Estimating daily high-resolution PM2.5 concentrations over Texas: Machine Learning approach

Ghahremanloo, Masoud; Choi, Yunsoo; Sayeed, Alqamah; Salman, Ahmed Khan; Pan, Shuai; Amani, Meisam

doi:10.1016/j.atmosenv.2021.118209

Cited by 46 publications

(23 citation statements)

References 71 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Advances in machine learning (ML) algorithms have enabled more accurate estimations of the surface concentrations of pollutants. Previous studies have applied various ML and deep learning (DL) algorithms to estimate surface concentrations of pollutants (Chen et al., 2019; Ghahremanloo, Choi, et al., 2021; Park et al., 2020; Xu et al., 2020). Ghahremanloo, Choi, et al.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Estimation of Daily Ground‐Level NO₂ Concentrations From Remote Sensing Data

et al. 2021

Self Cite

View full text Add to dashboard Cite

A number of studies have reported that nitrogen dioxide (NO 2 ) is strongly associated with premature mortality (Abdolahnejad et al., 2018;Crouse et al., 2015) and several adverse health effects such as respiratory-related ailments (Weinmayr et al., 2010;Zhu et al., 2019) and cardiovascular diseases (Liu et al., 2015). Despite the high precision of NO 2 point measurements at ground stations, they are limited in number, thus restricting the accurate examination of changes in surface NO 2 concentrations, especially in urban environments, where the spatial distribution of pollutants changes dramatically. As this problem lowers the

show abstract

Section: Introductionmentioning

confidence: 99%

Deep Learning Estimation of Daily Ground‐Level NO₂ Concentrations From Remote Sensing Data

et al. 2021

Self Cite

View full text Add to dashboard Cite

show abstract

“…Third, IAQ measurements were not conducted during the whole year and seasonal and temporal changes could not be addressed. In South Texas, the PM 2.5 levels were highest in summer, due to the hot and humid climate, and lowest in winter, and residents may experience more symptoms in summer and less in winter [65]. However, the seasonal difference could be excluded by assessing and comparing IAQ and health complaints in the same season.…”

Section: Discussionmentioning

confidence: 99%

Indoor Air Quality and Health Outcomes in Employees Working from Home during the COVID-19 Pandemic: A Pilot Study

et al. 2021

View full text Add to dashboard Cite

Indoor air quality (IAQ) has a substantial impact on public health. Since the beginning of the COVID-19 pandemic, more employees have worked remotely from home to minimize in-person contacts. This pilot study aims to measure the difference in workplace IAQ before and during the pandemic and its impact on employees’ health. The levels of fine particulate matter (PM2.5) and total volatile organic chemicals (tVOC) were measured in the employees’ offices before the COVID-19 pandemic and at homes while working from home during the pandemic using Foobot air monitors. The frequencies of six sick building syndrome (SBS) symptoms were evaluated at each period of monitoring. The result showed PM2.5 levels in households while working from home were significantly higher than in offices while working at the office for all participants (p < 0.05). The PM2.5 levels in all households exceeded the health-based annual mean standard (12 µg/m3), whereas 90% of offices were in compliance. The tVOC levels were all below the standard (500 µg/m3). We also found a higher frequency of SBS symptoms were observed while working from home as the IAQ was worse at home. This study suggested that working from home might have a detrimental health impact due to poor IAQ and providing interventions to remote employees should be considered.

show abstract

“…More machine learning algorithms and deep learning algorithms are being used in the study of atmospheric pollutants, which brings more references to carry out trend prediction and aggregation sensing of VOCs, such as k-nearest neighbour (KNN) [19], random forest (RF) [20], multilayer perceptron (MLP) [21], long short-term memory (LSTM) [22,23], convolutional neural networks (CNN) [24] and chemical transport models (CTMs) [25]. PM 2.5 , PM 10 , O 3 , NO 2 , SO 2 and CO have more research results as common air pollutants.…”

Section: Introductionmentioning

confidence: 99%

Regional VOCs Gathering Situation Intelligent Sensing Method Based on Spatial-Temporal Feature Selection

et al. 2022

View full text Add to dashboard Cite

As VOCs pose a threat to human health, it is important to accurately capture changes in VOCs concentrations and sense VOCs concentrations in relevant areas. Therefore, it is necessary to improve the accuracy of VOCs concentration prediction and realise the VOCs aggregation situation sensing. Firstly, on the basis of regional grid division, the inverse distance spatial interpolation method is used for spatial interpolation to collect regional VOCs data information. Secondly, extreme gradient boosting (XGBoost) is used for spatio-temporal feature selection, combined with graph convolutional neural network (GCN) to construct regional spatial relationships of VOCs, and multiple linear regression (MLR) to process VOCs time series data and predict the VOCs concentration in the grid. Finally, the aggregation potential values of VOCs are calculated based on the prediction results, and the potential perception results are visualised. A VOCs aggregation perception method based on concentration prediction is proposed, using the XGBoost-GCN-MLR method with a scenario-aware approach for VOCs to perceive the VOCs aggregation in the relevant region. VOCs concentration prediction and VOCs aggregation trend perception were carried out in Xi’an, Baoji, Tongchuan, Weinan and Xianyang. The results show that compared with the GCN model, XGBoost model, MLR model and GCN-MLR model, the XGBoost-GCN-MLR model reduces the input variables, achieves the optimisation of the input parameters of the VOCs concentration prediction model, reduces the complexity of the prediction model and improves the prediction accuracy. Intelligent sensing of VOCs aggregation can visualise the regional VOCs. The intelligent sensing of VOCs aggregation can visualise the development trend and status of regional VOCs aggregation and convey more information, which has practical value.

show abstract

Estimating daily high-resolution PM2.5 concentrations over Texas: Machine Learning approach

Cited by 46 publications

References 71 publications

Deep Learning Estimation of Daily Ground‐Level NO₂ Concentrations From Remote Sensing Data

Deep Learning Estimation of Daily Ground‐Level NO₂ Concentrations From Remote Sensing Data

Indoor Air Quality and Health Outcomes in Employees Working from Home during the COVID-19 Pandemic: A Pilot Study

Regional VOCs Gathering Situation Intelligent Sensing Method Based on Spatial-Temporal Feature Selection

Contact Info

Product

Resources

About

Estimating daily high-resolution PM2.5 concentrations over Texas: Machine Learning approach

Cited by 46 publications

References 71 publications

Deep Learning Estimation of Daily Ground‐Level NO2 Concentrations From Remote Sensing Data

Deep Learning Estimation of Daily Ground‐Level NO2 Concentrations From Remote Sensing Data

Indoor Air Quality and Health Outcomes in Employees Working from Home during the COVID-19 Pandemic: A Pilot Study

Regional VOCs Gathering Situation Intelligent Sensing Method Based on Spatial-Temporal Feature Selection

Contact Info

Product

Resources

About

Deep Learning Estimation of Daily Ground‐Level NO₂ Concentrations From Remote Sensing Data

Deep Learning Estimation of Daily Ground‐Level NO₂ Concentrations From Remote Sensing Data