Bromoform, dibromochloromethane, and dibromomethane over the East China Sea and the western Pacific Ocean: Oceanic emission and spatial variation

Liu, Shanshan; He, Zhen; Yang, Gui‐Peng

doi:10.1016/j.chemosphere.2020.127151

Cited by 6 publications

(2 citation statements)

References 46 publications

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“…The FULL case estimates an amount closer to the observations for both halogen oxides (daytime average of 0.45 pptv and maximum of 1.4 pptv for BrO at the surface; the daytime average of 0.68 pptv and maximum of 1.3 pptv of IO in MBL; and an average of 1.2 pptv and maximum of 2.6 pptv of IO at the surface; Table S1). A recent study (S. Liu et al, 2020) reported a wide range of mixing ratios of organic bromine species (CHBr 3 , CH 2 Br 2 , and CHBr 2 Cl) in the East China Sea and West Pacific. The present study underestimates these organic bromine species but covers the lower end of the measurements (Table S1).…”

Section: Model Performance Evaluationmentioning

confidence: 99%

Chemical Interactions Between Ship‐Originated Air Pollutants and Ocean‐Emitted Halogens

Badía

Fernández

et al. 2021

JGR Atmospheres

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Shipment is the dominant method for redistributing products from one continent to another and is critical in sustaining the world's economy. Shipping activities result in emissions of air pollutants over natural waters and contribute approximately 15% of the NO x emissions and 13% of the SO x emissions on a global scale (IMO, 2014; Kontovas, 2020). Ship-originated air pollutants (primary emissions and their secondary products) have been reported to affect the atmospheric composition, air quality, and human health. A pioneering study by Lawrence and Crutzen (1999) revealed that ship emissions reshape atmospheric chemistry in the marine boundary layer (MBL). Eyring et al. (2010) reported that ship emissions of air pollutants caused air quality problems with elevated nitrogen oxides, sulfur, particulate matter, and ground-level ozone (O 3) in coastal areas and harbors. Fan et al. (2016) and Lv et al. (2018) also suggested that ship emissions have a noticeable influence on air quality in East China and East Asia, respectively. The reduced air quality due to ship emissions can lead to adverse health impacts with 14,500-37,500 worldwide premature deaths per year (H. Liu et al., 2016). Ship emissions affect climate via their effects on greenhouse gases (CO 2 , CH 4 , and O 3) and cloud condensation nuclei (Von Schneidemesser et al., 2015). Ship emissions also change marine stratocumulus regime of open cells into closed cells inducing negative radiative forcing (Y.-C. Chen et al., 2012; Goren & Rosenfeld, 2012). The budget of reactive halogen species (RHS) can also be influenced by shipping emissions. Osthoff et al. (2008) reported the first ambient measurement of ClNO 2 , an important reservoir of reactive chlorine,

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Section: Model Performance Evaluationmentioning

confidence: 99%

Chemical Interactions Between Ship‐Originated Air Pollutants and Ocean‐Emitted Halogens

Badía

Fernández

et al. 2021

JGR Atmospheres

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“…Therefore, the prerequisite for constructing an accurate water quality prediction model is to have good water quality data, and further preprocessing of water quality sample data is required before training. A. SslX et al [ 6 ] and X. Sun et al [ 7 ] used a PCA algorithm to correlate seawater quality and reduce the dimensionality of water quality data, respectively.…”

Section: Introductionmentioning

confidence: 99%

A PCA–EEMD–CNN–Attention–GRU–Encoder–Decoder Accurate Prediction Model for Key Parameters of Seawater Quality in Zhanjiang Bay

Xie

et al. 2022

Materials

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In order to effectively solve the problem of low accuracy of seawater water quality prediction, an optimized water quality parameter prediction model is constructed in this paper. The model first screened the key factors of water quality data with the principal component analysis (PCA) algorithm, then realized the de-noising of the key factors of water quality data with an ensemble empirical mode decomposition (EEMD) algorithm, and the data were input into the two-dimensional convolutional neural network (2D-CNN) module to extract features, which were used for training and learning by attention, gated recurrent unit, and an encoder–decoder (attention–GRU–encoder–decoder, attention–GED) integrated module. The trained prediction model was used to predict the content of key parameters of water quality. In this paper, the water quality data of six typical online monitoring stations from 2017 to 2021 were used to verify the proposed model. The experimental results show that, based on short-term series prediction, the root mean square error (RMSE), mean absolute percentage error (MAPE), and decision coefficient (R2) were 0.246, 0.307, and 97.80%, respectively. Based on the long-term series prediction, RMSE, MAPE, and R2 were 0.878, 0.594, and 92.23%, respectively, which were all better than the prediction model based on an enhanced clustering algorithm and adam with a radial basis function neural network (ECA–Adam–RBFNN), a prediction model based on a softplus extreme learning machine method with partial least squares and particle swarm optimization (PSO–SELM–PLS), and a wavelet transform-depth Bi–S–SRU (Bi-directional Stacked Simple Recurrent Unit) prediction model. The PCA–EEMD–CNN–attention–GED prediction model not only has high prediction accuracy but can also provide a decision-making basis for the water quality control and management of aquaculture in the waters around Zhanjiang Bay.

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Coastal observation of halocarbons in the Yellow Sea and East China Sea during winter: Spatial distribution and influence of different factors on the enzyme-mediated reactions

Zou

Liu

et al. 2021

Environmental Pollution

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Bromoform, dibromochloromethane, and dibromomethane over the East China Sea and the western Pacific Ocean: Oceanic emission and spatial variation

Cited by 6 publications

References 46 publications

Chemical Interactions Between Ship‐Originated Air Pollutants and Ocean‐Emitted Halogens

Chemical Interactions Between Ship‐Originated Air Pollutants and Ocean‐Emitted Halogens

A PCA–EEMD–CNN–Attention–GRU–Encoder–Decoder Accurate Prediction Model for Key Parameters of Seawater Quality in Zhanjiang Bay

Coastal observation of halocarbons in the Yellow Sea and East China Sea during winter: Spatial distribution and influence of different factors on the enzyme-mediated reactions

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