Satellite-based short- and long-term exposure to PM2.5 and adult mortality in urban Beijing, China

Liang, Fengchao; Xiao, Qingyang; Gu, Dongfeng; Xu, Meimei; Tian, Lin; Guo, Qun; Wu, Ziting; Liu, Yang

doi:10.1016/j.envpol.2018.06.097

Cited by 53 publications

(28 citation statements)

References 42 publications

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“…2,3 The people's health, vegetation, agriculture and building materials are harmed in the Los Angeles Smog in 1930s, so people must use protective equipment to avoid being affected. [4][5][6][7] To make people avoid air pollution in advance, China's environment department has also formulated a large number of measures and policies. 8 The prediction of PM 2.5 concentration is the first research topic at present.…”

Section: Introductionmentioning

confidence: 99%

Atmospheric PM_2.5 concentration prediction and noise estimation based on adaptive unscented Kalman filtering

Wang

et al. 2021

Measurement and Control

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Due to the randomness and uncertainty in the atmospheric environment, and accompanied by a variety of unknown noise. Accurate prediction of PM2.5 concentration is very important for people to prevent injury effectively. In order to predict PM2.5 concentration more accurately in this environment, a hybrid modelling method of support vector regression and adaptive unscented Kalman filter (SVR-AUKF) is proposed to predict atmospheric PM2.5 concentration in the case of incorrect or unknown noise. Firstly, the PM2.5 concentration prediction model was established by support vector regression. Secondly, the state space framework of the model is combined with the adaptive unscented Kalman filter method to estimate the uncertain PM2.5 concentration state and noise through continuous updating when the model noise is incorrect or unknown. Finally, the proposed method is compared with SVR-UKF method, the simulation results show that the proposed method is more accurate and robust. The proposed method is compared with SVR-UKF, AR-Kalman, AR and BP methods. The simulation results show that the proposed method has higher prediction accuracy of PM2.5 concentration.

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Section: Introductionmentioning

confidence: 99%

Atmospheric PM_2.5 concentration prediction and noise estimation based on adaptive unscented Kalman filtering

Wang

et al. 2021

Measurement and Control

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“…Fine particulate matter (PM 2.5 ) is the particulate matter with diameter equal to or less than 2.5 μm and has become a serious threat to human health as a number of epidemiological studies have demonstrated marked association between PM 2.5 exposure and increased incidence and aggravation of respiratory and cardiovascular diseases 1, 2. Once inhaled, PM 2.5 deposits in lung tissues and diffuses in blood inducing lung and systematic injuries 3, 4.…”

Section: Introductionmentioning

confidence: 99%

PM_2.5 Upregulates MicroRNA-146a-3p and Induces M1 Polarization in RAW264.7 Cells by Targeting Sirtuin1

Zhong¹,

Liao²,

Hu³

et al. 2019

Int. J. Med. Sci.

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Background: Fine particulate matter (PM2.5) exposure is proved to be associated with illnesses, but the mechanism is not clear. Potential effects of PM2.5 on innate immunity have become a hotspot recently. Confronting PM2.5, macrophages are able to be activated and induce inflammatory responses. Whether PM2.5 exposure affects macrophage polarization and associated mechanisms remains to be further explored. Afterwards, whether Sirtuin1 (SIRT1) an important intermediate regulator in various physiological processes takes part in the macrophage polarization induced by PM2.5 is unknown. MiRNAs are acknowledged as key regulator in posttranscriptional modification and our previous study found that miR-146a is a novel biomarker of PM2.5 exposure. Thus, we propose a hypothesis, PM2.5 exposure induces M1 polarization and miR-146a-3p is a potential upstream regulator by targeting SIRT1.Methods: RAW264.7 cells were treated with different concentrations of PM2.5 for 24h. The expressions of cytokines and key molecular markers were detected by qRT-PCR, Western blotting and ELISA. The activation degree of TLRs and NF-κB was assessed by Western blotting. The specific agonist and antagonist of SIRT1 were used to explore the potential role of SIRT1 in M1 polarization induced by PM2.5. MiR-146a-3p mimic and inhibitor were pre-transfected into RAW264.7 cells and the effects on M1 polarization induced by PM2.5 were evaluated. Luciferase analysis was used to identify the binding site of miR-146a-3p and SIRT1.Results: PM2.5 increased the mRNA and protein expression of M1 markers including interleukin-6 (IL-6), tumor necrosis factor alpha (TNF-α) and inducible nitric oxide synthase (iNOS) in RAW264.7 cells. The protein level of TLR4 was significantly increased and the ratio of phosphorylated NF-κB p65 versus p65 subunit was also elevated in PM2.5 group. PM2.5 decreased the protein level of SIRT1 but not the mRNA expression in vitro and in vivo experiments. Pre-treatment with SIRT1 agonist SRT1720 rescued the PM2.5 induced M1 response. Whereas, SIRT1 antagonist EX527 augment the effect. MiR-146a-3p was upregulated in PM2.5 treated RAW264.7 cells. Luciferase experiments reported that SIRT1 was directly targeted by miR-146a-3p. Overexpression of miR-146a-3p downregulated the expression of SIRT1 protein in untreated RAW264.7 cells. Importantly, inhibition of miR-146a-3p upregulated SIRT1 protein and suppressed M1 polarization in PM2.5 treated RAW264.7 cells.Conclusions: These results suggested that PM2.5 induces the inflammatory M1 polarization and TLR4/NF-κB signal transduction pathway might be involved in the process. MiR-146a-3p is a novel regulator of PM2.5 exerted M1 polarization by targeting SIRT1.

