An evaluated aerosol extinction coefficient dataset and its application to improve visibility forecasts in Xiong’an, China

1 Introduction

With growing urbanization and industrialization in China, air pollution has become a major environmental issue affecting visibility across the country, especially in rapidly developing urban areas (Guo et al., 2014; Yue et al., 2017). Research demonstrates that particulate matter (PM_2.5, PM₁₀), aerosols, and other pollutants significantly reduce visibility by scattering and absorbing light, thereby causing severe impacts on public health, traffic safety, and economic activities (De Marco et al., 2019; Hao et al., 2021; Su et al., 2020). To quantify the influence of various factors on visibility, the atmospheric extinction coefficient is introduced; it represents the cumulative effect of multiple light-extinguishing substances in the atmosphere across visible and near-infrared wavelengths. Particularly during hazy conditions, aerosol extinction becomes a primary factor affecting visibility (Ting et al., 2022; Hu et al., 2017). The aerosol extinction coefficient (AEC), which is governed by particulate mass concentration, chemical composition, and hygroscopicity, constitutes a major source of uncertainty in visibility forecasting (Bhattacharjee et al., 2023).

The primary technical methods for calculating the aerosol extinction coefficient include the physics-based Mie scattering theory, the statistics-based empirical formula method, and the remote sensing-based retrieval method, each possessing distinct advantages and application limitations. Mie scattering theory (Mie, 1908) calculates scattering and absorption coefficients based on aerosol particle size distribution, refractive index, and wavelength, forming the theoretical foundation for AEC computation. This method assumes aerosols are spherical particles and enables precise calculation of extinction efficiency by integrating particle size distribution with chemical composition (Ohta et al., 1990; Sumlin et al., 2018). Han et al. (2013) utilized a Mie scattering model within the Regional Atmospheric Modeling System–Community Multiscale Air Quality (RAMS-CMAQ) modeling system to simulate visibility during pollution episodes in Beijing and the North China Plain. Their results indicated that the low visibility events in this region were primarily caused by high concentrations of PM_2.5, resulting from the accumulation of local pollutant emissions and long-range transport. In related studies, Mie scattering theory is also frequently employed to analyze the extinction characteristics of PM_2.5. For instance, the hygroscopic growth of ammonium sulfate and ammonium nitrate under high-humidity conditions significantly enhances AEC (Gao et al., 2021). However, this method requires detailed particle size spectra and chemical composition data, which are difficult to obtain, and its high computational complexity constrains its application in real-time forecasting (Zhan et al., 2025; García-Arroyo and Osca, 2017).

Secondly, the empirical formula method establishes a functional relationship between AEC and variables such as PM_2.5 concentration and humidity through statistical regression, and it is widely used in air quality and visibility models. For example, Liu et al. (2022) developed a multiple regression equation relating visibility to PM_2.5 concentration, temperature, and dew point temperature, which effectively reproduced the variation of winter visibility from 2016 to 2020 in the Beijing-Tianjin-Hebei and Yangtze River Delta regions. Among these methods, the US Interagency Monitoring of Protected Visual Environments (IMPROVE) program, based on long-term observations and research on key optical parameters related to visibility and aerosol composition, reconstructed the relationship between aerosol chemical component mass concentration and the extinction coefficient (Hand and Malm, 2007; Malm et al., 1994). This program has become the most widely used empirical method for estimating the extinction coefficient. The IMPROVE empirical method estimates AEC based on PM_2.5 chemical components such as sulfate, nitrate, and organic carbon (Pitchford et al., 2007). This algorithm incorporates the hygroscopic growth factor (f (Relative Humidity, RH)), effectively characterizing the influence of humidity on extinction (Zhao et al., 2019). For instance, researchers applying the IMPROVE algorithm to assess visibility impairment in China’s Beijing-Tianjin-Hebei and Yangtze River Delta regions found that ammonium sulfate and organic matter contributed approximately 40% and 24%, respectively, to the extinction coefficient (Gao et al., 2021). However, this method relies on region-specific chemical composition data, exhibits poor versatility, and responds inadequately to dynamic changes in PM_2.5 composition.

Furthermore, satellite and radar retrieval methods are often used for AEC calculation, utilizing remote sensing data such as those from the Cloud-Aerosol Lidar (CALIPSO) and Moderate Resolution Imaging Spectroradiometer (MODIS). CALIOP data provide the aerosol vertical distribution, which, combined with MODIS Aerosol Optical Depth (AOD), enables the estimation of surface AEC via a two-layer model (He et al., 2012). Research in Shanghai, China, demonstrated that the AEC estimated using the two-layer model using combined CALIOP and MODIS data was highly correlated with observed visibility, with a correlation coefficient of 0.86 (He et al., 2012). However, satellite retrieval is often limited by cloud contamination and relatively coarse spatial resolution, which restricts its ability to meet the demands of numerical forecasting.

