Dataset Information

New Deep Learning Model to Estimate Ozone Concentrations Found Worrying Exposure Level over Eastern China.

ABSTRACT: Ozone (O₃), whose concentrations have been increasing in eastern China recently, plays a key role in human health, biodiversity, and climate change. Accurate information about the spatiotemporal distribution of O₃ is crucial for human exposure studies. We developed a deep learning model based on a long short-term memory (LSTM) network to estimate the daily maximum 8 h average (MDA8) O₃ across eastern China in 2020. The proposed model combines LSTM with an attentional mechanism and residual connection structure. The model employed total O₃ column product from the Tropospheric Monitoring Instrument, meteorological data, and other covariates as inputs. Then, the estimates from our model were compared with real observations of the China air quality monitoring network. The results indicated that our model performed better than other traditional models, such as the random forest model and deep neural network. The sample-based cross-validation R² and RMSE of our model were 0.94 and 10.64 μg m^-3, respectively. Based on the O₃ distribution over eastern China derived from the model, we found that people in this region suffered from excessive O₃ exposure. Approximately 81% of the population in eastern China was exposed to MDA8 O₃ > 100 μg m^-3 for more than 150 days in 2020.

SUBMITTER: Wang S

PROVIDER: S-EPMC9223487 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature

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New Deep Learning Model to Estimate Ozone Concentrations Found Worrying Exposure Level over Eastern China.

Wang Sichen S Mu Xi X Jiang Peng P Huo Yanfeng Y Zhu Li L Zhu Zhiqiang Z Wu Yanlan Y

International journal of environmental research and public health 20220611 12

Ozone (O3), whose concentrations have been increasing in eastern China recently, plays a key role in human health, biodiversity, and climate change. Accurate information about the spatiotemporal distribution of O3 is crucial for human exposure studies. We developed a deep learning model based on a long short-term memory (LSTM) network to estimate the daily maximum 8 h average (MDA8) O3 across eastern China in 2020. The proposed model combines LSTM with an attentional mechanism and residual conne ...[more]

PMID: 35742435

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Project description:During May 2016 a very large boreal wildfire burned throughout the Athabasca Oil Sands Region (AOSR) in central Canada, and in close proximity to an extensive air quality monitoring network. This study examines speciated 24-h integrated polycyclic aromatic hydrocarbon (PAH) and volatile organic compound (VOC) measurements collected every sixth day at four and seven sites, respectively, from May to August 2016. The sum of PAHs (ΣPAH) was on average 17 times higher in fire-influenced samples (852 ng m-3, n = 8), relative to non-fire influenced samples (50 ng m-3, n = 64). Diagnostic PAH ratios in fire-influenced samples were indicative of a biomass burning source, whereas ratios in June to August samples showed additional influence from petrogenic and fossil fuel combustion. The average increase in the sum of VOCs (ΣVOC) was minor by comparison: 63 ppbv for fire-influenced samples (n = 16) versus 46 ppbv for non-fire samples (n = 90). The samples collected on August 16th and 22nd had large ΣVOC concentrations at all sites (average of 123 ppbv) that were unrelated to wildfire emissions, and composed primarily of acetaldehyde and methanol suggesting a photochemically aged air mass. Normalized excess enhancement ratios (ERs) were calculated for 20 VOCs and 23 PAHs for three fire influenced samples, and the former were generally consistent with previous observations. To our knowledge, this is the first study to report ER measurements for a number of VOCs and PAHs in fresh North American boreal wildfire plumes. During May the aged wildfire plume intercepted the cities of Edmonton (∼380 km south) or Lethbridge (∼790 km south) on four separate occasions. No enhancement in ground-level ozone (O3) was observed in these aged plumes despite an assumed increase in O3 precursors. In the AOSR, the only daily-averaged VOCs which approached or exceeded the hourly Alberta Ambient Air Quality Objectives (AAAQOs) were benzene (during the fire) and acetaldehyde (on August 16th and 22nd). Implications for local and regional air quality as well as suggestions for supplemental air monitoring during future boreal fires, are also discussed.

Dataset Information

New Deep Learning Model to Estimate Ozone Concentrations Found Worrying Exposure Level over Eastern China.

Publications

New Deep Learning Model to Estimate Ozone Concentrations Found Worrying Exposure Level over Eastern China.

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