Nitrogen isotopes suggest agricultural and non-agricultural sources contribute equally to NH3 and NH4+ in urban Beijing during December 2018

Bhattarai, Noshan; Tian, Hezhong; Dong, Zhaoxin; Chang, Xing; Jiang, Yueqi; Zheng, Haotian

doi:10.1016/j.envpol.2023.121455

Cited by 6 publications

(3 citation statements)

References 97 publications

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“…As a result, the δ 15 N signatures of v-NH 3 in the previous studies were significantly lower than those in the present study, resulting in a lower relative contribution of the v-NH 3 in the SIAR modelbased estimates. In previous studies, 15‰ has been used to correct source data from passive samplers for the estimation of v-NH 3 contributions (Bhattarai et al, 2020(Bhattarai et al, , 2023X. Feng et al, 2023;Kawashima et al, 2022).…”

Section: Sources Apportionment Of Nhmentioning

confidence: 99%

Sources of NH₄⁺ in PM_2.5 and Their Seasonal Variations in Urban Tianjin China: New Insights From the Seasonal δ¹⁵N Values of NH₃ Source

Xiao,

Ding,

2024

JGR Atmospheres

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The stable nitrogen isotopic composition (δ15N) has been widely used to quantify sources of ammonium (NH4+) in PM2.5. However, the overlap and uncertainty in δ15N values from different NH3 sources, coupled with their seasonal variability, hinder accurate identification of NH4+ source. Here, the δ15N values of various NH3 source samples collected by the active sampler were determined. Subsequently, we measured the δ15N values of NH4+ in PM2.5, which were collected seasonally in Tianjin. We found that the combustion‐related NH3 (c‐NH3) exhibiting higher δ15N values compared to volatile NH3 (v‐NH3), but all δ15N values was fell within the range reported by previous studies. Furthermore, inconsistent seasonal variations were observed in the δ15N‐NH3 values originating from emissions of agricultural soil and human excreta. The application of the Bayesian isotope mixing model (MixSIAR model) revealed a significant increase in the contribution of v‐NH3 to NH4+ when incorporating current source data, as opposed to previous data, for δ15N of NH3 source. Notably, the contribution of v‐NH3 (53.1%) to NH4+ was almost equivalent to that of c‐NH3 (46.9%) when considering the seasonal δ15N signatures of NH3 source. Additionally, the estimated contribution of v‐NH3 to NH4+ exhibited significant seasonal variability, which is more reasonable than in the non‐seasonal scenario. This study demonstrated that v‐NH3 and c‐NH3 contributed to NH4+ in PM2.5 in Tianjin almost equally, and it is highlighted that the seasonal δ15N values of NH3 sources should be considered when estimating the contributions of different NH3 sources to NH4+ in PM2.5 by the MixSIAR model.

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Section: Sources Apportionment Of Nhmentioning

confidence: 99%

Sources of NH₄⁺ in PM_2.5 and Their Seasonal Variations in Urban Tianjin China: New Insights From the Seasonal δ¹⁵N Values of NH₃ Source

Xiao,

Ding,

2024

JGR Atmospheres

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“…Air quality (such as PM 2.5 , NO 2 , and SO 2 ) has been shown to closely interact with soil ammonia. Because of the important role of NH 3 in aerosol nucleation and haze [12], the gradual decrease in the formation of NH + 4 aerosols will lead to a significant increase in the residence time of NH 3 in the atmosphere [13], while excessive NH 3 stays in the aerosol phase, which may reduce the removal of the condensed phase [14]. On the contrary, the reaction of SO 2 and NH 3 with active radicals and NO x may also affect the formation of O 3 and secondary aerosol intermediates [15,16].…”

Section: Introductionmentioning

confidence: 99%

“…This is because gaseous NO 2 is a reaction intermediate that reacts with NH 3 ; NH 3 has a good adsorption effect on NO 2 . The concentration of SO 2 , NO 2 , CO, and other precursors is significantly higher, which may impact the production of NH + 4 in the environment [14]. A large number of methods have been widely used in the field of soil gas flux prediction.…”