The atmospheric extinction coefficient is a core physical quantity that quantifies the interaction between light and the atmosphere. As a key input for numerical simulations of visibility, it supports the analysis of aerosol physical and chemical properties, research on atmospheric radiative transfer, and assessment of climate effects, thereby forming a scientific bridge connecting fundamental research with practical needs such as pollution control and climate policy formulation. China’s Xiong’an New Area, officially established in 2017 as a national-level new district, is designed to relieve Beijing of functions non-essential to its role as the capital and promote the coordinated regional development of the Beijing-Tianjin-Hebei area (Liao and Huang, 2020). As shown in Supplementary Figure S1, this region is characterized by unique geographical attributes: nestled against the eastern slopes of the Taihang Mountains in the west, adjacent to the North China Plain in the east, fronting the Bohai Bay, and cradling the renowned Baiyang Lake Wetland, often called the “Pearl of North China” Xiong’an has a warm-temperate monsoon continental climate. Situated at the lower reach of nine rivers, the area is jointly influenced by its complex terrain and the northern fringe of the East Asian monsoon belt, with prevailing northeasterly-to-southwesterly airflow throughout the year. This makes the climate highly sensitive and variable, and prone to meteorological hazards such as low-visibility events. This specific geographical and climatic context, compounded by multifaceted pressures such as persistently high PM_2.5 concentrations, pronounced humidity variability, and concentrated local emissions (Zhang et al., 2023), collectively establishes the region as a prototypical area for investigating atmospheric extinction characteristics, visibility evolution, and aerosol effects amid rapid urbanization.

This study focuses on the Chinese Xiong’an New Area as a case study. By establishing a multi-source data fusion approach integrated with numerical simulation techniques, we have constructed a comprehensive dataset of aerosol extinction coefficients. This effort compensates for the limitations inherent in relying solely on the IMPROVE empirical formula or satellite retrieval methods for constructing extinction coefficient datasets, thereby providing a novel methodology for related fields. The dataset enabled regional visibility simulations using the China Meteorological Administration Mesoscale model (CMA-MESO), the outcomes of which were rigorously evaluated against ground-based observational data. The dataset encompasses an optimized aerosol extinction coefficient series and visibility forecast products. Its construction methodology is not only applicable to visibility simulation and forecasting evaluation in urban environments but also provides scientific data support and practical reference for regional air quality management, sustainable urban planning, atmospheric radiation transfer modeling, and climate impact analysis.

2 Methods

2.1 Extinction coefficient calculation methods

This research is dedicated to constructing an aerosol extinction coefficient dataset through a multi-source data fusion approach, primarily to serve visibility prediction in mesoscale numerical weather modeling. This study is based on the CMA-MESO model, a mesoscale numerical weather prediction system independently developed by the China Meteorological Administration (CMA) which has been under continuous development and refinement since 2001 (Chen et al., 2008; Zhuang et al., 2023; Jishan, 2004). The system operates on the principles of mesoscale meteorological dynamics, utilizing high-resolution numerical simulation and multi-source data assimilation to generate refined weather element forecasts. It provides critical support for both daily meteorological operations and the safeguarding of major public events (Chen and Shen, 2006; Huang et al., 2017).

In this study, two methods were applied in the CMA_MESO model to simulate visibility data in China, which were labelled as S1 and S2.

2.1.1 The S1 scheme

S1 is a set of algorithms based on historical station observation data. Wang et al. (2020) found the PM_2.5 concentration showed a good linear relationship with the extinction coefficients at different relative humidity in most parts of China, according to observation data. Especially in Beijing, the correlation coefficients between extinction coefficient and PM_2.5 concentration always fluctuate around 0.90, with relative humidity ranging from 20% to 90%. The relationship equations between PM_2.5 concentration and extinction coefficient are given by Equation 1.

${\delta }_{ext}=a\left(RH\right)\times {PM}_{2.5}+b(1)$

Where δext is the extinction coefficient, m⁻¹; $a\left(RH\right)$ is the extinction efficiency of the aerosol produced by PM_2.5 under different relative humidity; PM_2.5 is the fine particulate matter concentration, μg/m³ b is the additional extinction coefficient arising from Rayleigh scattering and gas absorption, m⁻¹.

