Next Article in Journal
Possible Mechanisms Contributing to the Occurrence of a Waterspout in Victoria Harbour, Hong Kong, on 28 September 2024: Observational and Numerical Studies
Previous Article in Journal
Long-Term Integrated Measurements of Aerosol Microphysical Properties to Study Different Combustion Processes at a Coastal Semi-Rural Site in Southern Italy
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Atmosphere
Volume 16
Issue 7
10.3390/atmos16070867
atmosphere-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Local Emissions Drive Summer PM2.5 Pollution Under Adverse Meteorological Conditions: A Quantitative Case Study in Suzhou, Yangtze River Delta

by
Minyan Wu
1,*,
Ningning Cai
1,
Jiong Fang
2,
Ling Huang
2,
Xurong Shi
3,
Yezheng Wu
1,
Li Li
2 and
Hongbing Qin
4,5,*
1
Suzhou Environmental Monitoring Station, Suzhou 215000, China
2
School of Environmental and Chemical Engineering, Shanghai University, Shanghai 200444, China
3
Center of Air Quality Simulation and System Analysis, Chinese Academy of Environmental Planning, Beijing 100041, China
4
School of Environmental Science and Engineering, Suzhou University of Science and Technology, Suzhou 215009, China
5
Jiangsu Provincial Key Laboratory of Environmental Science and Engineering, Suzhou 215009, China
*
Authors to whom correspondence should be addressed.
Atmosphere 2025, 16(7), 867; https://doi.org/10.3390/atmos16070867
Submission received: 30 May 2025 / Revised: 1 July 2025 / Accepted: 10 July 2025 / Published: 16 July 2025
(This article belongs to the Section Air Quality)
Browse Figures
Versions Notes

Abstract

Accurately identifying the sources of fine particulate matter (PM2.5) pollution is crucial for pollution control and public health protection. Taking the PM2.5 pollution event that occurred in Suzhou in June 2023 as a typical case, this study analyzed the characteristics and components of PM2.5, and quantified the contributions of meteorological conditions, regional transport, and local emissions to the summertime PM2.5 surge in a typical Yangtze River Delta (YRD) city. Chemical composition analysis highlighted a sharp increase in nitrate ions (NO3−, contributing up to 49% during peak pollution), with calcium ion (Ca2+) and sulfate ion (SO42−) concentrations rising to 2 times and 7.5 times those of clean periods, respectively. Results from the random forest model demonstrated that emission sources (74%) dominated this pollution episode, significantly surpassing the meteorological contribution (26%). The Weather Research and Forecasting model combined with the Community Multiscale Air Quality model (WRF–CMAQ) further revealed that local emissions contributed the most to PM2.5 concentrations in Suzhou (46.3%), while external transport primarily originated from upwind cities such as Shanghai and Jiaxing. The findings indicate synergistic effects from dust sources, industrial emissions, and mobile sources. Validation using electricity consumption and key enterprise emission data confirmed that intensive local industrial activities exacerbated PM2.5 accumulation. Recommendations include strengthening regulations on local industrial and mobile source emissions, and enhancing regional joint prevention and control mechanisms to mitigate cross-boundary transport impacts.

1. Introduction

Ambient fine particulate matter (PM2.5) is a critical atmospheric pollutant with adverse effects on human health, ecosystems, and climate systems [1,2,3]. PM2.5 exposure has been proven to have a strong relationship with the increased risks of respiratory, cardiovascular, and gastrointestinal diseases [4,5,6,7,8,9]. Since the implementation of the Air Pollution Prevention and Control Action Plan in 2013, China has enforced stricter pollution standards, resulting in significant air quality improvements, including a notable reduction in PM2.5 levels [10,11]. PM2.5 concentrations fell by 42% between 2015 and 2022. Despite the achievements, new challenges and regional disparities persist, particularly in economically dynamic areas such as the Yangtze River Delta (YRD).
As one of China’s most industrialized and populous regions, the YRD is facing unique air quality challenges due to intensive industrial activity, a high population density, and complex meteorological conditions [11,12,13,14]. Suzhou, a YRD megacity with over 10 million residents and strong industrial bases, exemplifies these problems well. Massive industrial emissions coupled with the high vehicular density, render Suzhou a hotspot for PM2.5 accumulation [15,16,17]. Historically, PM2.5 pollution events have been commonly observed in winter due to stagnant meteorological conditions and cross-regional transport from northern regions like Beijing–Tianjin–Hebei (BTH) [18,19], while summer typically exhibits the lowest PM2.5 concentrations, attributed to favorable dispersion conditions [20]. However, in recent years, an anomalous rise in PM2.5 levels in the YRD have been witnessed in summer, often coinciding with increases in ozone pollution [21,22,23]. Since 2020, Suzhou, a typical city, has experienced frequent simultaneous increases in ozone and PM2.5 levels during the summer, which rarely occurred before 2019. Additionally, in recent years, regions such as BTH, the Sichuan–Chongqing region, and the Fenwei Plain have also experienced combined PM2.5 and O3 pollution during the summer [24,25,26,27]. Therefore, it is essential to analyze the sources of a typical PM2.5 and O3 co-pollution event that occurred in June 2023. Existing studies have extensively analyzed long-term concentration trends; however, critical gaps remain in understanding the drivers of short-term, high-intensity pollution events, which are characteristically driven by instantaneous synergistic effects of meteorological conditions; trans-regional transmission; and abrupt local emissions.
At present, the method of using an air quality model to analyze the source of PM2.5 is relatively mature, with a wide range of applications, the ability to carry out multi-scale and high-resolution simulation, and a complete physical and chemical mechanism [28,29,30,31,32,33,34,35]. However, the analytical accuracy of local complex pollution sources is still limited, especially in the areas where meteorological conditions and emission sources interact intensively [36]. The data dependence of the air quality model is high and the real-time performance is limited, and long-term large-scale air quality simulation still needs a large amount of computing resources [37]. Emerging machine learning techniques, particularly random forest models, offer complementary advantages by capturing nonlinear interactions between meteorological and emission variables with higher computational efficiency [38,39]. Studies combining machine learning and an air quality model have shown better performance in assessing air pollution impacts compared to traditional models [40,41,42].
This study addresses these gaps by applying random forest modeling, The Weather Research and Forecasting model combined with the Community Multiscale Air Quality model (WRF–CMAQ) simulations, and high-resolution observational data to dissect a representative PM2.5 pollution event in Suzhou during June 13–23 2023—an event that impacted multiple major cities across the YRD during summer, a season usually characterized by lower PM2.5 pollution levels. We aim to (1) quantify the contributions of meteorology, regional transport, and local emissions to PM2.5 surges, (2) identify dominant pollution sources through chemical composition and sectoral analysis, and (3) propose actionable strategies for mitigating short-term pollution events. Our findings provide a scientific foundation for refining regional air quality management in the YRD, emphasizing the need for coordinated emission controls and adaptive governance frameworks.