Section: Introductionmentioning

confidence: 99%

Tree-Structured Parzan Estimator–Machine Learning–Ordinary Kriging: An Integration Method for Soil Ammonia Spatial Prediction in the Typical Cropland of Chinese Yellow River Delta with Sentinel-2 Remote Sensing Image and Air Quality Data

Song,

Ye,

Zheng

et al. 2023

Remote Sensing

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Spatial prediction of soil ammonia (NH3) plays an important role in monitoring climate warming and soil ecological health. However, traditional machine learning (ML) models do not consider optimal parameter selection and spatial autocorrelation. Here, we present an integration method (tree-structured Parzen estimator–machine learning–ordinary kriging (TPE–ML–OK)) to predict spatial variability of soil NH3 from Sentinel-2 remote sensing image and air quality data. In TPE–ML–OK, we designed the TPE search algorithm, which encourages gradient boosting decision tree (GBDT), random forest (RF), and extreme gradient boosting (XGB) models to pay more attention to the optimal hyperparameters’ high-possibility range, and then the residual ordinary kriging model is used to further improve the prediction accuracy of soil NH3 flux. We found a weak linear correlation between soil NH3 flux and environmental variables using scatter matrix correlation analysis. The optimal hyperparameters from the TPE search algorithm existed in the densest iteration region, and the TPE–XGB–OK method exhibited the highest predicted accuracy (R2 = 85.97%) for soil NH3 flux in comparison with other models. The spatial mapping results based on TPE–ML–OK methods showed that the high fluxes of soil NH3 were concentrated in the central and northeast areas, which may be influenced by rivers or soil water. The analysis result of the SHapley additive explanation (SHAP) algorithm found that the variables with the highest contribution to soil NH3 were O3, SO2, PM10, CO, and NDWI. The above results demonstrate the powerful linear–nonlinear interpretation ability between soil NH3 and environmental variables using the integration method, which can reduce the impact on agricultural nitrogen deposition and regional air quality.

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Insights into national distribution of NH3 concentrations in Republic of Korea: findings from passive sampler observations and implications for sources and management

Yu,

Shin,

Jung

et al. 2024

Environ Monit Assess

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Nitrogen isotopes suggest agricultural and non-agricultural sources contribute equally to NH3 and NH4+ in urban Beijing during December 2018

Cited by 6 publications

References 97 publications

Sources of NH₄⁺ in PM_2.5 and Their Seasonal Variations in Urban Tianjin China: New Insights From the Seasonal δ¹⁵N Values of NH₃ Source

Sources of NH₄⁺ in PM_2.5 and Their Seasonal Variations in Urban Tianjin China: New Insights From the Seasonal δ¹⁵N Values of NH₃ Source

Tree-Structured Parzan Estimator–Machine Learning–Ordinary Kriging: An Integration Method for Soil Ammonia Spatial Prediction in the Typical Cropland of Chinese Yellow River Delta with Sentinel-2 Remote Sensing Image and Air Quality Data

Insights into national distribution of NH3 concentrations in Republic of Korea: findings from passive sampler observations and implications for sources and management

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Nitrogen isotopes suggest agricultural and non-agricultural sources contribute equally to NH3 and NH4+ in urban Beijing during December 2018

Cited by 6 publications

References 97 publications

Sources of NH4+ in PM2.5 and Their Seasonal Variations in Urban Tianjin China: New Insights From the Seasonal δ15N Values of NH3 Source

Sources of NH4+ in PM2.5 and Their Seasonal Variations in Urban Tianjin China: New Insights From the Seasonal δ15N Values of NH3 Source

Tree-Structured Parzan Estimator–Machine Learning–Ordinary Kriging: An Integration Method for Soil Ammonia Spatial Prediction in the Typical Cropland of Chinese Yellow River Delta with Sentinel-2 Remote Sensing Image and Air Quality Data

Insights into national distribution of NH3 concentrations in Republic of Korea: findings from passive sampler observations and implications for sources and management

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Sources of NH₄⁺ in PM_2.5 and Their Seasonal Variations in Urban Tianjin China: New Insights From the Seasonal δ¹⁵N Values of NH₃ Source

Sources of NH₄⁺ in PM_2.5 and Their Seasonal Variations in Urban Tianjin China: New Insights From the Seasonal δ¹⁵N Values of NH₃ Source