The value of (RH) and b were referred to Wang et al. (2020). Based on hourly observational data of PM_2.5 and relative humidity from 2018 to 2023, the aerosol extinction coefficient in Xiong’an was calculated using Formula 1. Considering the significant differences in particulate pollution characteristics across seasons and distinct diurnal variation patterns, the hourly data for each respective month over the 5-year period were averaged, forming the basis for the extinction coefficient dataset under the S1 scheme. The average value is calculated using Equation 2, as follows:

${\overline{\delta }}_{\left(m,h\right)}=\frac{{\sum }_{y=1}^5{\delta }_{y,m,h}}{5}(2)$

Where y stands for year, m for month, and h for hour. ${\overline{\delta }}_{\left(m,h\right)}$ represents the final required average extinction coefficient for the m-th month and h-th hour, while ${\delta }_{y,m,h}$ denotes the original extinction coefficient data for the h-th hour of the m-th month in the y-th year.

2.1.2 The S2 scheme

S2 is generated from S1 through the integration of satellite aerosol extinction data. This approach employs the Space-Time Multiscale Analysis System (STMAS), an advanced data assimilation system developed by the Earth System Research Laboratory (ESRL) of the National Oceanic and Atmospheric Administration (NOAA), to merge the baseline data produced by Formula 1 with the MERRA-2 reanalysis product, thereby creating the new dataset. MERRA-2 provides globally covered reanalysis data and significantly improves the spatiotemporal consistency and accuracy of atmospheric variables (such as aerosols) by assimilating multi-source satellite observations. The core algorithm of STMAS is the multigrid sequential variational method, which performs iterative analysis from coarse to fine grids. This method utilizes coarse grids to accelerate the convergence of large-scale, low-frequency patterns, thereby providing an initial field for the fine-grid analysis. This process effectively eliminates aliasing effects across different scales, ultimately producing a fused product with high spatiotemporal resolution (Xie et al., 2011).

The objective function on each grid is given by Equation 3:

${\mathrm{J}}^{\left(n\right)}=\frac{1}{2}{X}^{\left(n\right)T}{X}^{\left(n\right)}+\frac{1}{2}{\left(H^{\left(n\right)}{X}^{\left(n\right)}-Y^{\left(n\right)}\right)}^T{O}^{\left(n\right)-1}\left(H^{\left(n\right)}{X}^{\left(n\right)}-Y^{\left(n\right)}\right)\left(n=1,2,3,L,N\right)(3)$

The Y value on each grid is calculated using Equation 4 as follows:

$\left\{\begin{array}{l}{Y}^{\left(1\right)}=Y^{\left(\mathit{\text{obs}}\right)}-H^{\left(1\right)}{X}^b\left(n=1\right)\\ Y^{\left(n\right)}=Y^{\left(n-1\right)}-H^{\left(n-1\right)}{X}^{\left(n-1\right)}\left(n=2,3,L,N\right)\end{array}\right.(4)$

The final analysis result is derived from the superposition of the results of each heavy grid analysis, as expressed in Equation 5:

$X^a=X^b+X_L=X^b+{\displaystyle \sum _{n=1}^N}{X}^{\left(n\right)}(5)$

Among them $\mathrm{Y}=Y^{obs}-H{X}^b$, $\mathrm{X}=X^a-X^b$. O is the observation error covariance matrix; $X^b,X^a\text{a}\text{n}\text{d}{Y}^{obs}$ are the background field, analysis field, and observation field vectors, respectively; H is the bilinear interpolation operator from the model grid to the observation point; X represents the correction vector relative to the model field vector, calculated by the variational data assimilation system; Y is the difference between the observation field and the model field; n represents the nth grid, and N is the total number of grids. The element data is fused according to the multi-grid variational analysis method to generate a fusion product.

The development of the S2 extinction coefficient dataset involved transforming the S1-derived data into a 3-km resolution grid through the application of the Cressman (Cressman, 1959) interpolation method, which was selected for its demonstrated advantages in spatiotemporal distribution accuracy. The Cressman objective analysis method employs a successive correction technique to interpolate data from discrete observation stations onto regular grid points. The specific procedure is as follows: First, an initial guess field for the grid points is established, which in this study is defined as the regional mean within the scanning radius R. Subsequently, observational data within this radius are used to correct the initial guess field by calculating the analysis increment ($\Delta {\delta }_{ij}$) between the observed values and the initial field. This correction process is repeated iteratively until the adjusted field converges satisfactorily with the observations. In practice, a single objective analysis typically requires configuring the configuration of multiple scanning radii R and several iterations of the Cressman method to achieve optimal results. The formula is expressed as follows in Equations 6–9:

${\delta }_{\mathit{\text{ij}}}^n={\delta }_{\mathit{\text{ij}}}^{n-1}+\Delta {\delta }_{\mathit{\text{ij}}}(6)$