2. Materials and Methods

2.1. Data Source

The data for six conventional pollutants (PM2.5, PM10, sulfur (SO2), nitrogen dioxide (NO2), carbon monoxide (CO), ozone (O3)) as well as meteorological parameters such as temperature, humidity, and wind speed were obtained from 10 national air quality monitoring stations. PM2.5 and PM10 are monitored using a particulate matter monitor (5030i, Thermo Fisher Scientific, Waltham, MA, USA), which utilizes a dual-method hybrid approach to measure PM2.5 and PM10, combining β-ray absorption (beta attenuation) and light scattering (nephelometry) technologies for high-precision, real-time monitoring. According to the relevant standards issued by the Ministry of Ecology and Environment of China [43,44], PM2.5 and PM10 are maintained and quality controlled, and data are reviewed by the relevant governmental departments to ensure the accuracy of the data. The chemical composition of PM2.5, including elemental carbon (EC), organic matter (OM, which is calculated by organic carbon (OC) multiplied by 1.6 [45]), calcium ions (Ca2+), magnesium ions (Mg2+), potassium ions (K+), ammonium ions (NH4+), sodium ions (Na+), sulfate ions (SO42−), nitrate ions (NO3−), chloride ions (Cl−), and fluoride ions (F−), was observed at the South Gate site—the only national particulate-matter component monitoring site in Suzhou (Figure 1). The PM2.5 data characteristics of the 10 national air quality monitoring stations and the South Gate site are consistent. The online measurement of water-soluble ions in PM2.5 is conducted using online ion chromatography (2060 MARGA, Metrohm, Herisau, Switzerland). The concentrations of OC and EC in PM2.5 are measured using the OC/EC online monitoring analyzer (RT4, Sunset Lab, Tigard, OR, USA), with a time resolution of 1 h. To ensure the accuracy of the data, regular checks on instrument flow rates, the replacement of accessories, air-tightness tests, and backups of original data are performed. The data were reviewed and audited by the National Environmental Monitoring Center and assessed in accordance with standards issued by the Ministry of Ecology and Environment of China, ensuring reliability and accuracy to analyze the effects of anthropogenic emissions and meteorological factors on PM2.5 concentrations.
Electricity consumption data, used to evaluate the activity level in Suzhou, were sourced from the Suzhou Statistical Yearbook, published by the Suzhou Bureau of Statistics. Additionally, emissions data for key air-polluting enterprises, including sulfur dioxide, nitrogen oxides, and particulate matter, were obtained from automatically monitored data linked to the national network, with a valid data transmission rate of over 98%.

2.2. Random Forest Model and Validation

In this study, a random forest model [46,47,48,49] was used to predict pollutant concentrations based on time and meteorological variables, and to differentiate the relative contributions of meteorology and emissions to pollutant levels The random forest model is a widely used machine learning model. Specifically, the time variables include the Unix time, year, week, and hour, representing the long-term, annual, weekly, and daily variation characteristics of pollutant emissions. The meteorological variables used in the random forest model include the wind speed (WS, calculated from U10 and V10), wind direction (WD, calculated from U10 and V10), temperature (T), relative humidity (RH, calculated from T and the dewpoint temperature), surface solar radiation (SSR), surface pressure (SP), total precipitation (TP), boundary layer height (BLH), total cloud cover (TCC), length of each air mass trajectory (Length), and cluster of each air mass trajectory (Cluster), to fully account for the impact of meteorology on pollutants. The meteorological parameters above were extracted from the Global Reanalysis Data published by NOAA (National Oceanic and Atmospheric Administration), while Length and Cluster were generated by the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model.
A “random sampling–model prediction–concentration averaging” method was adopted to standardize the meteorological conditions and obtain the pollutant concentration changes unaffected by meteorology. First, a random forest model was developed to predict pollutant concentrations. Then, while keeping the Unix time column unchanged, historical meteorological data were randomly sampled for each data point to form a new dataset with shuffled sequences. This new dataset was fed into the random forest model to predict a time series of pollutant concentrations. This “random sampling–model prediction” process was repeated 1000 times, and the 1000 prediction results were averaged to obtain pollutant concentrations considering almost all meteorological conditions. These meteorologically standardized pollutant concentrations reflected changes solely due to emissions.

2.3. WRF–CMAQ Model and Validation

The WRF–CMAQ model was employed in this study to analyze the contributions of pollutant transport to Suzhou’s PM2.5 levels on June 13–23 2023, as well as to conduct source apportionment for PM2.5.
The WRF physical process and parameterization scheme are shown in Table 1. The WRF–CMAQ model considers chemical mechanisms such as the gas phase, liquid phase, and heterogeneous phase, so the calculation results of PM2.5 concentrations in the model have included the influence of the generation of secondary particles such as nitrate. The 2 m temperature (T2), 10 m wind speed (WS10), and 2 m relative humidity (RH) simulated by the WRF model were compared with surface observation data to evaluate the accuracy of the meteorological field simulation. The performance of the model was assessed using statistical indicators such as Normalized Mean Bias (NMB), correlation coefficient (R), Index of Agreement (IOA), and root mean square error (RMSE) (Table 2). According to the evaluation results, the temperature simulation performed the best, with an R value of 0.84 and a RMSE of 2.7, although the model exhibited a slight overestimation overall. The RMSE of wind speed in the Suzhou area during this period, as calculated, was 2.0 m/s, indicating a certain degree of overestimation by the model. Regarding humidity, despite a relatively high RMSE, the correlation remained robust (R = 0.79) (detailed information is provided in the SI Figures S1 and S2).
This study used the three-level nested CMAQv5.3.2 model to simulate air quality across the Yangtze River Delta region [50,51]. The outermost domain (D01) had a horizontal resolution of 36 km, covering all of China, with 174 × 136 grid points and a central longitude–latitude of (110° E, 34° N). The second domain (D02) had a horizontal resolution of 12 km, covering eastern China, with 135 × 228 grid points. The innermost domain (D03) had a horizontal resolution of 4 km, with 192 × 216 grid points, covering the entire Yangtze River Delta region. The projection was consistent with the WRF model, using the Lambert Conformal Conic projection. Initial and boundary conditions for D01 were derived from the default CMAQ configuration, while the boundary conditions for D02 and D03 were obtained from the simulation results of D01 and D02, respectively. Since the initial conditions for all three layers used default configuration files, the simulation was run with a 5-day spin-up period to reduce the impact of initial conditions. The Multi-resolution Emission Inventory for China (MEIC inventory, Tsinghua University, Beijing, China), developed by Tsinghua University, was used for the 36 km, 12 km, and 4 km grids (except for the three provinces and one city in the Yangtze River Delta). The emission inventory for Jiangsu province (JS), Zhejiang province (ZJ), Anhui province, and Shanghai was updated using the 2018 city statistical yearbooks and 2017 environmental statistics. Natural VOC emissions were calculated using the Model of Emissions of Gases and Aerosols from Nature version 3.0 (MEGAN3.0 model) (Figure 2).
The model’s performance was evaluated by comparing observed PM2.5 concentrations in Suzhou during 13–23 June 2023, with those simulated by the CMAQ model. Statistical indicators used for the evaluation included the Normalized Mean Bias (NMB) and the Index of Agreement (IOA). According to the evaluation results, both the NMB and IOA values met the recommended benchmarks proposed by Huang et al., 2021 [52], indicating that the model results are reliable and capable of accurately simulating the distribution of PM2.5 concentrations during this period.

