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Article

Scattering Characteristics of Submicron Particulate Chemical Components During Winter in Northern and Southern Chinese Cities

by
Jialin Shi
1,
Mingzhe Li
1,
Qinghong Wang
1,
Wenfei Zhu
1,2,3,*,
Liping Qiao
4,
Shengrong Lou
1,4 and
Song Guo
5
1
School of Energy and Power Engineering, University of Shanghai for Science and Technology, Shanghai 200094, China
2
Institute of Climate and Energy Research, ICE-3: Troposphere, Forschungszentrum, 52425 Jülich, Germany
3
Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention (LAP3), Shanghai 200438, China
4
State Environmental Protection Key Laboratory of Formation of Urban Air Pollution Complex, Shanghai Academy of Environmental Sciences, Shanghai 200233, China
5
College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China
*
Author to whom correspondence should be addressed.
Atmosphere 2025, 16(11), 1302; https://doi.org/10.3390/atmos16111302
Submission received: 7 October 2025 / Revised: 7 November 2025 / Accepted: 12 November 2025 / Published: 18 November 2025
(This article belongs to the Section Aerosols)
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Abstract

Understanding aerosol chemical components’ roles in light extinction is critical for air quality management and climate mitigation. This study compared PM1 optical properties and chemical compositions in Shanghai (southern China) and Dezhou (northern China) during winter using high-resolution aerosol mass spectrometers and optical instruments. Results showed PM1 scattering coefficients (10.9–549.8 Mm−1) in Shanghai were dominated by traffic-related organic aerosols (OA) (45.2%), with ammonium sulfate and nitrate contributing 60.5% of extinction. In Dezhou, higher scattering coefficients (3.5–2635.1 Mm−1) were driven by heating/biomass burning, with OA accounting for 57.8% and ammonium nitrate 27.2%. Mass scattering efficiencies (MSEs) in Dezhou were significantly higher (sulfate: 10.75 m2/g; nitrate: 10.15 m2/g; OA: 4.9 m2/g) than those in Shanghai (4.2/3.85/3.00 m2/g). Pollution episodes revealed distinct mechanisms: high-humidity OA accumulation for Shanghai vs. nitrate-organic synergy for Dezhou. The IMPROVE model systematically underestimated scattering coefficients, emphasizing the need for region-specific parameterization. OA was identified as the primary scattering contributor in both cities, though inorganic species became critical under high-pollution conditions. These findings suggest targeted strategies: reducing VOC emissions in southern China and controlling NOx in northern industrial areas to improve winter visibility and air quality.