$\Delta {\delta }_{\mathit{\text{ij}}}=\frac{{\sum }_{k=1}^K{w}_{\mathit{\text{ij}},k}\left({O_k-\delta }_{\mathit{\text{ij}}}\right)}{{\sum }_{k=1}^K{w}_{\mathit{\text{ij}},k}}(7)$

$w_{\mathit{\text{ijk}}}=\left\{\begin{array}{l}\frac{R^2-{r_{\mathit{\text{ij}},k}}^2}{R^2+{r_{\mathit{\text{ij}},k}}^2},r_{\mathit{\text{ij}},k}\le R\\ 0,r_{\mathit{\text{ij}},k}>R\end{array}\right.(8)$

${\mathrm{\delta }}_{\mathit{\text{ij}}}^{\left(n,R\right)}=V^R{\delta }_{\mathit{\text{ij}}}^{\left(n,R\right)}+\left(1-V^R\right){\delta }_{\mathit{\text{ij}}}^{\left(n,R-1\right)}(9)$

Where R represents the scanning radius, $r_{ij,k}$ denotes the distance between the k-th observation within the scanning radius and the grid point (i,j), $w_{ij,k}$ is the influence weight of the k-th observation on grid point (i,j), $O_k$ represents the k-th observational value within the scanning radius, ${\delta }_{ij}$ is the value of the initial guess field, and ${\delta }_{ij}^n$ denotes the Cressman analysis value after the (n−1)-th iteration. $V^R$ represents the Cressman analysis weight for scanning radius R, where the dimensions of V and R are identical. ${\delta }_{ij}^{\left(n,R\right)}$ is the Cressman analysis result for scanning radius R, while ${\delta }_{ij}^{\left(n,R-1\right)}$ corresponds to the analysis result for scanning radius R−1.

The 3-km gridded data, obtained through the aforementioned interpolation method, were subsequently fused with the MERRA-2 reanalysis aerosol extinction data using the data assimilation approach described in Equation 3. This procedure yielded the integrated grid data that form the S2 extinction coefficient dataset.

The aerosol extinction coefficients calculated by the S1 and S2 schemes will be converted into visibility via Koschmieder’s law for the purpose of evaluating the dataset quality and its application effectiveness, as given in Equation 10.

$\text{Vis}=\frac{3.912}{\beta \times \ln 10}(10)$

2.2 Test methods

To verify the accuracy of the extinction coefficient, this study assessed its impact on visibility forecasting. The performance of the two calculation schemes (S1 and S2) was evaluated through statistical and graded verification tests, following the methodology detailed in Supplementary Table S1 (Jongeward et al., 2016).

The statistical test metrics comprise the mean error (ME) defined in Equation 11, the normalized mean bias (NMB) given by Equation 12, and the root-mean-square error (RMSE) expressed in Equation 13.

$ME={\displaystyle \sum _{i=1}^N}\left(F_i-O_i\right)/N(11)$

$NMB={\displaystyle \sum _{i=1}^N}\left(F_i-O_i\right)/{\displaystyle \sum _{i=1}^N}{O}_i\times 100\%(12)$

$RMSE=\sqrt{{\frac{1}{N}{\displaystyle \sum _{i=1}^N}\left(F_i-O_i\right)}^2}(13)$

Where i represents the values for each hour, Fi is the i-th forecast value of the scheme, Oi is the i-th observed value, and N is the total number of samples.

For the grading test, the Threat Score (TS) was computed using the three visibility classes in Supplementary Figure S1. The parameters were calculated as specified in Equation 14.

$TS=\frac{NA}{NA+NB+NC}(14)$

Where NA is the number of correct forecasts, NB is the number of empty reports, and NC is the number of missed reports. A higher TS signifies superior forecast quality.

2.3 The source of data

The surface observation data used in this case study include PM_2.5 concentration, relative humidity, and visibility. The pollution data were obtained from the Environmental Meteorological Service Platform (http://eia-data.com/), and the meteorological data are from the Xiaozuanfeng platform (https://meteo.agrodigits.com/), a publicly accessible platform providing meteorological observations for research applications. All observational data were collected from stations in the Xiong’an area. The case study focused on the Xiong’an region and utilized data from these observation station covering the period from 1 December 2018, to 1 December 2023, covering a 5-year period.

The MERRA-2 (Modern Era Retrospective-Analysis for Research, version 2) (https://disc.gsfc.nasa.gov/datasets/M2TMNXAER_5.12.4/summary) is a long-term reanalysis product that includes various meteorological variables. In this study, we used the monthly average aerosol extinction coefficient at 550 nm data from the MERRA-2 reanalysis dataset, covering the date range 1 December 2018, to 1 December 2023.