3. Results and Discussions

3.1. Monitoring Data

3.1.1. Regional Air Quality Overview

The PM2.5 concentration of Suzhou and surrounding cities in June has generally decreased year by year since 2015. In June 2022, PM2.5 concentrations in cities across Suzhou and surrounding cities remained below 20 µg/m3 (Figure 3). However, by June 2023, PM2.5 concentrations worsened across the region, with Suzhou experiencing a 20% increase year on year. Comparatively, cities to Suzhou’s northwest and north, such as Changzhou, Zhenjiang, Nantong, and Wuxi, saw PM2.5 increases of 14%, 8%, 6%, and 5%, respectively. Meanwhile, cities to the east and south, such as Huzhou, Shanghai, and Jiaxing, experienced sharper PM2.5 increases of 42%, 30%, and 16%, respectively. Overall, in June 2023, PM2.5 concentrations worsened to varying degrees across the YRD region, with Suzhou’s increase being moderate. Cities southeast of Suzhou experienced more severe deteriorations in PM2.5 levels compared to those in the northwest.

3.1.2. Analysis of PM2.5 Pollution Characteristics and Components in Suzhou During the Pollution Period

From 2018 to 2021, PM2.5 concentrations in Suzhou exhibited a year-on-year declining trend during June, reaching 19 µg/m3 in 2021. However, levels rebounded to 20 µg/m3 in June 2022 and increased sharply to 24 µg/m3 in June 2023—a 20% rise from June 2022 (Figure 4). From 13 to 23 June 2023, Suzhou experienced two consecutive increases in the PM2.5 concentration. Throughout the study period, daily PM2.5 concentrations persistently exceeded the WHO AQG-2021 daily guideline value (15 μg/m3), with the exception of 17–19 June. On 15 June, the first peak day saw an average daily value of 36 µg/m3, and on June 21, the second peak day recorded an average daily value of 42 µg/m3, exceeding the June average of 18 µg/m3. Hourly data showed that the hourly PM2.5 concentration peaked at 69 µg/m3 in the evening of June 15 and at 76 µg/m3 at midnight on 22 June.
The chemical composition of PM2.5 was selected from 13 to 23 June 2023 to analyze the main sources of pollutants during the high-concentration period. Figure 5 shows the hourly variations in PM2.5 chemical components at the Suzhou South Gate Station and in O3, SO2, and NO2 concentrations at the Wuzhong District Station, with the nearest state atmospheric monitoring station being located southwest of the South Gate Station. During periods of high PM2.5 concentrations, nitrate levels increased significantly, directly driving the rise in PM2.5. On the evening of June 21, nitrate concentrations sharply increased from 6.6 µg/m3 to 31 µg/m3 within only three hours. During this time, nitrate’s contribution to PM2.5 increased from 20% to 49%, and the contribution of secondary inorganic components (sulfate, nitrate, and ammonium) rose from 71% to 88%. This indicates that the main driver of the PM2.5 increase was nitrate and other secondary inorganic components. Nitrogen oxides (NOx), SO2, and ammonia (NH3) are the main precursors for the formation of NO3−, SO42−, and NH4+ [53], suggesting that emissions of these precursor gases had increased during this period. Suzhou is highly industrialized with sectors such as steel production, power generation, and cement manufacturing, along with having the highest number of motor vehicles in Jiangsu Province. Considering this, the increased precursor concentration probably resulted from the increased emissions from industrial sources and the vehicle sector.
To compare, we also analyzed the ion composition of PM2.5 during a clean period (18 June, 08:00, to 19 June, 13:00, with an average PM2.5 concentration of 12 µg/m3) and a pollution period (22 June, 01:00, to 23 June, 06:00, with an average PM2.5 concentration of 42 µg/m3). During the pollution period, the average Ca2+ concentration was 0.3 µg/m3, which was twice as high as during the clean period, indicating a significant increase in dust source contributions, which was mainly influenced by the emission of local dust sources, and the level of the impact was enhanced by the adverse weather conditions. Additionally, the average sulfate concentration during the pollution period was 8.5 µg/m3, which was 7.5 times higher than during the clean period, reflecting a marked increase in industrial source contributions.

3.2. Quantifying the Contributions of Meteorology and Emissions

Meteorological conditions and pollutant emissions are the primary factors influencing PM2.5 concentrations, with varying contributions depending on the time and location. The random forest model can analyze meteorological and pollution source contributions in a short amount of time through dynamic data learning. Therefore, this study quantified the contribution of meteorology and emissions to PM2.5 in Suzhou from June 2019 to 2023 using the random forest model based on meteorological data and PM2.5 data in Suzhou every June from 2019 to 2023. The specific method is detailed in the Materials and Methods section.
In comparison to the same period in 2022, June 2023 saw a 2% increase in the average humidity in Suzhou, a 0.5 m/s decrease in wind speed, a 1.3 °C drop in temperature, a 2 hPa increase in the average atmospheric pressure, and a 15.5% increase in rainfall (Table 3). These conditions were more stable and less favorable for the dispersion of pollutants compared to 2022.
The results indicated that meteorological conditions in June 2023 were more adverse for pollution dispersion than those of June 2022, while the contribution of emissions to PM2.5 increased significantly in June 2023 compared to the same period in 2022. From the results of the random forest model, the meteorological and emission factors contributed to PM2.5 concentration increases of 0.7 µg/m3 and 2.0 µg/m3, respectively, accounting for 26% and 74% of the total increase. Emissions were the dominant factor in the rise of PM2.5 in Suzhou in June 2023.