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“…There is a big difference in respiratory rate under different conditions, thus the effect of the respiratory rate on exposure should be considered. Long-term respiratory volume could be calculated by Equation (5) [36,37,38]: IRL=BMR×E×VQ×A1000 where IR L is long-term respiratory volume (m 3 /d), BMR is basal metabolic rate (kJ/d), E is oxygen consumption by unit energy metabolic (giving us 0.05 L/kJ), VQ is the dimensionless ventilation equivalent of 27 here, and A is the coefficient of long-term respiratory volume. BMR and A of different sex and age groups can be found in technical specifications according to height and weight.…”

Section: Resultsmentioning

confidence: 99%

Contributions of Indoor and Outdoor Sources to Ozone in Residential Buildings in Nanjing

Huang

Yang

Gao

2019

IJERPH

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Ozone has become one of the most serious air pollutants in China in recent years. Since people spend most of their time indoors, the ozone in the indoor environment could be a major factor affecting the occupants’ health. The indoor ozone in residential buildings mainly comes from two sources: outdoor atmosphere and indoor ozone produced by electrical devices. In this study, a typical residence in Nanjing was taken as an example to calculate and compare the contributions of indoor and outdoor sources to ozone in the building. A questionnaire survey about the type, the placement, and the frequency of use of the ozone emission devices was performed to provide the basis for the settings of indoor ozone sources. The multi-zone software CONTAM was used hourly to simulate the ozone concentration in summer and in winter with inner doors either closed or open, and it was noted whether there were ozone emission devices indoors or not. Source contribution was quantified and compared by three methods in this paper: (1) the average indoor/outdoor (I/O) ratio, (2) the I/O ratio frequency, and (3) the ratio of indoor ozone concentration without ozone sources to that with ozone sources. The results showed that the contribution of outdoor sources was much greater than that of indoor sources in summer, but in winter, the frequency of I/O > 1 could reach 55.8% of the total seasonal time, and the ratio of indoor ozone concentration without sources to that with sources could reach as high as 74.3%. This meant that the indoor concentration had the potential to exceed the outdoor. Furthermore, human respiratory exposure in different ages and genders was calculated. It was found that teenagers aged 10–18 years old and female adults had a higher respiratory exposure level.

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Satellite-based short- and long-term exposure to PM2.5 and adult mortality in urban Beijing, China

Cited by 53 publications

References 42 publications

Atmospheric PM_2.5 concentration prediction and noise estimation based on adaptive unscented Kalman filtering

Atmospheric PM_2.5 concentration prediction and noise estimation based on adaptive unscented Kalman filtering

PM_2.5 Upregulates MicroRNA-146a-3p and Induces M1 Polarization in RAW264.7 Cells by Targeting Sirtuin1

Contributions of Indoor and Outdoor Sources to Ozone in Residential Buildings in Nanjing

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Satellite-based short- and long-term exposure to PM2.5 and adult mortality in urban Beijing, China

Cited by 53 publications

References 42 publications

Atmospheric PM2.5 concentration prediction and noise estimation based on adaptive unscented Kalman filtering

Atmospheric PM2.5 concentration prediction and noise estimation based on adaptive unscented Kalman filtering

PM2.5 Upregulates MicroRNA-146a-3p and Induces M1 Polarization in RAW264.7 Cells by Targeting Sirtuin1

Contributions of Indoor and Outdoor Sources to Ozone in Residential Buildings in Nanjing

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Atmospheric PM_2.5 concentration prediction and noise estimation based on adaptive unscented Kalman filtering

Atmospheric PM_2.5 concentration prediction and noise estimation based on adaptive unscented Kalman filtering

PM_2.5 Upregulates MicroRNA-146a-3p and Induces M1 Polarization in RAW264.7 Cells by Targeting Sirtuin1