Reply on RC2 DOI Creative Commons
Baozhu Ge

Published: Aug. 5, 2023

Abstract. A comprehensive understanding of the effects meteorology, emissions, and chemistry on severe haze is critical in mitigation air pollution. However, such an greatly hindered by nonlinearity atmospheric systems. In this study, we developed quantitative decoupling analysis (QDA) method to quantify chemical reactions, their nonlinear interactions fine particulate matter (PM2.5) pollution running built-in scenario simulations each model step. Different from previous methods, QDA achieves a fully decomposed hourly changes PM2.5 concentration during events into seven parts, including pure meteorological contribution (M), emissions (E), (C), among these processes (i.e., ME, MC, EC, MCE). Via embedding Weather Research Forecasting–Nested Air Quality Prediction Modeling System, employed combined it with Integrated Process Rate study typical heavy episode Beijing. We evaluate performance against in situ quality observations describe analytical factors case. Results showed that M varied most significantly at different stages episode, 0.21 µg⋅m−3⋅h−1 accumulation stage −11.82 removal stage, indicating dominated fluctuation amplitude concentration. acted as important cleaner for non-polluting periods but stopped being effective instead became contributor tended grow rapidly under superimposed influence processes, which would probably mark beginning event. The E ranged 0.63 0.88 owing diurnal variation emissions. was shown increase level haze, becoming largest (0.37 µg⋅m−3⋅h−1) maintenance period, 25 % higher than pre-contamination period. And C+CE made significant stages, reactions are more polluted period other periods. Nonnegligible exist concentrations (−1.83 2.44 – something has generally been ignored studies development heavy-pollution control strategies. helpful eliminating interference obtaining purified result target process have indicative significances. ratio CE C positively correlated speed. For precursors like NH3, smaller value indicated NH3 deficient, thus reducing had efficient controlling effect PM2.5. This highlights can be used realize in-depth adverse conditions judge whether excessive or not. Not only provide researchers policymakers valuable information key behind pollution, also help modelers identify sources uncertainties numerical models.

Language: Английский

Characteristics of secondary inorganic aerosols and contributions to PM2.5 pollution based on machine learning approach in Shandong Province DOI
Tianshuai Li,

Qingzhu Zhang,

Xinfeng Wang

et al.

Environmental Pollution, Journal Year: 2023, Volume and Issue: 337, P. 122612 - 122612

Published: Sept. 25, 2023

Language: Английский

Citations

13
Role of air stagnation in determining daily average PM2.5 concentrations in areas with significant impact of long-range transport DOI Creative Commons
Seongeun Jeong, Yoon‐Hee Kang, Eunhye Kim

et al.

Atmospheric Pollution Research, Journal Year: 2024, Volume and Issue: 15(7), P. 102147 - 102147

Published: April 10, 2024

In areas where the regional transport of air pollutants exerts a significant impact, ascertaining whether short-term stagnation affects PM2.5 concentrations is crucial for accurate quality forecasting and effective management planning. However, this research area remains underexplored. study, we analyzed relationship between stagnant atmospheric conditions daily average in with substantial long-range impact (LRT). Specifically, focused on days elevated (daily ≥ 35 μg/m3) from January to March 2019 South Korea. The analysis was performed using Weather Research Forecasting Community Multiscale Air Quality Modeling System. Stagnant were quantified ventilation index (VI), calculated as product planetary boundary layer height 10-m wind speed. correlation coefficient VI (r = –0.23) lower than that NO2 –0.60). This can be attributed fact LRT 2.9-fold higher local emission (LEI) during concentrations. Notably, –0.03) considerably LEI –0.70). Hence, predicting concentration solely based proved challenging an characterized by LRT. For future research, plays role, prediction requires distinguishing effects

Language: Английский

Citations

4
Detecting causal relationships between fine particles and ozone based on observations in four typical cities of China DOI Creative Commons
Ling Qi,

Jikun Yin,

Jiaxi Li

et al.

Environmental Research Letters, Journal Year: 2024, Volume and Issue: 19(5), P. 054006 - 054006

Published: March 25, 2024

Abstract As the concentration of fine particles (PM 2.5 ) is declining, ozone (O 3 has been increasing in China recent years. To collaboratively control PM and O , it critical to understand relationship between two identify major controlling factors. We use a convergent cross-mapping method detect causal daily maximum 8 h average (MDA8) concentrations Beijing, Taizhou, Shenzhen Chengdu, China, four seasons 2015–2021. In addition, we also examined effects atmospheric oxidation capacity, precursors meteorological elements on MDA8 cities. are strongly positively correlated show bidirectional relationships during Beijing Taizhou summer Shenzhen, due mainly strong photochemical reactions daytime. During winter, relationships, but significantly negatively correlated, driven by NO 2 relative humidity. Weak bidirectional, unidirectional no detected other these cities, top three factors differ from those . Season-, city- pollutant-specific measures required.

Language: Английский

Citations

3
Changes in Domestic Emissions Impact on Provincial PM2.5 and NO2 Concentrations during the 1st to 4th Seasonal PM Management Periods DOI
Jiwon Seo, Yoon‐Hee Kang, Eunhye Kim

et al.