3.3. Local and Transmission Contributions of PM2.5

The concentration of air pollutants is influenced by both local emissions and transboundary transport. Quantifying the contribution of external sources to Suzhou’s air quality, in addition to local emissions, is crucial for guiding regional joint pollution prevention and control.
The WRF–CMAQ model was employed to analyze the sources of pollution during the high PM2.5 period from 13 to 23 June 2023. Local sources contributed 46.3% to Suzhou’s PM2.5, while contributions from Shanghai, Jiaxing, and Huzhou contributed 12.5%, 7.3%, and 1.9%, respectively, to the PM2.5 concentration in Suzhou. Other cities in Anhui, Jiangsu, and Zhejiang provinces also had significant impacts on Suzhou. The results show that local sources contributed the most to the PM2.5 concentrations in Suzhou during this pollution event, indicating that local anthropogenic emissions play a decisive role in this pollution process. At the same time, the contribution of exogenous transmission mainly comes from southern and southeastern cities.
In detail, we investigated the hourly contribution ratio of PM2.5 in Suzhou from 13 to 23 June 2023 (see Figure 6 and Figure 7). The local source contribution of PM2.5 concentrations in Suzhou has always been at a high level throughout the whole pollution process. During the increasing PM2.5 episode, local contributions in Suzhou rose significantly, especially at night when wind speeds decreased, leading to a stable atmosphere. During the late night of 22 June and early morning of 23 June, local sources accounted for over 60% of the PM2.5 increase, especially highlighting the importance of controlling local emissions. From the transmission of surrounding cities, on 17 and 18 June, the prevailing southeast winds brought pollution from Shanghai, while on 23 June, southerly winds transported pollutants from Jiaxing. The above result suggests that strengthening coordinated regional pollution control, particularly from cities upwind of Suzhou, is essential for reducing transboundary impacts to PM2.5 concentrations in Suzhou. However, it should be noted that the WRF model overestimated the wind speeds during 18 June to 20 June, which might lead to underestimated contributions from local emissions.
Moreover, based on these findings, we applied the WRF–CMAQ air quality model to analyze the contribution of local emission sources to PM2.5 in Suzhou during the period from 13 June to 23 June 2023 (Figure 8), where the ‘other’ sources encompass the contributions of agriculture, biomass burning, and residential activities to PM2.5. Overall, dust sources contributed the most to PM2.5 concentrations, accounting for 39.9% (ranging from 31.9% to 50.0%), followed by industrial sources, which contributed 30.4% (ranging from 21.3% to 38.4%). The day-by-day source apportionment results show that during the pollution period (21–22 June), compared to the clean period (17–19 June), the contribution rates of dust sources, mobile sources, and industrial boilers and kilns increased significantly. This indicates that dust sources, mobile sources, and industrial boilers and kilns were the dominant contributors during the high PM2.5 period. The source of dust was dominant during the high-concentration period of PM2.5, which was consistent with the results of a significant increase in calcium ion concentration during the pollution period in the previous analysis of the chemical composition of PM2.5.
To further verify the significance of the impact of local industrial pollution emissions, we conducted an electricity consumption analysis of the whole society and pollutant emissions of key polluting enterprises in Suzhou. The electricity consumption of the whole society includes electricity consumption in primary industries, secondary industries, and tertiary industries, and the domestic electricity consumption of urban and rural residents. Firstly, the electricity consumption data were used to evaluate Suzhou’s activity level, showing a clear increase in social activity following the end of the Coronavirus Disease 2019 (COVID-19) pandemic. This activity level is reflected in and total electricity consumption, which directly correlates with pollutant emissions. As is shown in Figure 9a, from February to May 2023, the total electricity consumption in Suzhou increased significantly compared to the same period in the previous year, with year-on-year increases ranging from 1% to 21%, and slightly decreasing in June. In June 2023, the total electricity consumption in Suzhou reached 14.5 billion kilowatt-hours, the highest since 2023. As is illustrated in Figure 9b, the monthly emissions of pollutants from key polluting enterprises in Suzhou followed a pattern similar to the electricity consumption. In January 2023, NOx and SO2 emissions decreased year on year compared to January 2022 due to the impact of the COVID-19 pandemic. From February to June 2023, NOx and SO2 emissions rose year on year compared with the same period in 2022 as the economy recovered. In particular, in June 2023, the NOx and SO2 emissions of key polluting enterprises in Suzhou reached 58 t and 22 t, with both being the highest since 2023. To sum up, both the electricity consumption of the whole society and pollutant discharge of key polluting enterprises in Suzhou are at a high level, and local emissions have a significant contribution to the concentration of PM2.5 in Suzhou, so the local industrial emission control should be strengthened.

4. Conclusions

This study quantitatively investigated the drivers of a summer PM2.5 pollution event in Suzhou, a representative city in the YRD, by integrating the WRF–CMAQ simulation, the random forest model, and high-resolution observational data. Key findings reveal the following: (1) During high-concentration PM2.5 episodes, the atmospheric stagnation significantly facilitated PM2.5 accumulation, and the analysis of PM2.5 components showed that industrial emissions, mobile sources, and dust sources had a synergistic contribution. (2) The random forest model shows that emission contributions are the main cause of pollution; the WRF–CMAQ model shows that local emissions contribute significantly to PM2.5 in Suzhou. The contribution of external transport primarily originated from southeastern upwind cities. (3) The source analysis results show that dust sources, mobile sources, and industrial boilers and kilns are the main contributors in local emission sources.
These results underscore the critical role of localized industrial and vehicular emissions in exacerbating short-term PM2.5 pollution, particularly under adverse meteorological conditions. To mitigate such episodes, we recommend (1) stringent regulation of industrial boilers, kilns, and dust-generating activities, (2) enhanced control of vehicular emissions through traffic management and cleaner energy transitions, and (3) robust regional joint prevention mechanisms to address cross-boundary transport. Additionally, improving the accuracy of real-time emission monitoring and operational data quality control is imperative for effective policymaking. Future research should systematically evaluate the efficacy of these mitigation strategies and expand the framework to other YRD cities to inform scalable air quality management solutions.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/atmos16070867/s1, Figure S1. Comparison of simulated wind speed and observation value. Figure S2. Comparison of simulated PM2.5 concentration and observation value in other cities in Yangtze River Delta and Suzhou. Figure S3. Model domain setup.

