Associations of combined short-term exposures to ambient PM2.5 air pollution and noise annoyance on mental health disorders: a panel study of healthy college students in Tehran

Azhdari, Seyyede Sara; Yunesian, Masud; Hassanvand, Mohammad Sadegh; Nodehi, Ramin Nabizadeh; Darvishali, Siamak; Faridi, Sasan; Shamsipour, Mansour

doi:10.1007/s11869-022-01199-8

Cited by 3 publications

(8 citation statements)

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“…Our regression results suggested that participants exposed to a severely compromised ambient environment (i.e., “strongly health-threatening” cluster) tended to be associated with more anxiety symptoms. This finding is in line with previous studies that had reported positive associations between anxiety and exposure to noise [ 45 , 46 ] and PM 2.5 [ 8 , 45 ], especially when the exposure levels were high [ 3 , 47 ]. Moreover, we also found that participants with a “moderately health-threatening” exposure pattern were associated with significantly fewer anxiety symptoms than those with a “strongly health-threatening” exposure pattern.…”

Section: Discussionsupporting

confidence: 93%

“…State-of-the-art mobility-based exposure assessments focus primarily on the health effects of aggregated exposures (e.g., daily or weekly exposure averages per person) [ 8 – 12 ]. However, such aggregation might average out short-term spatiotemporal variabilities in exposures, which likely have different health effects than exposure averages [ 13 ].…”

Section: Introductionmentioning

confidence: 99%

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Short-term exposure sequences and anxiety symptoms: a time series clustering of smartphone-based mobility trajectories

Lan,

Helbich

2023

Int J Health Geogr

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Background Short-term environmental exposures, including green space, air pollution, and noise, have been suggested to affect health. However, the evidence is limited to aggregated exposure estimates which do not allow the capture of daily spatiotemporal exposure sequences. We aimed to (1) determine individuals’ sequential exposure patterns along their daily mobility paths and (2) examine whether and to what extent these exposure patterns were associated with anxiety symptoms. Methods We cross-sectionally tracked 141 participants aged 18–65 using their global positioning system (GPS) enabled smartphones for up to 7 days in the Netherlands. We estimated their location-dependent exposures for green space, fine particulate matter, and noise along their moving trajectories at 10-min intervals. The resulting time-resolved exposure sequences were then partitioned using multivariate time series clustering with dynamic time warping as the similarity measure. Respondents’ anxiety symptoms were assessed with the Generalized Anxiety Disorders-7 questionnaire. We fitted linear regressions to assess the associations between sequential exposure patterns and anxiety symptoms. Results We found four distinctive daily sequential exposure patterns across the participants. Exposure patterns differed in terms of exposure levels and daily variations. Regression results revealed that participants with a “moderately health-threatening” exposure pattern were significantly associated with fewer anxiety symptoms than participants with a “strongly health-threatening” exposure pattern. Conclusions Our findings support that environmental exposures’ daily sequence and short-term magnitudes may be associated with mental health. We urge more time-resolved mobility-based assessments in future analyses of environmental health effects in daily life.

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Section: Discussionsupporting

confidence: 93%

Section: Introductionmentioning

confidence: 99%

Short-term exposure sequences and anxiety symptoms: a time series clustering of smartphone-based mobility trajectories

Lan,

Helbich

2023

Int J Health Geogr

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“…This strong correlation implies that there is a hidden pattern between the parameters, which can be useful during the model training process to improve prediction accuracy. Additionally, PM 2.5 is a well-established air pollutant that has been consistently associated with numerous adverse health effects [1]. Consequently, our objective was to estimate the concentration of PM 2.5 by leveraging meteorological parameters and historical pollution levels, as these factors exhibit strong correlations with PM 2.5 levels.…”

Section: A Study Areamentioning

confidence: 99%

“…After statistical analysis, an average of 63.20% of the PM 2.5 levels at the six monitoring sites were determined to be greater than the World Health Organization's (WHO) first interim threshold of 35 µg/m 3 . Exposure to this pollutant can have adverse health effects on both vulnerable and healthy individuals [1]. The health effects may include respiratory and cardiovascular problems, among others, and can range in severity depending on the level and duration of exposure.…”

Section: Spearman (ρ)mentioning

confidence: 99%

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CombineDeepNet: A Deep Network for Multistep Prediction of Near-Surface PM$_{2.5}$ Concentration

Dey,

Dev,

Phelan

2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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PM2.5 is a type of air pollutant that can cause respiratory and cardiovascular problems. Precise PM2.5 (µg/m 3 ) concentration prediction may help reduce health concerns and provide early warnings. To better understand the air pollution, a number of approaches have been presented for predicting PM2.5 concentrations. Previous research used deep learning models for hourly predictions of air pollutant due to their success in pattern recognition, however, these models were unsuitable for multi-site, long-term predictions, particularly in regards to the correlation between pollutants and meteorological data. This article proposes the combine deep network (CombineDeepNet), which combines multiple deep network, including a bidirectional long short-term memory (BiLSTM), bidirectional gated recurrent unit (BiGRU), and a shallow model represented by fully connected layers (FC), to create a hybrid forecasting system. It can effectively capture the complex relationships between air pollutants and various influencing factors to predict hourly PM2.5 concentrations in multiple monitoring sites based in China. The best root mean square error (RMSE) achieved was 22.0 µg/m 3 (long-term) and 6.2 µg/m 3 (short-term), with mean absolute error (MAE) values of 3.4 µg/m 3 (long-term) and 2.2 µg/m 3 (short-term). Additionally, the correlation coefficient (R 2 ) reached 0.96 (longterm) and 0.83 (short-term) across six monitoring sites. These results demonstrate that CombineDeepNet enhances prediction accuracy compared to popular deep learning methods. Therefore, CombineDeepNet proves to be a important framework for predicting PM2.5 concentration.

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Mental health impacts of environmental exposures: A scoping review of evaluative instruments

Baker,

Barlow,

Daniel

et al. 2024

Science of The Total Environment

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Associations of combined short-term exposures to ambient PM2.5 air pollution and noise annoyance on mental health disorders: a panel study of healthy college students in Tehran

Cited by 3 publications

References 50 publications

Short-term exposure sequences and anxiety symptoms: a time series clustering of smartphone-based mobility trajectories

Short-term exposure sequences and anxiety symptoms: a time series clustering of smartphone-based mobility trajectories

CombineDeepNet: A Deep Network for Multistep Prediction of Near-Surface PM$_{2.5}$ Concentration

Mental health impacts of environmental exposures: A scoping review of evaluative instruments

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