1. Introduction

In recent years, China has frequently experienced haze episodes, characterized by the significant reduction in horizontal visibility due to the scattering and absorption of light by suspended aerosol particles, playing a key role in haze formation [1,2]. Aerosols, composed of complex assemblages of solid or liquid particles, exert significant impacts on climate change and atmospheric optical properties. In particular, high concentrations of fine particulate matter (e.g., PM2.5) can greatly enhance both light scattering and absorption. This phenomenon not only contributes to haze formation and reduced visibility but also presents a considerable risk to human health. Particulate extinction accounts for 75–95% of total atmospheric light extinction in urban areas [3,4,5], with submicron particles (PM1) providing especially significant contributions [6,7]. Particle scattering is the predominant factor contributing to atmospheric light extinction, accounting for over 80% of the total effect, especially under polluted conditions [8]. The intensity increases with particle concentrations under high-humidity conditions, significantly affecting visibility and air quality [9,10]. An in-depth analysis of submicron particle extinction characteristics is essential for accurately understanding their impact on visibility and providing a scientific basis for developing effective strategies to improve visibility.
While PM1 scattering often increases with particle mass, mass alone does not guarantee stronger scattering. The magnitude is strongly governed by the size distribution and chemical composition, which together determine the complex refractive index, density, and mixing state [11]. Changes in their chemical composition can alter both optical and hygroscopic properties, consequently influencing visibility. Investigated aerosol scattering and the contributions of chemical composition in the northern suburbs of Nanjing during the wintertime. They found that particulate mass concentration and aerosol scattering coefficient both increased with pollution levels, showing a strong correlation (correlation coefficient (r) = 0.93) [12]. The study used the IMPROVE equation to link particulate species with scattering coefficients, finding that ammonium nitrate, ammonium sulfate, and organic aerosols (OA) are the main contributors to pollution in Nanjing’s northern suburbs in winter. Typically, ammonium sulfate and OA are the leading contributors to aerosol extinction, but in some areas, ammonium nitrate can dominate [13]. In typical environmental conditions in Beijing, ammonium sulfate and ammonium nitrate account for approximately 55% of the scattering coefficient during heavy pollution episodes, significantly more than the 29% contribution from OA [14]. Meanwhile, ref. [6] observed that in Beijing, ammonium nitrate has a greater impact than ammonium sulfate during non-heating seasons, while ammonium sulfate assumes a more prominent role during the heating seasons [15]. Similarly, for particles with diameters ranging from 0.56 to 1.0 µm, nitrate and sulfate were the most significant contributors to PM (19.4–39.7% and 18.1–34.7%, respectively). Rising nitrate and sulfate concentrations significantly drive pollution events [16]. In northern areas, especially during the heating period in winter, sulfate and nitrate concentrations are typically high, making substantial contributions to scattering coefficients. In the south, biomass burning and industrial emissions increase OA and nitrate in winter, impacting scattering [17,18]. However, urban studies in Mexico City found that the larger scattering contribution of organic aerosols during the dry-warm season (73% in total scattering) from February to May than that in the dry cool season, similar to winter (63%). Coincidentally, they also attributed the increase in OA scattering contribution to the arrival of large quantities of aged particles carried by biomass combustion plumes, which occur more frequently during warm seasons [19,20]. Thus, analyzing submicron particle composition and their role in extinction is crucial.
Currently, three main methods are used to calculate the contributions of aerosol components to extinction: the Interagency Monitoring of Protected Visual Environments (IMPROVE) formulas, Mie theory, and multiple linear regression analysis [21]. The IMPROVE equation is widely utilized in research concerning particulate matter extinction, serving as an effective instrument for estimating the extinction properties of particulate matter across various environmental conditions [22,23,24,25]. Pronounced differences are exhibited in chemical species concentrations and particle-size distributions between China and Western countries. The regression between extinction coefficients and fine particulate matter in China shows a clear intercept, indicating that reconstructed extinction coefficients are significantly underestimated in high-concentration conditions. In North China, scattering coefficients are underestimated by 60% and 38% in Shijiazhuang and Xinglong, respectively [13]. In East China, the underestimation ranges from 16% to 30% in Nanjing and reaches 36% in Shanghai. The empirical formulas cannot be directly employed to estimate species-specific extinction contributions in Chinese urban areas. Mie theory provides highly accurate calculations of particulate contributions to extinction but is complex and requires difficult-to-obtain particle-size distribution data [26]. In contrast, multiple linear regression models link extinction coefficients to specific aerosol parameters, similar to the localized parameterization of the IMPROVE equation, offering more targeted and practical results by reflecting local conditions [27,28].
In this study, two representative cities, Shanghai in the southern region and Dezhou in the northern region, were selected for investigation. Field data were collected utilizing aerosol monitoring instruments with high temporal resolution. A comprehensive analysis was performed to investigate the variations in scattering coefficients during the winter heating season in both cities. A multiple linear regression analysis was conducted to quantitatively elucidate the relationship between the chemical composition of atmospheric fine particulate matter and the scattering coefficient. This analysis concentrated on the variations in the contributions of individual components as pollution levels changed, providing valuable insights for the development of real-time pollution prevention and control strategies.