Author Contributions

Conceptualization, M.W., X.S. and H.Q.; Methodology, M.W., N.C., X.S. and L.L.; Software, J.F., L.H. and L.L.; Validation, J.F., L.H. and Y.W.; Writing—original draft, M.W. and N.C.; Writing—review & editing, M.W., N.C. and X.S.; Visualization, N.C.; Supervision, H.Q. All authors have read and agreed to the published version of the manuscript.

Funding

Suzhou Science and Technology Project: SS202142.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.

Acknowledgments

The authors thank Minfeng Zhou at the Jiangsu Suzhou Environmental Monitoring Center and Miao Ning and Xin Zhang at the Chinese Academy of Environmental Planning for their technical help and useful discussions.

Conflicts of Interest

The authors declare no conflict of interest.

Abbreviations

The following abbreviations are used in this manuscript:
BLHBoundary layer height
BTHBeijing–Tianjin–Hebei
Ca2+Calcium ion
Cl−Chloride Ion
COCarbon monoxide
COVID-19Coronavirus Disease 2019
ECElemental carbon
F−Fluoride ion
HYSPLITHybrid Single-Particle Lagrangian Integrated Trajectory
IOAIndex of Agreement
JSJiangsu province
K+Potassium ion
MEGAN3.0 modelModel of Emissions of Gases and Aerosols from Nature version 3.0
MEIC inventoryThe Multi-resolution Emission Inventory for China
Mg2+Magnesium ion
Na+Sodium ion
NH3Ammonia
NH4+Ammonium ion
NMBNormalized mean bias
NO2Nitrogen dioxide
NO3−Nitrate ion
NOAANational Oceanic and Atmospheric Administration
NOxNitrogen oxides
O3Ozone
OCOrganic carbon
OMOrganic matter
PM10Particles with a diameter of less than 10 μm
PM2.5Particles with a diameter of less than 2.5 μm
RCorrelation coefficient
RHRelative humidity
RMSERoot Mean Square Error
RRTMRapid Radiative Transfer Model
SO2Sulfur
SO42−Sulfate ion
SPSurface pressure
SSRSurface solar radiation
TTemperature
T2The 2 m temperature
TCCTotal cloud cover
TPTotal precipitation
WDWind direction
WRF–CMAQThe Weather Research and Forecasting model combined with the Community Multiscale Air Quality model
WSWind speed
WS1010 m wind speed
YRDYangtze River Delta
YSUYonsei University
ZJZhejiang province

