Elemental composition of indoor and outdoor coarse particulate matter at an inner-city high school

Williamson, Kristin M.; Das, Sourav; Ferro, A.; Chellam, Shankararaman

doi:10.1016/j.atmosenv.2021.118559

Cited by 5 publications

(2 citation statements)

References 78 publications

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“…Particulate Matter (PM) is one of the main ingredients of air pollution. It can have a complex chemical composition and include things like heavy metals and carbon, which pose a fatal threat to human organs [ 1 , 2 ]. Short-term exposure to high concentrations of particulate pollutants lead to rises in heart rate and blood pressure and even cardiac arrest [ 3 – 5 ].…”

Section: Introductionmentioning

confidence: 99%

LASSO and attention-TCN: a concurrent method for indoor particulate matter prediction

Shi

et al. 2023

Appl Intell

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Long time exposure to indoor air pollution environments can increase the risk of cardiovascular and respiratory system damage. Most previous studies focus on outdoor air quality, while few studies on indoor air quality. Current neural network-based methods for indoor air quality prediction ignore the optimization of input variables, process input features serially, and still suffer from loss of information during model training, which may lead to the problems of memory-intensive, time-consuming and low-precision. We present a novel concurrent indoor PM prediction model based on the fusion model of Least Absolute Shrinkage and Selection Operator (LASSO) and an Attention Temporal Convolutional Network (ATCN), together called LATCN. First, a LASSO regression algorithm is used to select features from PM1, PM2.5, PM10 and PM (>10) datasets and environmental factors to optimize the inputs for indoor PM prediction model. Then an Attention Mechanism (AM) is applied to reduce the redundant temporal information to extract key features in inputs. Finally, a TCN is used to forecast indoor particulate concentration in parallel with inputting the extracted features, and it reduces information loss by residual connections. The results show that the main environmental factors affecting indoor PM concentration are the indoor heat index, indoor wind chill, wet bulb temperature and relative humidity. Comparing with Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) approaches, LATCN systematically reduced the prediction error rate (19.7% ~ 28.1% for the NAE, and 16.4% ~ 21.5% for the RMSE) and improved the model running speed (30.4% ~ 81.2%) over these classical sequence prediction models. Our study can inform the active prevention of indoor air pollution, and provides a theoretical basis for indoor environmental standards, while laying the foundations for developing novel air pollution prevention equipment in the future.

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Section: Introductionmentioning

confidence: 99%

LASSO and attention-TCN: a concurrent method for indoor particulate matter prediction

Shi

et al. 2023

Appl Intell

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“…Residential areas located near major highways also experience elevated PM exposure levels [31][32][33][34][35]. Other anthropogenic PM sources emanate from biomass burning, industrial activities, construction sites, mining, and other related crustal material combustion [14,19,29,[36][37][38][39][40][41].…”

Section: Introductionmentioning

confidence: 99%

Using Low-Cost Sensors to Assess PM2.5 Concentrations at Four South Texan Cities on the U.S.—Mexico Border

et al. 2022

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Low-cost sensors have been used considerably to characterize air pollution in the last few years. This study involves the usage of this technology for the first time to assess PM2.5 pollution at four cities on the U.S.–Mexico border. These cities in the Lower Rio Grande Valley Region of South Texas are Brownsville, Edinburg, Weslaco, and Port Isabel. A year-long sampling campaign was undertaken from 1 March 2021 to 31 March 2022. TSI BlueSky™ Air Quality Monitors were deployed concurrently at 11 different locations in these four cities. Twenty-four-hour PM2.5 concentrations from these sensors were then compared with ambient PM2.5 data available at the Texas Commission on Environmental Quality (TCEQ) Continuous Ambient Monitoring Station (CAMS) sites to elucidate spatial and temporal variability in the pollutant concentrations at the neighborhood level. The results indicate low to moderate spatial heterogeneity in the PM2.5 concentrations throughout the region. Our findings suggest that low-cost sensors in combination with CAMS sites have the potential to aid community monitoring for real-time spatiotemporal PM2.5 pollution patterns.

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Physicochemical assessment of atmospheric particulate matter emissions during open-pit mining operations in a massive sulphide ore exploitation

Boente

Millán-Martínez

Campa

et al. 2022

Atmospheric Pollution Research

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Elemental composition of indoor and outdoor coarse particulate matter at an inner-city high school

Cited by 5 publications

References 78 publications

LASSO and attention-TCN: a concurrent method for indoor particulate matter prediction

LASSO and attention-TCN: a concurrent method for indoor particulate matter prediction

Using Low-Cost Sensors to Assess PM2.5 Concentrations at Four South Texan Cities on the U.S.—Mexico Border

Physicochemical assessment of atmospheric particulate matter emissions during open-pit mining operations in a massive sulphide ore exploitation

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