2. Materials and Methods

2.1. Field Observations

The intensive field measurements were conducted to examine the variations in scattering coefficients in the urban areas of Shanghai and Dezhou and assess the influence of local submicron particles. Data were collected at the Shanghai Academy of Environmental Science observation station (SAES) from 28 November 2016 to 13 January 2017, and at the Shandong Dezhou Station from 6 November 2017 to 23 January 2018. The Shanghai observation station (31.10° N, 121.25° E) is situated in Xuhui District, with sampling instruments housed in an air-conditioned room on the top floor of a research building, approximately 32 m above ground level. The location is representative of typical urban environmental conditions with no large-scale industrial or local emission sources nearby (Figure S1). The Dezhou station (37°8′ N, 116°27′ E) is located in the courtyard of the Pingyuan County Meteorological Bureau in Shandong Province. The surrounding area is mainly farmland with stable land use and no tall structures, representing a typical regional setting in North China (Figure S2).

2.2. Data Measurements

A high-resolution time-of-flight aerosol mass spectrometer (HR-ToF-AMS, Aerodyne Research Inc., Billerica, MA, USA) was used to analyze non-refractory components in PM1, specifically OA, sulfate, nitrate, ammonium, and chloride. HR-ToF-AMS consists of four main sections: an inlet region, a particle-size measurement region, an evaporation/ionization region, and an ion detection region. Atmospheric fine particles were passed through an orifice and an aerodynamic lens in the inlet region, and then they were focused into a beam and accelerated. The particles were subsequently sorted by size along fixed-length particle measurement paths and introduced into a vacuum detection chamber (10−5–10−7 torr), where non-refractory constituents were rapidly thermally vaporized at 600 °C and ionized. Finally, the ionized particles were directed to a multichannel plate (MCP) detector for measurement. Detailed descriptions of the structure, performance, and operational principles of AMS can be found in previous research [29]. Flow calibration, size calibration, and ionization-efficiency calibration were conducted to ensure accurate and reliable AMS measurements. Detailed information can be found in Section S1 of the supplement.
A photoacoustic extinctionmeter (PAX, Droplet Measurement Technologies, Longmont, CO, USA) with a 525 nm wavelength was used for the real-time measurement of extinction coefficients and scattering coefficients of particulate matter in Shanghai. The instrument operated with an internal pump, sampling at a flow rate of 1 L/min. A cyclone separator and bypass flow system were employed to ensure that only particles with diameters below 1 μm entered the PAX. Additionally, a diffusion dryer was installed upstream of the PAX inlet to maintain the relative humidity of the sample air below 30–40%, with regular replacement of the drying device to ensure effective dehumidification. During the Dezhou observation period, an Aurora-1000 Integrating Nephelometer (Ecotech Pty Ltd., Melbourne, Australia) was used to measure the scattering coefficient of particles at a scattering angle ranging from 10° to 170° and a wavelength of 525 nm, with a particle cutoff size of 1 μm and a dryer for keeping the inlet airflow dry [30]. The nephelometer underwent a zero check every 24 h, and span calibration was performed biweekly using R134a gas, along with corrections for truncation errors and other angular effects. During the Shanghai observation period, the scattering coefficients measured by PAX showed excellent agreement with those from the simultaneously operated optical instrument CAPS (Figure S3), demonstrating the reliability of the scattering coefficient measurements. Due to data limitations, scattering coefficient cross-validation was available only for Shanghai, while this verification could not be performed for Dezhou.
For both sites, the sampling inlet configuration for the scattering measurements and AMS remained a common dryer, cyclone separator, and uniform flow control, ensuring that optical data correspond to chemical mass measurements. The measured scattering coefficients for Shanghai and Dezhou represent dry scattering coefficients. The regression coefficients in Equation (2) represent dry mass scattering efficiencies (MSEs) without hygroscopic growth f(RH) effects, enabling regional comparison under controlled humidity conditions.