References

  1. Zheng, Y.X.; Xue, T.; Zhang, Q.; Geng, G.N.; Tong, D.; Li, X.; He, K.B. Air quality improvements and health benefits from China’s clean air action since 2013. Environ. Res. Lett. 2017, 12, 114020. [Google Scholar] [CrossRef]
  2. Liu, H.J.; Wu, B.B.; Liu, S.H.; Shao, P.Y.; Liu, X.Y.; Zhu, C.Y.; Wang, Y.; Wu, Y.M.; Xue, Y.F.; Gao, J.J.; et al. A regional high-resolution emission inventory of primary air pollutants in 2012 for Beijing and the surrounding five provinces of North China. Atmos. Environ. 2018, 181, 20–33. [Google Scholar] [CrossRef]
  3. Huang, X.J.; Tang, G.Q.; Zhang, J.K.; Liu, B.X.; Liu, C.; Zhang, J.; Cong, L.L.; Cheng, M.T.; Yan, G.X.; Gao, W.K.; et al. Characteristics of PM2.5 pollution in Beijing after the improvement of air quality. J. Environ. Sci. 2021, 100, 1–10. [Google Scholar] [CrossRef]
  4. Ding, A.J.; Huang, X.; Nie, W.; Sun, J.N.; Kerminen, V.-M.; Petäjä, T.; Su, H.; Cheng, Y.F.; Yang, X.-Q.; Wang, M.H.; et al. Enhanced haze pollution by black carbon in megacities in China. Geophys. Res. Lett. 2016, 43, 2873–2879. [Google Scholar] [CrossRef]
  5. Gao, J.; Woodward, A.; Vardoulakis, S.; Kovats, S.; Wilkinson, P.; Li, L.P.; Xu, L.; Li, J.; Yang, J.; Li, J.; et al. Haze, public health and mitigation measures in China: A review of the current evidence for further policy response. Sci. Total Environ. 2017, 578, 148–157. [Google Scholar] [CrossRef]
  6. Zhang, R.; Wang, G.; Guo, S.; Zamora, M.L.; Ying, Q.; Lin, Y.; Wang, W.G.; Hu, M.; Wang, Y. Formation of urban fine particulate matter. Chem. Rev. 2015, 115, 3803–3855. [Google Scholar] [CrossRef]
  7. Palmer, K.T.; McNeill-Love, R.; Poole, J.R.; Coggon, D.; Frew, A.J.; Linaker, C.H.; Shute, J.K. Inflammatory responses to the occupational inhalation of metal fume. Eur. Respir. J. 2006, 27, 366–373. [Google Scholar] [CrossRef]
  8. Wang, G.H.; Zhang, R.Y.; Gomez, M.E.; Yang, L.X.; Zamora, M.L.; Hu, M.; Lin, Y.; Peng, J.F.; Guo, S.; Meng, J.J.; et al. Persistent sulfate formation from London Fog to Chinese haze. Proc. Natl. Acad. Sci. USA 2016, 113, 13630–13635. [Google Scholar] [CrossRef] [PubMed]
  9. Lei, W.K.; Li, X.R.; Zhang, L.; Xu, J.; Zhao, W.J.; Liu, Z.R. Pollution characteristics and health risk assessment of heavy metals in PM2.5 collected in Baoding. Environ. Sci. 2021, 42, 38–44. [Google Scholar]
  10. Ministry of Ecology and Environment of People’s Republic of China: Report on the State of the Ecology and Environment in China 2022. Available online: https://english.mee.gov.cn/Resources/Reports/soe/SOEE2019/202312/P020231206607194894058.pdf (accessed on 9 May 2024).
  11. Zhang, Q.; Zheng, Y.; Tong, D.; Shao, M.; Wang, S.X.; Zhang, Y.H.; Xu, X.D.; Wang, J.N.; He, H.; Liu, W.; et al. Drivers of improved PM2.5 air quality in China from 2013 to 2017. Proc. Natl. Acad. Sci. USA 2019, 116, 24463–24469. [Google Scholar] [CrossRef]
  12. Shu, L.; Wang, T.J.; Xie, M.; Li, M.M.; Zhao, M.; Zhang, M.; Zhao, X.Y. Episode study of fine particle and ozone during the CAPUM-YRD over Yangtze River Delta of China: Characteristics and source attribution. Atmos. Environ. 2019, 203, 87–101. [Google Scholar] [CrossRef]
  13. Gao, Y.Q.; Wang, H.L.; Jing, S.A.; Qiao, L.P.; Li, L.; Zhu, S.H.; Lou, S.R.; Tao, S.K.; Feng, J.L.; Li, X.; et al. Chemical characterization, spatial distribution, and source identification of organic matter in PM2.5 in summertime Shanghai, China. Environ. Sci. 2018, 39, 1978–1986. [Google Scholar]
  14. Hua, N.; Shang, Y.; Xie, M.J. Variations in PM2.5 Composition and Sources During 2020–2021 COVID-19 Epidemic Periods in Nanjing. Environ. Sci. 2023, 44, 593–601. [Google Scholar]
  15. Tian, M.; Wang, H.B.; Chen, Y.; Yang, F.M.; Zhang, X.H.; Zou, Q.; Zhang, R.Q.; Ma, Y.L.; He, K.B. Characteristics of aerosol pollution during heavy haze events in Suzhou, China. Atmos. Chem. Phys. 2016, 16, 7357–7371. [Google Scholar] [CrossRef]
  16. Wang, N.F.; Chen, Y.; Hao, Q.J.; Wang, H.B.; Yang, F.M.; Zhao, Q.; Bo, Y.; He, K.B.; Yao, Y.G. Seasonal Variation and Source Analysis of the Water-soluble Inorganic lons in Fine Particulate Matter in Suzhou. Environ. Sci. 2016, 37, 4482–4489. [Google Scholar]
  17. Miao, Q.; Yang, Q.; Wu, Y.Z.; Wei, H.; Zhou, M.F.; Zhang, X.H.; Zou, Q. Concentration Variation and Source Analysis of Metal Elements in PM2.5 During COVID-19 Control in Suzhou. Environ. Sci. 2022, 43, 2851–2857. [Google Scholar]
  18. Huang, X.; Ding, A.J.; Wang, Z.L.; Ding, K.; Gao, J.; Chai, F.K.; Fu, C.B. Amplified transboundary transport of haze by aerosol–boundary layer interaction in China. Nat. Geosci. 2020, 13, 428–434. [Google Scholar] [CrossRef]
  19. Qin, M.M.; Hu, A.Q.; Mao, J.J.; Li, X.; Sheng, L.; Sun, J.J.; Li, J.Y.; Wang, X.S.; Zhang, Y.H.; Hu, J.L. PM2.5 and O3 relationships affected by the atmospheric oxidizing capacity in the Yangtze River Delta, China. Sci. Total Environ. 2022, 810, 152268. [Google Scholar] [CrossRef]
  20. Wu, S.Q.; Yao, J.Q.; Yang, R.; Zhang, S.W.; Zhao, W.J. Spatio-temporal Variations in PM2.5 and Its Influencing Factors in the Yangtze River Delta Urban Agglomeration. Environ. Sci. 2023, 44, 5325–5334. [Google Scholar]
  21. Diao, Y.W.; Wang, H.L.; Shen, L.J.; Yang, M.; Shi, S.S.; Lu, W. Analysis of air pollution characteristics and associated compound air polution case in Nanjing from 2015 to 2021. Res. Environ. Sci. 2023, 36, 260–272. [Google Scholar] [CrossRef]
  22. Mao, Z.C.; Xu, J.M.; Yang, D.D.; Yu, Z.Q.; Qu, Y.H.; Zhou, G.Q. Analysis of characteristics and meteorological causes of PM2.5-O3 compound pollution in Shanghai. China Environ. Sci. 2019, 39, 2730–2738. [Google Scholar]
  23. Wang, Q. Chemical characteristics and sources of volatile organic compounds in Shanghai during an ozone and particulate pollution episode in May 2019. Environ. Sci. 2020, 41, 2555–2564. [Google Scholar]
