Quantifying the impacts of anthropogenic and natural perturbations on gaseous elemental mercury (GEM) at a suburban site in eastern China using generalized additive models

Zhang, Lei; Zhou, Peisheng; Zhong, Hui; Zhao, Yu; Dai, Liang; Wang, Qin’geng; Xi, Mengxiao; Long, Yi; Wang, Yutong

doi:10.1016/j.atmosenv.2020.118181

Cited by 11 publications

(16 citation statements)

References 57 publications

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“…In general, a GAM model is considered to have a good interpretation of the response variable when the adjusted value of R 2 exceeds 0.5 [ 39 ]. In comparison with previous studies [ 21 , 35 , 39 ], we believe that GAM models can capture the characteristics of ozone and the results are reliable.…”

Section: Resultssupporting

confidence: 66%

“…The results of F test for GAM reflect the variance contribution of each predictor variable to the response variable [ 40 ]. The F value of a single independent variable divided by the sum of the F values of all the independent variables can represent the contribution of this variable to the change in the predictor variable [ 35 ]. We calculated the relative contribution of each variable based on the F test, and the results are listed in Table 2 .…”

Section: Resultsmentioning

confidence: 99%

“…Therefore we used Gaussian distributions and the identity link function in our research, which means . Penalized cubic regression splines (CRS) were used to smooth the function to ensure that the model was not over-fitted or under-fitted [ 35 ].…”

Section: Methodsmentioning

confidence: 99%

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A potential controlling approach on surface ozone pollution based upon power big data

Wang

et al. 2022

SN Appl. Sci.

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Surface ozone pollution has attracted extensive attention with the decreasing of haze pollution, especially in China. However, it is still difficult to efficiently control the pollution in time despite numbers of reports on mechanism of ozone pollution. Here we report a method for implementing effective control of ozone pollution through power big data. Combining the observation of surface ozone, NO2, meteorological parameters together with hourly electricity consumption data from volatile organic compounds (VOCs) emitting companies, a generalized additive model (GAM) is established for quantifying the influencing factors on the temporal and spatial distribution of surface ozone pollution from 2020 to 2021 in Anhui province, central China. The average R2 value for the modelling results of 16 cities is 0.82, indicating that the GAM model effectively captures the characteristics of ozone. The model quantifies the contribution of input variables to ozone, with both NO2 and industrial VOCs being the main contributors to ozone, contributing 33.72% and 21.12% to ozone formation respectively. Further analysis suggested the negative correlation between ozone and NO2, revealing VOCs primarily control the increase in ozone. Under scenarios controlling for a 10% and 20% reduction in electricity use in VOC-electricity sensitive industries that can be identified by power big data, ozone concentrations decreased by 9.7% and 19.1% during the pollution period. This study suggests a huge potential for controlling ozone pollution through power big data and offers specific control pathways.

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Section: Resultssupporting

confidence: 66%

Section: Resultsmentioning

confidence: 99%

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A potential controlling approach on surface ozone pollution based upon power big data

Wang

et al. 2022

SN Appl. Sci.

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“…The variation of GEM concentrations is influenced by a variety of factors such as anthropogenic emissions, meteorological conditions, and intra-and inter-regional transport (Tang et al, 2018;C. Liu et al, 2019;Zhang et al, 2021). In previous studies, the impact of anthropogenic emissions changes was often quantified by compiling emission inventories (Zhang et al, 2015;Wu et al, 2016;K.…”

Section: Introductionmentioning

confidence: 99%

“…Generalized additive models (GAMs), which use a regression analysis method, are data-driven and able to incorporate nonlinear relationships of air pollution with numerical and categorical variables, which have been introduced into influencing factor identification in recent years (Wood and Augustin, 2002). The impact of local anthropogenic emissions, regional transport, and meteorological factors on GEM concentrations in Nanjing, East China, was quantified using GAMs (Zhang et al, 2021). GAMs were also used to explain the decline of GEM concentrations in Beijing, and results showed that reduction of anthropogenic mercury emissions, variation in meteorological conditions, and change in globe background level explained 51.5 %, 47.1 %, and 1.4 % of the decrease in GEM concentrations, respectively (Wu et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Measurement report: Atmospheric mercury in a coastal city of Southeast China – inter-annual variations and influencing factors

Shi

Chen

et al. 2022

Atmos. Chem. Phys.

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Abstract. The long-term monitoring of atmospheric mercury is an important part of the effective evaluation of the Minamata Convention on Mercury. Gaseous elemental mercury (GEM), along with conventional air pollutants and meteorological parameters, was simultaneously observed in Xiamen, Southeast China, in January and July over the period 2012–2020. GEM concentrations in January were highest in 2015 (4.47 ng m−3) and decreased by 2020 (3.93 ng m−3), while GEM concentrations in July were highest in 2017 (2.65 ng m−3) and lowest in 2020 (1.56 ng m−3). The temporal variation of GEM was typically characterized by higher concentrations in winter than in summer and in nighttime than in daytime. Bivariate polar plots and the concentration-weighted trajectory (CWT) model were used to identify the source regions of GEM on a local and regional scale. The results indicate that the high GEM concentrations in January 2015 were likely due to a combination of high-level Hg emissions and adverse meteorological conditions. Generalized additive models (GAMs), which use a regression analysis method, were established and applied to investigate the influencing factors on the inter-annual variation of GEM. The factors anthropogenic emissions, meteorological conditions, and transportation explained 37.8 %±11.9 %, 31.4 %±9.0 %, and 30.8 %±9.9 % on average of the variation of GEM concentrations, respectively. There was a positive relationship of daily GEM concentrations with T and RH, mostly linking to natural surface emissions and Hg chemical transformations. The interpretation rate of anthropogenic emissions has significantly decreased since 2012, indicating the effectiveness of emission mitigation measures in reducing GEM concentrations in the study region.

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Daily-Basis Observation of Gaseous Mercury (Hg0) Concentrations in an Industrialized City in Japan and Introducing Two New Parameters to Evaluate the Risks of Gaseous Mercury

Takata,

Kamo,

Iwamoto

et al. 2023

Water Air Soil Pollut

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Quantifying the impacts of anthropogenic and natural perturbations on gaseous elemental mercury (GEM) at a suburban site in eastern China using generalized additive models

Cited by 11 publications

References 57 publications

A potential controlling approach on surface ozone pollution based upon power big data

A potential controlling approach on surface ozone pollution based upon power big data

Measurement report: Atmospheric mercury in a coastal city of Southeast China – inter-annual variations and influencing factors

Daily-Basis Observation of Gaseous Mercury (Hg0) Concentrations in an Industrialized City in Japan and Introducing Two New Parameters to Evaluate the Risks of Gaseous Mercury

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