2.4 Data records

The outputs of this study have been systematically archived as the following four data files:

1. PM_2.5 concentration observations in Xiong’an. This dataset contains daily PM_2.5 concentration measurements from five monitoring stations in the Xiong’an area-Rongcheng Middle School, Civil Affairs Bureau of Anxin County, Electric Power Bureau of Anxin County, Environmental Protection Bureau of Xiong County, and Natural Resources Bureau of Xiong County, covering the period from September 1 to 30, 2023. Daily average concentrations are provided.

2. Relative humidity observations in Xiong’an. This file includes hourly relative humidity records from Xiong’an monitoring stations during the same period (September 1–30, 2023), with data recorded at 24-h intervals.

3. MERRA-2 monthly mean aerosol extinction coefficient. This dataset provides global monthly mean aerosol extinction coefficient data for September 2023, derived from the MERRA-2 reanalysis product.

4. Fused aerosol extinction coefficient from Scheme S2. This archive contains hourly aerosol extinction coefficient data for September 2023, generated using the fusion approach of Scheme S2. The data cover the Beijing-Tianjin-Hebei region (112°E−120°E, 35°N–43°N). The file includes 24 individual data files, one for each hour, each containing variables such as longitude, latitude, and aerosol extinction coefficient.

3 Technical validation

3.1 Visibility forecast performance

To validate the dataset constructed using Schemes S1 and S2, we assessed its impact on visibility forecasting for the period from September to November 2023. Supplementary Figure S2; Table 1 summarize the visibility forecast performance of the S1 and S2 schemes in the Xiong’an New Area during this period. The S1 method considerably overestimates visibility, with ME range of 11.755–15.594 km, NMB of 1.414%–2.329%, and RMSE of 13.960–17.080 km. The uneven distribution of meteorological stations and PM_2.5 monitoring stations, along with their incomplete spatial overlap, introduces uncertainties into ground-based aerosol extinction calculations. Moreover, the use of a fixed climatological AEC in S1 fundamentally fails to capture the day-to-day synoptic variability in pollution and meteorology, which constitutes the primary source of its substantial forecasting errors. In contrast, the S2 scheme significantly reduces the error by incorporating the 5-year mean PM_2.5 and RH data. Its performance shows an ME range of −6.823 to 1.360 km, NMB of −0.298% to 0.121%, and RMSE of 4.290–10.981 km, demonstrating notable improvement over S1. Compared to S1, the S2 scheme achieves substantial reductions across all error metrics. Specifically, ME decreases by 58.0%–108.1%, NMB is lowered by 107.7%–120.4%, and RMSE shows a reduction of 35.7%–74.9%.

Table 1

Table 1. The ME, NMB and RMSE results of three schemes for each month.

Figure 1 shows the daily mean PM_2.5 concentration and corresponding visibility during three selected episodes. From September 3rd to 11th, PM_2.5 concentrations fluctuated between 60 and 100 μg/m³, accompanied by a decrease in visibility to 2–4 km. Between October 24 to 26, PM_2.5 concentrations remained low (40–50 μg/m³), and visibility improved to 5–7 km. From November 21st to 22nd, PM_2.5 concentrations rose to around 80 μg/m³, and visibility dropped to 3–5 km. These three episodes were selected to evaluate the performance of the visibility forecasts.

Figure 1

Figure 1. The daily average PM_2.5 and visibility observation data in the Xiong’an area for the selected time period: (a) 3–11 September 2023, (b) 24–26 September 2023, (c) 21–22 November 2023.

As shown in Figure 2, the S2 scheme consistently achieved lower daily mean ME, NMB, and RMSE values across all three periods. The inverse relationship between PM_2.5 and visibility observed in Figure 1 is directly reflected in the forecast performance metrics of Figure 2. Specifically, S2 yielded a daily average RMSE of approximately 6–8 km, outperforming S1, which had an RMSE of 12–15 km. Notably, although the RMSE of S2 increased from September (with a maximum of 4.29 km) to November (with a maximum of 10.98 km), this likely reflects the greater challenge of forecasting visibility during the more dynamically complex and polluted autumn season in North China. During October 24th–26th, when PM_2.5 concentrations was low, S1 overestimated visibility, with an NMB ranging from 1% to 10%, while S2 remained stable (NMB≈−0.5%). On November 21st–22nd, as PM_2.5 concentration increased, S2 maintained a lower RMSE (about 6 km) compared to S1. These results demonstrate the ability of the S2 scheme to deliver relatively accurate visibility predictions under varying PM_2.5 concentration conditions.

Figure 2

Figure 2. The ME, NMB and RMSE of daily visibility forecasts for the selected time period: (a) 3–11 September 2023, (b) 24–26 September 2023, (c) 21–22 November 2023.