  24. Song, X.H.; Yan, L.; Liu, W.; He, J.Y.; Wang, Y.C.; Huang, T.L.; Li, Y.Y.; Chen, M.; Meng, J.J.; Hou, Z.F. Spatiotemporal Distribution Characteristics of Co-pollution of PM2.5 and Ozone over BTH with Surrounding Area from 2015 to 2021. Environ. Sci. 2023, 44, 1841–1851. [Google Scholar]
  25. Liu, Y.W.; Wang, N.; Hu, C.M. Analysis of synoptic classification of PM2.5-O3 compound pollution in Sichuan Province. Acta Sci. Circumstantiae 2025, 45, 293–302. [Google Scholar]
  26. Zhou, T.; Zhou, Q.; Zhang, Y.; Wu, Y.S.; Sun, J. Characteristics of PM2.5-O3 compound pollution and meteorological impact in Fenwei Plain. Meteorol. Mon. 2023, 49, 1359–1370. [Google Scholar]
  27. Wang, Z.S.; Zhang, D.W.; Li, Y.T.; Dong, X.; Sun, R.W.; Sun, N.D. Different Air Pollution Situations of O3 and PM2.5 During Summer in Beijing. Environ. Sci. 2016, 37, 807–815. [Google Scholar]
  28. Annalisa, D.B.; Vincenzo, M.; Mattia, P.; Giampietro, C.; Marco, C.; Rossella, F. Interaction of the Sea Breeze with the Urban Area of Rome: WRF Mesoscale and WRF Large-Eddy Simulations Compared to Ground-Based Observations. Bound-Lay Meteorol. 2022, 185, 333–363. [Google Scholar] [CrossRef]
  29. Shikhovtsev, A.Y.; Kovadlo, P.G.; Lezhenin, A.A.; Gradov, V.S.; Zaiko, P.O.; Khitrykau, M.A.; Kirichenko, K.E.; Driga, M.B.; Kiselev, A.V.; Russkikh, I.V.; et al. Simulating Atmospheric Characteristics and Daytime Astronomical Seeing Using Weather Research and Forecasting Model. Appl. Sci. 2023, 13, 6354. [Google Scholar] [CrossRef]
  30. Shikhovtsev, A.Y.; Kovadlo, P.G.; Lezhenin, A.A.; Korobov, O.A.; Kiselev, A.V.; Russkikh, I.V.; Kolobov, D.Y.; Shikhovtsev, M.Y. Influence of Atmospheric Flow Structure on Optical Turbulence Characteristics. Appl. Sci. 2023, 13, 1282. [Google Scholar] [CrossRef]
  31. Dong, H.; Cao, S.; Ge, Y. Simulation of flow over complex terrain by coupling of WRF and LES. In Proceedings of the IABSE Young Engineers Colloquium in East Asia, Shanghai, China, 24 October 2018; pp. 56–58. [Google Scholar]
  32. Jaeckels, J.M.; Bae, M.-S.; Schauer, J.J. Positive Matrix Factorization (PMF) Analysis of Molecular Marker Measurements to Quantify the Sources of Organic Aerosols. Environ. Sci. Technol. 2007, 41, 5763–5769. [Google Scholar] [CrossRef]
  33. Norris, G.; Duvall, R.; Brown, S.; Bai, S. EPA Positive Matrix Factorization (PMF)5.0 Fundamentals and User Guide; U.S. EPA: Washington, DC, USA, 2014.
  34. Brown, S.G.; Eberly, S.; Paatero, P.; Norris, G.A. Methods for estimating uncertainty in PMF solutions: Examples with ambient air and water quality data and guidance on reporting PMF results. Sci. Total Environ. 2015, 518–519, 626–635. [Google Scholar] [CrossRef] [PubMed]
  35. Huangfu, Y.Q.; Tian, Y.Z.; Dong, S.H.; Dai, Q.L.; Shi, G.L.; Zhou, X.Y.; Wei, Z.; Qian, Y.; Feng, Y.C. Evaluating the performance of PMF model for Atmospheric PM source apportionment in multi-site. China Environ. Sci. 2018, 38, 2032–2038. [Google Scholar]
  36. Yang, J.; Wei, Z.Y.; Xv, H.Y.; Wu, J. Air Quality Index Prediction Model Based on WRF-CMAQ. Oper. Res. Fuzziol. 2023, 13, 120–128. [Google Scholar] [CrossRef]
  37. Liu, X. Characteristics and Causes of PM2.5 Regional and Local Pollution Events Based on Machine Learning. Ph.D. Thesis, Lanzhou University, Lan Zhou, China, 2022. [Google Scholar]
  38. Fu, W.X.; Huang, L.; Ding, J.H.; Qin, M.M.; Yu, X.N.; Xie, F.J.; Hu, J.L. Elucidating the Impacts of Meteorology and Emission Changes on Concentrations of Major Air Pollutants in Major Cities in the Yangtze River Delta Region Using a Machine Learning De-weather Method. Environ. Sci. 2023, 44, 5879–5888. [Google Scholar] [CrossRef]
  39. Li, J.H.; Hu, Q.H.; Li, Q.H.; Ji, X.G.; Xue, J.K.; Lin, P.Z. Evaluation on impact of fireworks on PM2.5 concentration during the Spring Festival based on machine learning. Environ. Eng. 2025, 43, 90–102. [Google Scholar] [CrossRef]
  40. Zhu, J.Y.; An, J.L.; Feng, Y.Z.; He, J.; Zhang, Y.X.; Wang, J.X. Atmospheric ozone concentration prediction in Nanjing based on lightGBM. Environ. Sci. 2023, 44, 3685–3694. [Google Scholar]
  41. Hou, L.L.; Dai, Q.L.; Song, C.B.; Liu, B.W.; Guo, F.Z.; Dai, T.J.; Li, L.X.; Liu, B.S.; Bi, X.H.; Zhang, Y.F.; et al. Revealing drivers of haze pollution by explainable machine learning. Environ. Sci. Technol. Lett. 2022, 9, 112–119. [Google Scholar] [CrossRef]
  42. Xu, H.; Yu, H.F.; Xu, B.; Wang, Z.Y.; Wang, F.; Wei, Y.T.; Liang, W.Q.; Liu, J.X.; Liang, D.N.; Feng, Y.C.; et al. Machine learning coupled structure mining method visualizes the impact of multiple drivers on ambient ozone. Commun.Earth Environ. 2023, 4, 265. [Google Scholar] [CrossRef]
  43. MEE. Technical Specifications for Operation and Quality Control of Ambient Air Quality Automated Monitoring System for Particulate Matter (PM10 and PM2.5); China Environmental Science Press: Beijing, China, 2018. [Google Scholar]
  44. MEE. Ambient Air Quality Standards; China Environmental Science Press: Beijing, China, 2012. [Google Scholar]
  45. Turpin, B.; Lim, H. Species contributions to PM2.5 mass concentrations: Revisiting common assumptions for estimating organic mass. Aerosol Sci. Technol. 2001, 35, 602–610. [Google Scholar] [CrossRef]
  46. Grange, S.K.; Carslaw, D.C.; Lewis, A.C.; Boleti, E.; Hueglin, C. Random forest meteorological normalisation models for Swiss PM10 trend analysis. Atmos. Chem. Phys. 2018, 18, 6223–6239. [Google Scholar] [CrossRef]
  47. Vu, T.V.; Shi, Z.B.; Cheng, J.; Zhang, Q.; He, K.B.; Wang, S.X.; Harrison, R.M. Assessing the impact of clean air action on air quality trends in Beijing using a machine learning technique. Atmos. Chem. Phys. 2019, 19, 11303–11314. [Google Scholar] [CrossRef]
