinelibrary.wiley.com/doi/10.1002/env.2751


Air pollutant removal by four sidewalk tree species in the largest city in Taiwan

Human health is endangered by air pollutants, particularly in densely populated cities. In Taipei, Taiwan, we compared the interception of suspended particles and metal elements by four pedestrian tree species with different leaf surface wettability, leaf area, and phenology. Throughfall was more abundant in tree species with larger leaf areas. Despite higher rates of suspended particles in rainfall during the low leaf area period, the enrichment was higher in the high leaf area period, suggesting that leaf location was a key factor in canopy formation. Throughfall enrichment of suspended particles positively correlated with water content, indicating that air pollutants intercepted by tree canopies were not completely washed off by rainfall. Depending on our findings, leaf area and length of foliated period are key factors that influence canopy interception of particulate matter and related metal components, while leaf surface wettability is of secondary importance.

Source link: https://onlinelibrary.wiley.com/doi/10.1002/jeq2.20395


Regulation of Synoptic Circulation in Regional PM2.5 Transport for Heavy Air Pollution: Study of 5‐year Observation Over Central China

Large-scale synoptic circulation has influenced regional air pollution levels in the downstream region, which has been poorly understood for heavy air pollution. We investigated the Twain Hu Basin, a region of high PM2. 5 pollution over central China, for unbridled air pollution in the recent study of 5 year u2010-year analysis, in order to investigate the implementation of synoptic circulation controls governing regional PM2. 5 transportation for heavy air pollution. Three regional PM2. 5 transport pathways for the HPEs across central China were governed by three variations of synoptic circulation over CEC, with low air pressure to the north, high air pressure to the northeast, and low air pressure to the east. We then used Tu2010mode principal component analysis in conjunction with the Ku2010means cluster method to categorize synoptic circulations, and poor air pressure to the east - according to Tu2010means cluster method to categorize synoptic a za zaz regulated k cipcipit govt governed by three regional PM2. 5 transport governed respectively by v.

Source link: https://onlinelibrary.wiley.com/doi/10.1029/2021JD035937

* Please keep in mind that all text is summarized by machine, we do not bear any responsibility, and you should always check original source before taking any actions

* Please keep in mind that all text is summarized by machine, we do not bear any responsibility, and you should always check original source before taking any actions