3.2 Forecasting capability in visibility intervals

As shown in Table 2; Supplementary Figure S3, the S2 scheme achieves higher TS values than S1 across most visibility intervals, demonstrating its improved capability in simulating aerosol effects on visibility. In the 1–3 km interval, the S2 scheme achieves higher TS values (0.0440–0.0663) than S1 (0.0048–0.0295). The peak TS for S2 in September (0.0663) coincides with an observed PM_2.5 concentration of approximately 100 μg/m³, underscoring its accuracy in capturing haze episodes. For the 3–5 km interval, S2 performs slightly better than S1 in September (TS = 0.0373) and October (TS = 0.0697). Similarly, in the higher visibility range (5–10 km), S2 outperforms S1 in September (0.1673) and November (0.1730). The consistently higher TS values of S2 across most ranges confirm its enhanced ability to simulate the aerosol effect.

Table 2

Table 2. TS scores by Month and Visibility Range.

4 Conclusion

In response to the severe visibility degradation caused by air pollution in China, this study focused on the pivotal role of the aerosol extinction coefficient in quantifying visibility and established a dedicated dataset to support related research. Two calculation schemes (S1, S2) within the CMA-MESO model were optimized, with a specific focus on the Xiong’an New Area. Scheme S1 calculates the aerosol extinction coefficient based on 5 years of observational data using empirical methods, while Scheme S2 integrates the extinction coefficient derived from S1 with MERRA-2 reanalysis data by applying the STMAS data fusion technique.

Evaluation of visibility forecasts in the Xiong’an area shows that S2 outperforms S1, with ME ranging from −6.823 km to 1.360 km and RMSE between 4.290 km and 10.981 km. Moreover, S2 exhibits a superior ability to capture haze events, as indicated by its higher TS in the visibility range of 1–3 km.

This study provides a methodological framework and a validated dataset that offer a scientific basis for air quality management and visibility forecasting in rapidly urbanizing regions.

Data availability statement

The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding authors.

Author contributions

WW: Conceptualization, Methodology, Supervision, Validation, Data curation, Writing – review and editing. XL: Investigation, Writing – review and editing, Resources, Writing – original draft, Visualization. XM: Writing – review and editing, Supervision, Investigation, Validation, Data curation, Conceptualization, Methodology. JW: Visualization, Writing – original draft, Resources, Writing – review and editing, Investigation. LiS: Conceptualization, Visualization, Writing – review and editing. LoS: Writing – review and editing, Visualization, Conceptualization. SW: Writing – review and editing, Formal Analysis, Data curation. DZu: Data curation, Writing – review and editing, Formal Analysis. DZh: Data curation, Writing – review and editing, Formal Analysis.

Funding

The author(s) declared that financial support was received for this work and/or its publication. This work was supported by the Opening Foundation of China Meteorological Administration Xiong’an Atmospheric Boundary Layer Key Laboratory (No. 2023LABL-B23), and the Joint Fund of the National Natural Science Foundation of China and the China Meteorological Administration (U2442211).

Conflict of interest

The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Generative AI statement

The author(s) declared that generative AI was not used in the creation of this manuscript.

Any alternative text (alt text) provided alongside figures in this article has been generated by Frontiers with the support of artificial intelligence and reasonable efforts have been made to ensure accuracy, including review by the authors wherever possible. If you identify any issues, please contact us.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fenvs.2026.1787337/full#supplementary-material

References

Bhattacharjee, P. S., Zhang, L., Baker, B., Montuoro, R., Grell, G. A., McQueen, J. T., et al. (2023). Evaluation of aerosol optical depth forecasts from NOAA's global aerosol forecast model (GEFS-Aerosols). Weather Forecast. 38, 225–249. doi:10.1175/waf-d-22-0083.1

CrossRef Full Text | Google Scholar

Chen, D. H., and Shen, X. S. (2006). Recent progress on GRAPES research and application. J. Appl. Meteorological Sci., 773–777.

Google Scholar

Chen, D., Xue, J., Yang, X., Zhang, H., Shen, X., Hu, J., et al. (2008). New generation of multi-scale NWP system (GRAPES): general scientific design. Sci. Bull. 53, 3433–3445. doi:10.1007/s11434-008-0494-z

CrossRef Full Text | Google Scholar

Cressman, G. P. (1959). An operational objective analysis system. Mon. Weather Rev. 87, 367–374. doi:10.1175/1520-0493(1959)087<0367:AOOAS>2.0.CO;2

