Implementation of a dynamical NH3 emissions parameterization in CMAQ for improving PM2.5 simulation in Taiwan

Hsu, Christine D.; Cheng, Fang‐Yi; Chang, Hsin-Yu; Lin, Neng Huei

doi:10.1016/j.atmosenv.2019.116923

Cited by 20 publications

(7 citation statements)

References 55 publications

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“…This finding is consistent with related studies. For example, NH 3 emissions from urban residential sewage account for a relatively high proportion of total NH 3 emissions and have an important impact on PM 2.5 concentrations [39]. It is worth noting that traffic emissions play a leading role in the four seasons for MUYR and EC in southern China, MYR and NC in northern China, and on a national scale.…”

Section: Discussionmentioning

confidence: 99%

“…In addition to physical and geographic elements, human factors also exert a significant impact on PM 2.5 concentrations [29]. Researchers have discussed the impact of many aspects on PM 2.5 concentrations, such as land use [30,31], urbanization [32,33], transportation [34], economic development [35,36], and emissions premises [37][38][39][40][41]. Studies including [42] and others have studied the impact of reforestation and conversion of arable land on future air quality in the southern United States.…”

Section: Introductionmentioning

confidence: 99%

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Effects of Anthropogenic Emissions from Different Sectors on PM2.5 Concentrations in Chinese Cities

Yang

Liu

Song

et al. 2021

IJERPH

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PM2.5 pollution has gradually attracted people’s attention due to its important negative impact on public health in recent years. The influence of anthropogenic emission factors on PM2.5 concentrations is more complicated, but their relative individual impact on different emission sectors remains unclear. With the aid of the geographic detector model (GeoDetector), this study evaluated the impacts of anthropogenic emissions from different sectors on the PM2.5 concentrations of major cities in China. The results indicated that the influence of anthropogenic emissions factors with different emission sectors on PM2.5 concentrations exhibited significant changes at different spatial and temporal scales. Residential emissions were the dominant driver at the national annual scale, and the NOX of residential emissions explained 20% (q = 0.2) of the PM2.5 concentrations. In addition, residential emissions played the leading role at the regional annual scale and during most of the seasons in northern China, and ammonia emissions from residents were the dominant factor. Traffic emissions play a leading role in the four seasons for MUYR and EC in southern China, MYR and NC in northern China, and on a national scale. Compared with primary particulate matter, secondary anthropogenic precursors have a more important effect on PM2.5 concentrations at the national or regional annual scale. The results can help to strengthen our understanding of PM2.5 pollution, improve PM2.5 forecasting models, and formulate more precise government control policy.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Effects of Anthropogenic Emissions from Different Sectors on PM2.5 Concentrations in Chinese Cities

Yang

Liu

Song

et al. 2021

IJERPH

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“…The existing research methods of air prediction are mainly divided into two kinds-deterministic methods and statistical methods. The deterministic method predicts air quality by building a simulation model of the diffusion and transport of atmospheric chemicals, such as the community multiscale air quality model (CMAQ) [2][3][4] and the nested air quality prediction model system (NAQPMS) [5][6][7]. This kind of theory model needs a lot of prior knowledge and a large number of accurate data.…”

Section: Introductionmentioning

confidence: 99%

An Improved Attention-Based Integrated Deep Neural Network for PM2.5 Concentration Prediction

Shi

Fang

et al. 2021

Applied Sciences

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The air quality prediction is a very important and challenging task, especially PM2.5 (particles with diameter less than 2.5 μm) concentration prediction. To improve the accuracy of the PM2.5 concentration prediction, an improved integrated deep neural network method based on attention mechanism is proposed in this paper. Firstly, the influence of exogenous series of other sites on the central site is considered to determine the best relevant site. Secondly, the data of all relevant sites are input into the improved dual-stage two-phase (DSTP) model, then the PM2.5 prediction result of each site is obtained. Finally, with the PM2.5 prediction result of each site, the attention-based layer predicts the PM2.5 concentration at the central site. The experimental results show that the proposed model is superior to most of the latest models.

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“…In Taiwan, some studies have indicated that the CMAQ simulation is associated with a dynamic NH 3 emissions approach that could improve diurnal and seasonal variations and reduce simulation bias [43]. A real-time air quality forecasting (AQF) system was developed using the Weather Research and Forecasting meteorological model and the CMAQ model for PM 2.5 prediction, and the AQF system was able to reduce the PM 2.5 forecast error and improve the root-mean-square error (RMSE) and mean bias (MB) calculations [44].…”

Section: Introductionmentioning

confidence: 99%

Effectiveness of SOx, NOx, and Primary Particulate Matter Control Strategies in the Improvement of Ambient PM Concentration in Taiwan

2021

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The Community Multiscale Air Quality (CMAQ) measurement was employed for evaluating the effectiveness of fine particulate matter control strategies in Taiwan. There are three scenarios as follows: (I) the 2014 baseline year emission, (II) 2020 emissions reduced via the Clean Air Act (CAA), and (III) other emissions reduced stringently via the Clean Air Act. Based on the Taiwan Emission Data System (TEDs) 8.1, established in 2014, the emission of particulate matter 2.5 (PM2.5) was 73.5 thousand tons y−1, that of SOx was 121.3 thousand tons y−1, and that of NOx was 404.4 thousand tons y−1 in Taiwan. The CMAQ model simulation indicated that the PM2.5 concentration was 21.9 μg m−3. This could be underestimated by 24% in comparison with data from the ambient air quality monitoring stations of the Taiwan Environmental Protection Administration (TEPA). The results of the simulation of the PM2.5 concentration showed high PM2.5 concentrations in central and southwestern Taiwan, especially in Taichung and Kaohsiung. Compared to scenario I, the average annual concentrations of PM2.5 for scenario II and scenario III showed reductions of 20.1% and 28.8%, respectively. From the results derived from the simulation, it can be seen that control of NOx emissions may improve daily airborne PM2.5 concentrations in Taiwan significantly and control of directly emitted PM2.5 emissions may improve airborne PM2.5 concentrations each month. Nevertheless, the results reveal that the preliminary control plan could not achievethe air quality standard. Therefore, the efficacy and effectiveness of the control measures must be considered to better reduce emissions in the future.

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Implementation of a dynamical NH3 emissions parameterization in CMAQ for improving PM2.5 simulation in Taiwan

Cited by 20 publications

References 55 publications

Effects of Anthropogenic Emissions from Different Sectors on PM2.5 Concentrations in Chinese Cities

Effects of Anthropogenic Emissions from Different Sectors on PM2.5 Concentrations in Chinese Cities

An Improved Attention-Based Integrated Deep Neural Network for PM2.5 Concentration Prediction

Effectiveness of SOx, NOx, and Primary Particulate Matter Control Strategies in the Improvement of Ambient PM Concentration in Taiwan

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