Journal of Korean Society for Atmospheric Environment, Journal Year: 2024, Volume and Issue: 40(2), P. 242 - 262

Published: April 30, 2024

Language: Английский

Citations

2
Global estimates of ambient reactive nitrogen components during 2000–2100 based on the multi-stage model DOI Creative Commons
Rui Li, Yining Gao, Lijia Zhang

et al.

Atmospheric chemistry and physics, Journal Year: 2024, Volume and Issue: 24(13), P. 7623 - 7636

Published: July 5, 2024

Abstract. High contents of reactive nitrogen components aggravate air pollution and could also impact ecosystem structures functioning across the terrestrial–aquatic–marine continuum. However, long-term historical trends future predictions at global scale still remain highly uncertain. In our study, field observations, satellite products, model outputs, many other covariates were integrated into multi-stage machine-learning to capture patterns during 2000–2019. order decrease estimate uncertainties in scenarios, constructed component dataset for period was utilised as constraint calibrate CMIP6 four scenarios. The results suggested that cross-validation (CV) R2 values species showed satisfying performance (R2>0.55). concentrations estimated China experienced persistent increases 2000–2013, while they suffered drastic decreases from 2013, except NH3. This might be associated with clean-air policies. Europe United States, these compounds have remained relatively stable since 2000. SSP3-7.0 (traditional-energy scenario) SSP1-2.6 (carbon neutrality highest lowest concentrations, respectively. Although some heavy-pollution scenarios (SSP3-7.0) 2020–2100, SSP2-4.5 (middle-emission more rapidly decreasing trends. Our emphasise need carbon pathways reduce atmospheric N pollution.

Language: Английский

Citations

2
The impact of aerosol-meteorology feedback on the effectiveness of emission reduction for PM2.5: A modeling case study in Northern China DOI

Jing He,

Yi Gao,

Liren Xu

et al.

Atmospheric Research, Journal Year: 2023, Volume and Issue: 294, P. 106963 - 106963

Published: Aug. 14, 2023

Language: Английский

Citations

6
Pollution characteristics and transformation mechanisms of secondary inorganic components during winter heavy pollution in Handan city in 2018–2020 DOI

Fanli Xue,

Wei Hu, Xiaolei Bao

et al.

Atmospheric Pollution Research, Journal Year: 2024, Volume and Issue: 15(7), P. 102150 - 102150

Published: April 12, 2024

Language: Английский

Citations

1
A systematic review of reactive nitrogen simulations with chemical transport models in China DOI
Haoran Zhang,

Xueyu Zhou,

Chuanhua Ren

et al.

Atmospheric Research, Journal Year: 2024, Volume and Issue: 309, P. 107586 - 107586

Published: July 14, 2024

Language: Английский

Citations

1
Quantitative Decoupling Analysis for Assessing the Meteorological, Emission, and Chemical Influences on Fine Particle Pollution DOI Creative Commons
Junhua Wang, Baozhu Ge, Lei Kong

et al.

Journal of Advances in Modeling Earth Systems, Journal Year: 2024, Volume and Issue: 16(11)

Published: Nov. 1, 2024

Abstract A comprehensive understanding of meteorological, emission and chemical influences on severe haze is essential for air pollution mitigation. However, the nonlinearity atmospheric system greatly hinders this understanding. In study, we developed quantitative decoupling analysis (QDA) method by applying Factor Separation (FS) into model processes to quantify effects emissions (E), meteorology (M), reactions (C), their nonlinear interactions impact fine particulate matter (PM 2.5 ) pollution. Taking a heavy‐haze episode in Beijing as an example, show that different from integrated process rate (IPR) scenario approach (SAA) previous studies, QDA explicitly demonstrate decomposing variation PM concentration individual contributions E , M C terms well among these processes. Results showed dominated hourly fluctuation concentration. The increase with increasing level haze, reaching maximum (0.37 μg m −3 h −1 at maintenance stage. Moreover, our reveals there are non‐negligible non‐linear emission, during stage, mean accounting 50% concentrations, which often ignored current control strategies. This study highlights can be used gain insight formation heavy pollution, identify uncertainty numerical models.

Language: Английский

Citations

1
Global estimates of reactive nitrogen components during 2000–2100 based on the multi-stage model DOI Creative Commons
Rui Li, Yining Gao,

Lijia Zhang

et al.

Published: March 5, 2024

Abstract. High contents of reactive nitrogen components aggravate air pollution and could also impact ecosystem structure function across the terrestrial-aquatic-marine continuum. However, long-term historical trends future prediction at global scale still remains high uncertainties. In our study, field observations, satellite products, model output, many other covariates were integrated into machine-learning to capture patterns during 2000–2019. order decrease estimate uncertainties in scenarios, constructed component dataset period was then utilized as constraint calibrate CMIP6 four scenarios. The results suggested cross-validation (CV) R2 values species showed satisfied performance (R2 > 0.55). concentrations estimated China experienced persistent increases 2000–2013, while they suffered from drastic decreases since 2013 except NH3. It might be associated with clean policy. these compounds Europe United States remained relatively stable 2000. SSP3-7.0 (traditional energy scenario) SSP1-2.6 (carbon neutrality highest lowest concentrations, respectively. Although some heavy-pollution scenarios (SSP3-7.0) 2020–2100, SSP2-4.5 (middle emission kept more rapid decreasing trends. Our emphasize need for carbon-neutrality pathway reduce atmospheric N pollution.

Language: Английский

Citations

0