CrossRef Full Text | Google Scholar

De Marco, A., Proietti, C., Anav, A., Ciancarella, L., D'Elia, I., Fares, S., et al. (2019). Impacts of air pollution on human and ecosystem health, and implications for the national Emission Ceilings Directive: insights from Italy. Environ. Int. 125, 320–333. doi:10.1016/j.envint.2019.01.064

PubMed Abstract | CrossRef Full Text | Google Scholar

Gao, L. N., Yan, P., Mao, J. T., Zhang, X. C., Zhang, X. L., Wu, Y. X., et al. (2021). Ambient atmospheric aerosol extinction coefficient reconstruction from PM2.5 mass concentrations and application to haze identification in China. Aerosol Air Qual. Res. 21, 200386. doi:10.4209/aaqr.200386

CrossRef Full Text | Google Scholar

García-Arroyo, J. A., and Osca, A. (2017). Coping with burnout: analysis of linear, non-linear and interaction relationships. An. De. Psicol. 33, 722–731. doi:10.6018/analesps.33.3.279441

CrossRef Full Text | Google Scholar

Guo, S., Hu, M., Zamora, M. L., Peng, J., Shang, D., Zheng, J., et al. (2014). Elucidating severe urban haze formation in China. Proc. Natl. Acad. Sci., 111, 17373–17378. doi:10.1073/pnas.1419604111

PubMed Abstract | CrossRef Full Text | Google Scholar

Han, X., Zhang, M., Tao, J., Wang, L., Gao, J., Wang, S., et al. (2013). Modeling aerosol impacts on atmospheric visibility in Beijing with RAMS-CMAQ. Atmos. Environ. 72, 177–191. doi:10.1016/j.atmosenv.2013.02.030

CrossRef Full Text | Google Scholar

Hand, J. L., and Malm, W. C. (2007). Review of aerosol mass scattering efficiencies from ground-based measurements since 1990. J. Geophys. Research-Atmospheres 112. doi:10.1029/2007jd008484

CrossRef Full Text | Google Scholar

Hao, X., Li, J., Wang, H., Liao, H., Yin, Z., Hu, J., et al. (2021). Long-term health impact of PM2.5 under whole-year COVID-19 lockdown in China. Environ. Pollut. 290, 118118. doi:10.1016/j.envpol.2021.118118

PubMed Abstract | CrossRef Full Text | Google Scholar

He, Q. S., Li, C. C., Geng, F. H., Yang, H. Q., Li, P. R., Li, T. T., et al. (2012). Aerosol optical properties retrieved from sun photometer measurements over Shanghai, China. J. Geophys. Research-Atmospheres 117. doi:10.1029/2011jd017220

CrossRef Full Text | Google Scholar

Hu, Y., Yao, L., Cheng, Z., and Wang, Y. (2017). Long-term atmospheric visibility trends in megacities of China, India and the United States. Environ. Res. 159, 466–473. doi:10.1016/j.envres.2017.08.018

PubMed Abstract | CrossRef Full Text | Google Scholar

Huang, L. P., Chen, D. H., Deng, L. T., Xu, Z. F., Yu, F., Jiang, Y., et al. (2017). Main technical improvements of GRAPES-Meso V4.0 and verification. J. Appl. Meteorological Sci. 28, 25–37. doi:10.11898/1001-7313.20170103

CrossRef Full Text | Google Scholar

Jishan, X. (2004). Progresses of researches on numerical weather prediction in China: 1999–2002. Adv. Atmos. Sci. 21, 467–474. doi:10.1007/bf02915573

CrossRef Full Text | Google Scholar

Jongeward, A. R., Li, Z. Q., He, H., and Xiong, X. X. (2016). Natural and anthropogenic aerosol trends from satellite and surface observations and model simulations over the North Atlantic Ocean from 2002 to 2012. J. Atmos. Sci. 73, 4469–4485. doi:10.1175/jas-d-15-0308.1

CrossRef Full Text | Google Scholar

Liao, Y. M., and Huang, D. P. (2020). Climate characteristics and change trend of Xiongan New Area. Chin. Agric. Sci. Bull. 36, 99–105. doi:10.11924/j.issn.1000-6850.casb20191201009

CrossRef Full Text | Google Scholar

Liu, Z. D., Wang, H., Peng, Y., Zhang, W. J., and Zhao, M. C. (2022). Multiple regression analysis of low visibility focusing on severe haze-fog pollution in various regions of China. Atmosphere 13, 203. doi:10.3390/atmos13020203

CrossRef Full Text | Google Scholar

Malm, W. C., Sisler, J. F., Huffman, D., Eldred, R. A., and Cahill, T. A. (1994). Spatial and seasonal trends in particle concentration and optical extinction in the United States. J. Geophys. Res. Atmos. 99, 1347–1370. doi:10.1029/93jd02916

CrossRef Full Text | Google Scholar

Mie, G. (1908). Beiträge zur Optik trüber Medien, speziell kolloidaler Metallösungen. Ann. Phys. 330, 377–445. doi:10.1002/andp.19083300302

CrossRef Full Text | Google Scholar

Ohta, S., Murao, N., and Moriya, T. (1990). Evaluation of absorption properties of atmospheric aerosols at solar wavelengths based on chemical characterization. Atmos. Environ. Part A. General Top. 24, 1409–1416. doi:10.1016/0960-1686(90)90048-R

CrossRef Full Text | Google Scholar

Pitchford, M., Malm, W., Schichtel, B., Kumar, N., Lowenthal, D., and Hand, J. (2007). Revised algorithm for estimating light extinction from IMPROVE particle speciation data. J. Air & Waste Manag. Assoc. 57, 1326–1336. doi:10.3155/1047-3289.57.11.1326

CrossRef Full Text | Google Scholar

Su, H., Cheng, Y., and Pöschl, U. (2020). New multiphase chemical processes influencing atmospheric aerosols, air quality, and climate in the anthropocene. Accounts Chem. Res. 53, 2034–2043. doi:10.1021/acs.accounts.0c00246

PubMed Abstract | CrossRef Full Text | Google Scholar

Sumlin, B. J., Heinson, W. R., and Chakrabarty, R. K. (2018). Retrieving the aerosol complex refractive index using PyMieScatt: a Mie computational package with visualization capabilities. J. Quantitative Spectrosc. Radiat. Transf. 205, 127–134. doi:10.1016/j.jqsrt.2017.10.012

CrossRef Full Text | Google Scholar

Ting, Y.-C., Young, L.-H., Lin, T.-H., Tsay, S.-C., Chang, K.-E., and Hsiao, T.-C. (2022). Quantifying the impacts of PM2.5 constituents and relative humidity on visibility impairment in a suburban area of eastern Asia using long-term in-situ measurements. Sci. Total Environ. 818, 151759. doi:10.1016/j.scitotenv.2021.151759

PubMed Abstract | CrossRef Full Text | Google Scholar

Wang, J. K., Xie, C., Zhang, T. H., Zhang, B. H., Zhang, H. D., and Rao, X. Q. (2020). Modification of visibility parameterization scheme and its application evaluation. J. Environ. Eng. Technol. 10, 330–337. doi:10.12153/j.issn.1674-991X.20190186

CrossRef Full Text | Google Scholar

Xie, Y., Koch, S., McGinley, J., Albers, S., Bieringer, P. E., Wolfson, M., et al. (2011). A space-time multiscale analysis system: a sequential variational analysis approach. Mon. Weather Rev. 139, 1224–1240. doi:10.1175/2010mwr3338.1

CrossRef Full Text | Google Scholar

Yue, X., Unger, N., Harper, K., Xia, X., Liao, H., Zhu, T., et al. (2017). Ozone and haze pollution weakens net primary productivity in China. Atmos. Chem. Phys. 17, 6073–6089. doi:10.5194/acp-17-6073-2017

CrossRef Full Text | Google Scholar

Zhan, C. L., Wei, C., Liu, Z. G., Liu, H. X., Yang, X. F., Zheng, J. R., et al. (2025). Seasonal trends and light extinction effects of PM2.5 chemical composition from 2021 to 2022 in a typical industrial city of central China. Atmos. Environ. 341, 120922. doi:10.1016/j.atmosenv.2024.120922

CrossRef Full Text | Google Scholar

Zhang, H. Y., Wang, X. J., Lv, L. Y., Li, G. H., Liu, X. Y., Li, X., et al. (2023). Insights into quantitative evaluation technology of PM2.5 transport at multi-perspective and multi-spatial and temporal scales in the North China Plain. Environ. Pollut. 338, 122693. doi:10.1016/j.envpol.2023.122693

PubMed Abstract | CrossRef Full Text | Google Scholar

Zhao, P. S., Ding, J., Du, X., and Su, J. (2019). High time-resolution measurement of light scattering hygroscopic growth factor in beijing: a novel method for high relative humidity conditions. Atmos. Environ. 215, 116912. doi:10.1016/j.atmosenv.2019.116912

CrossRef Full Text | Google Scholar

Zhuang, Z. R., Jiang, Y., Tian, W. H., Huang, L. P., Li, X. L., and Deng, L. T. (2023). Hourly rapid updating assimilation forecast system of CMA-MESO and preliminary analysis of short-term forecasting effect. Chin. J. Atmos. Sci. (In Chinese) 47, 925–942. doi:10.3878/j.issn.1006-9895.2206.21090

CrossRef Full Text | Google Scholar