  48. Shi, Z.B.; Song, C.B.; Liu, B.W.; Lu, G.D.; Xu, J.S.; Vu, T.V.; Elliott, R.J.R.; Li, W.J.; Bloss, W.J.; Harrison, R.M. Abrupt but smaller than expected changes in surface air quality attributable to COVD-19 lockdowns. Sci. Adv. 2021, 7, eabd6696. [Google Scholar] [CrossRef] [PubMed]
  49. Xia, X.S.; Chen, J.J.; Wang, J.J.; Cheng, X.F. PM2.5 concentration influencing factors in China based on the random forest model. Environ. Sci. 2020, 41, 2057–2065. [Google Scholar]
  50. Paatero, P.; Eberly, S.; Brown, S.G.; Norris, G.A. Methods for estimating uncertainty in factor analytic solutions. Atmos. Meas. Tech. 2014, 7, 781–797. [Google Scholar] [CrossRef]
  51. Lu, H.; Xie, M.; Liu, X.R.; Liu, B.J.; Jiang, M.Z.; Gao, Y.H.; Zhao, X.L. Adjusting prediction of ozone concentration based on CMAQ model and machine learning methods in Sichuan-Chongqing region, China. Atmos. Pollut. Res. 2021, 12, 101066. [Google Scholar] [CrossRef]
  52. Huang, L.; Zhu, Y.H.; Zhai, H.H.; Xue, S.H.; Zhu, T.Y.; Shao, Y.; Liu, Z.Y.; Emery, C.; Yarwood, G.; Wang, Y.J.; et al. Recommendations on benchmarks for numerical air quality model applications in China—Part 1: PM2.5 and chemical species. Atmos. Chem. Phys. 2021, 21, 2725–2743. [Google Scholar] [CrossRef]
  53. Seinfeld, J.H.; Pandis, S.N.; Noone, K. Atmospheric Chemistry and Physics: From Air Pollution to Climate Change. Phys. Today 1998, 50, 88–90. [Google Scholar] [CrossRef]
Atmosphere 16 00867 g001
Figure 1. Observation site and surrounding environment in this study.
Figure 1. Observation site and surrounding environment in this study.
Atmosphere 16 00867 g001
Atmosphere 16 00867 g002
Figure 2. Model domain setup.
Figure 2. Model domain setup.
Atmosphere 16 00867 g002
Atmosphere 16 00867 g003
Figure 3. PM2.5 concentration in Suzhou and surrounding cities in June.
Figure 3. PM2.5 concentration in Suzhou and surrounding cities in June.
Atmosphere 16 00867 g003
Atmosphere 16 00867 g004
Figure 4. Monthly changes in PM2.5 concentration and year-on-year changes in June in Suzhou over the past six years.
Figure 4. Monthly changes in PM2.5 concentration and year-on-year changes in June in Suzhou over the past six years.
Atmosphere 16 00867 g004
Atmosphere 16 00867 g005
Figure 5. The hourly change in chemical composition at different stations in Suzhou.
Figure 5. The hourly change in chemical composition at different stations in Suzhou.
Atmosphere 16 00867 g005
Atmosphere 16 00867 g006
Figure 6. Hourly contribution of PM2.5 in Suzhou from 13 to 23 June 2023.
Figure 6. Hourly contribution of PM2.5 in Suzhou from 13 to 23 June 2023.
Atmosphere 16 00867 g006
Atmosphere 16 00867 g007
Figure 7. Hourly contribution of PM2.5 in Suzhou during the key period of the pollution process.
Figure 7. Hourly contribution of PM2.5 in Suzhou during the key period of the pollution process.
Atmosphere 16 00867 g007
Atmosphere 16 00867 g008
Figure 8. Source analysis of PM2.5 in Suzhou from 13 to 23 June 2023.
Figure 8. Source analysis of PM2.5 in Suzhou from 13 to 23 June 2023.
Atmosphere 16 00867 g008
Atmosphere 16 00867 g009
Figure 9. (a) The electricity consumption of the whole society from January 2022 to June 2023 (data source: Suzhou Municipal Bureau of Statistics). (b) Monthly emission of pollutants in Suzhou’s key industrial enterprises from January 2022 to June 2023.
Figure 9. (a) The electricity consumption of the whole society from January 2022 to June 2023 (data source: Suzhou Municipal Bureau of Statistics). (b) Monthly emission of pollutants in Suzhou’s key industrial enterprises from January 2022 to June 2023.
Atmosphere 16 00867 g009
Table 1. WRF physical process and parameterization scheme.
Table 1. WRF physical process and parameterization scheme.
Physical ProcessParameterization Scheme
Microphysical processMorrison scheme
Cumulus convective schemeKain–Fritsch scheme
Land surface process schemeNoah scheme
Planetary boundary layer schemeYonsei University (YSU) scheme
Long-wave radiationRapid Radiative Transfer Model (RRTM) long-wave radiation scheme
Short-wave radiationRRTM short-wave radiation scheme
Table 2. Verification statistical indicators of measured and calculated meteorological quantities.
Table 2. Verification statistical indicators of measured and calculated meteorological quantities.
PeriodMeteorological ParameterNMBIOARRMSE
13 to 23 June 2023Temperature4.7%0.840.842.7
Wind speed23.0%0.600.372.0
humidity−18.3%0.780.7919.6
Table 3. Meteorological conditions in Suzhou in June 2022–2023.
Table 3. Meteorological conditions in Suzhou in June 2022–2023.
DateWDT (℃)RH (%)WS (m/s)P (hpa)Precipitation (mm)
2022.6SW27.2742.91003208.3
2023.6E25.9762.41005240.6
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Wu, M.; Cai, N.; Fang, J.; Huang, L.; Shi, X.; Wu, Y.; Li, L.; Qin, H. Local Emissions Drive Summer PM2.5 Pollution Under Adverse Meteorological Conditions: A Quantitative Case Study in Suzhou, Yangtze River Delta. Atmosphere 2025, 16, 867. https://doi.org/10.3390/atmos16070867

AMA Style

Wu M, Cai N, Fang J, Huang L, Shi X, Wu Y, Li L, Qin H. Local Emissions Drive Summer PM2.5 Pollution Under Adverse Meteorological Conditions: A Quantitative Case Study in Suzhou, Yangtze River Delta. Atmosphere. 2025; 16(7):867. https://doi.org/10.3390/atmos16070867

Chicago/Turabian Style

Wu, Minyan, Ningning Cai, Jiong Fang, Ling Huang, Xurong Shi, Yezheng Wu, Li Li, and Hongbing Qin. 2025. "Local Emissions Drive Summer PM2.5 Pollution Under Adverse Meteorological Conditions: A Quantitative Case Study in Suzhou, Yangtze River Delta" Atmosphere 16, no. 7: 867. https://doi.org/10.3390/atmos16070867

APA Style

Wu, M., Cai, N., Fang, J., Huang, L., Shi, X., Wu, Y., Li, L., & Qin, H. (2025). Local Emissions Drive Summer PM2.5 Pollution Under Adverse Meteorological Conditions: A Quantitative Case Study in Suzhou, Yangtze River Delta. Atmosphere, 16(7), 867. https://doi.org/10.3390/atmos16070867

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop