Assessment of Emission Reduction and Meteorological Change in PM2.5 and Transport Flux in Typical Cities Cluster during 2013–2017

Guan, Panbo; Zhang, Hanyu; Zhang, Zhida; Chen, Haoyuan; Bai, Weichao; Yao, Shiyin; Li, Yang

doi:10.3390/su13105685

Cited by 6 publications

(4 citation statements)

References 45 publications

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“…6). The percentages of PM MET 2:5 to PM OBS 2:5 from TSM, however, suggested the meteorology variations contributed to the reduction of the observed PM 2.5 in the YRD region, similar with previous studies with CTM adopted in YRD 49,50 . The opposite role of meteorology to the variation of PM 2.5 derived from different methods here and previous studies suggested that none of the existing methods can perfectly decouple the effects of emissions and meteorology on the trends of air pollutant concentrations.…”

Section: Contributions Of Emission and Meteorology To Pm 25 Trend By ...supporting

confidence: 89%

An intercomparison of weather normalization of PM2.5 concentration using traditional statistical methods, machine learning, and chemistry transport models

Zheng,

Kong,

Zhai

et al. 2023

npj Clim Atmos Sci

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Traditional statistical methods (TSM) and machine learning (ML) methods have been widely used to separate the effects of emissions and meteorology on air pollutant concentrations, while their performance compared to the chemistry transport model has been less fully investigated. Using the Community Multiscale Air Quality Model (CMAQ) as a reference, a series of experiments was conducted to comprehensively investigate the performance of TSM (e.g., multiple linear regression and Kolmogorov–Zurbenko filter) and ML (e.g., random forest and extreme gradient boosting) approaches in quantifying the effects of emissions and meteorology on the trends of fine particulate matter (PM2.5) during 2013−2017. Model performance evaluation metrics suggested that the TSM and ML methods can explain the variations of PM2.5 with the highest performance from ML. The trends of PM2.5 showed insignificant differences (p > 0.05) for both the emission-related ($${{\rm{PM}}}_{2.5}^{{\rm{EMI}}}$$ PM 2.5 EMI ) and meteorology-related components between TSM, ML, and CMAQ modeling results. $${{\rm{PM}}}_{2.5}^{{\rm{EMI}}}$$ PM 2.5 EMI estimated from ML showed the least difference to that from CMAQ. Considering the medium computing resources and low model biases, the ML method is recommended for weather normalization of PM2.5. Sensitivity analysis further suggested that the ML model with optimized hyperparameters and the exclusion of temporal variables in weather normalization can further produce reasonable results in emission-related trends of PM2.5.

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Section: Contributions Of Emission and Meteorology To Pm 25 Trend By ...supporting

confidence: 89%

An intercomparison of weather normalization of PM2.5 concentration using traditional statistical methods, machine learning, and chemistry transport models

Zheng,

Kong,

Zhai

et al. 2023

npj Clim Atmos Sci

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show abstract

“…The initial meteorological and boundary conditions for the WRF were obtained from the National Centers for Environmental Prediction (NCEP) final operational model global analysis data with a 6 h temporal resolution and 1 • × 1 • spatial resolution (https://rda. ucar.edu/datasets/ds083.2/#!access, accessed on 20 May 2021), which is reliable and has been widely used in previous studies [16][17][18]. Global topography and land use data were provided by the United States Geological Survey (USGS).…”

Section: Model Configurationmentioning

confidence: 99%

The Independent Impacts of PM2.5 Dropping on the Physical and Chemical Properties of Atmosphere over North China Plain in Summer during 2015–2019

Wei

Cai

et al. 2022

Sustainability

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Great changes occurred in the physical and chemical properties of the atmosphere in the North China Plain (NCP) in summer caused by PM2.5 dropping from 58 μg/m3 in 2015 to 36.0 μg/m3 in 2019. In this study, we first applied the WRF-Chem model to quantify the impact of PM2.5 reduction on shortwave radiation reaching the ground (SWDOWN), planetary boundary layer height (PBLH), and the surface concentration of air pollutants (represented by CO). Simulation results obtained an increase of 15.0% in daytime SWDOWN and 9.9% in daytime PBLH, and a decrease of −5.0% in daytime CO concentration. These changes were induced by the varied PM2.5 levels. Moreover, the variation in SWDOWN further led to a rise in the NO2 photolysis rate (JNO2) over this region, by 1.82 × 10−4~1.91 × 10−4 s−1 per year. Afterwards, we employed MCM chemical box model to explore how the JNO2 increase and the precursor decrease (CO, VOCs, and NOx) influenced O3 and HOx radicals. The results revealed that the photolysis rate (J) increase would individually cause a change on daytime surface O3, OH, and HO2 radicals by +9.0%, +18.9%, and +23.7%; the corresponding change induced by the precursor decrease was −2.5%, +1.9%, and −2.3%. At the same time, the integrated impacts of the change in J and precursors cause an increase of +6.3%, +21.1%, and +20.9% for daytime surface O3, OH, and HO2. Generally, the atmospheric oxidation capacity significantly enhanced during summer in NCP due to the PM2.5 dropping in recent years. This research can help understand atmosphere changes caused by PM2.5 reduction comprehensively.

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“…Air contamination can be one of the most severe worldwide issues causing ecological and environmental damage [1][2][3] as well as damage to human fitness [4][5][6], especially under long-term high-PM 2.5 (diameter less than or equal 2.5 µg/m 3 ) concentration conditions, which would pose serious threats to public health and respiratory filtration systems. PM 2.5 is known as "pulmonary particulate matter" and is a key index for assessing fitness harm [7,8]. Therefore, an accurate understanding of PM 2.5 concentration is of great significance for early warning of atmospheric quality, which helps to reduce health damage and economic loss.…”

Section: Introductionmentioning

confidence: 99%

Forecasting Fine Particulate Matter Concentrations by In-Depth Learning Model According to Random Forest and Bilateral Long- and Short-Term Memory Neural Networks

et al. 2022

Self Cite

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Accurate prediction of fine particulate matter concentration in the future is important for human health due to the necessity of an early warning system. Generally, deep learning methods, when widely used, perform better in forecasting the concentration of PM2.5. However, the source information is limited, and the dynamic process is uncertain. The method of predicting short-term (3 h) and long-term trends has not been achieved. In order to deal with the issue, the research employed a novel mixed forecasting model by coupling the random forest (RF) variable selection and bidirectional long- and short-term memory (BiLSTM) neural net in order to forecast concentrations of PM2.5/0~12 h. Consequently, the average absolute percentage error of 1, 6, and 12 h shows that the PM2.5 concentration prediction is 3.73, 9.33, and 12.68 μg/m3 for Beijing, 1.33, 3.38, and 4.60 μg/m3 for Guangzhou, 1.37, 4.19, and 6.35 μg/m3 for Xi’an, and 2.20, 7.75, and 10.07 μg/m3 for Shenyang, respectively. Moreover, the results show that the suggested mixed model is an advanced method that can offer high accuracy of PM2.5 concentrations from 1 to 12 h post.

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Assessment of Emission Reduction and Meteorological Change in PM2.5 and Transport Flux in Typical Cities Cluster during 2013–2017

Cited by 6 publications

References 45 publications

An intercomparison of weather normalization of PM2.5 concentration using traditional statistical methods, machine learning, and chemistry transport models

An intercomparison of weather normalization of PM2.5 concentration using traditional statistical methods, machine learning, and chemistry transport models

The Independent Impacts of PM2.5 Dropping on the Physical and Chemical Properties of Atmosphere over North China Plain in Summer during 2015–2019

Forecasting Fine Particulate Matter Concentrations by In-Depth Learning Model According to Random Forest and Bilateral Long- and Short-Term Memory Neural Networks

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