2.3. Data Analysis

Mass concentrations, size distributions, and related information for each chemical species, including OA, sulfate (SO42−), nitrate (NO3−), ammonium (NH4+), and chloride (Cl−) were analyzed using the standard Igor-based toolkits SQUIRREL (ver 1.57) and PIKA (ver 1.16I), developed by the Jimenez group at the University of Colorado (https://cires1.colorado.edu/jimenez-group/ToFAMSResources/ToFSoftware/, accessed on 14 November 2025). IE and Relative Ionization Efficiency (RIE) for two observations were adopted values in Tables S1 and S3. For particle bounce-off, 0.5 was adopted as the empirical collection efficiency (CE). Measurement uncertainties were evaluated by comparing the total AMS mass (OA, sulfate, nitrate, ammonium, and chloride) plus BC with the observed PM2.5 mass (Figure S4), showing a high correlation coefficient (R > 0.9), which confirms the reliability of the AMS data.
Multiple linear regression analysis was performed on atmospheric scattering and the major chemical components of particulate matter. Ion balance analysis based on calculated and measured ammonium concentrations indicated that sulfate and nitrate predominantly existed as ammonium salts in the form of ammonium sulfate (NH4)2SO4 and ammonium nitrate NH4NO3 (Figure S5). (NH4)2SO4, NH4NO3, and OA dominated the PM1 mass at both sites during the campaigns (Figure S6), while scattered species, such as ammonium chloride and metal elements, are present at very low concentrations and can be considered negligible. Accordingly, the regression analysis of MSE focused on the three dominant components under dried sampling conditions. The mass concentrations of (NH4)2SO4, and NH4NO3 were calculated as follows:
[(NH4)2SO4] = 1.375 [SO42−]
[NH4NO3] = 1.29 [NO3−]

3. Results and Discussion

3.1. Variation Characteristics of the Scattering Coefficient

Significant differences in emission sources between northern and southern China lead to distinct impacts on atmospheric aerosol composition and scattering properties during winter. The time series and diurnal variations in the scattering coefficient of submicron particles measured in the Shanghai observation station are shown in Figure 1. The scattering coefficient exhibited a sawtooth-like pattern, ranging from 10.9 Mm−1 to 549.8 Mm−1, with an average value of 120.2 ± 92.3 Mm−1. The diurnal variation in the scattering coefficient exhibited a single-peak pattern, reaching its maximum in the morning between 9:00 and 10:00. This peak is likely influenced by the temperature inversion layer and morning rush-hour emissions, as pollutants accumulate overnight and reach their highest levels in the morning. During the morning rush hour, high traffic volume leads to intensified vehicle emissions.
The scattering coefficient during the Dezhou observations exhibited a sawtooth-like pattern, ranging from 3.5 Mm−1 to 2635.1 Mm−1, with an average value of 420.3 ± 399.3 Mm−1. The observed patterns and data exhibited significant fluctuations and periodic high values, highlighting the complexity of pollution emissions in Dezhou during the heating season. The diurnal variation in the scattering coefficient exhibited distinct morning and evening peaks, with higher values in the early morning and late evening and lower values during the daytime. This pattern reflects the combined influence of traffic emissions, heating activities, and variations in the atmospheric mixing layer. Pollutants tend to accumulate in the early morning and evening while becoming diluted and dispersed during the daytime.
The scattering coefficient in Dezhou was significantly higher than that in Shanghai, with Dezhou experiencing prolonged periods of elevated values. Moreover, the diurnal variations in the scattering coefficient differed significantly between the two cities. In Shanghai, the scattering coefficient remained relatively stable with minor fluctuations, whereas in Dezhou, it showed a pronounced increase in the evening (16:00–20:00), with a peak around 20:00.

3.2. Variation Characteristics of Submicron Particulate Matter Composition

During wintertime in Shanghai, the concentration of PM1 ranged from 3 to 145.3 μg/m3, with an average of 33.6 ± 23.3 μg/m3. Figure 2 presents the time series of the concentrations of major chemical components observed in Shanghai. The concentrations of OA, ammonium nitrate, and ammonium sulfate were ranged at 1.1–107.6 μg/m3, 0.3–51.6 μg/m3, and 1.3–25.1 μg/m3, respectively. OA was the dominant component, accounting for 45.2% to total mass, while ammonium nitrate and ammonium sulfate accounted for approximately 27.4% each. During the wintertime in Dezhou, the concentration of PM1 ranged from 2.8 to 424.9 μg/m3, with an average of 58.8 ± 47.8 μg/m3. The concentration ranges of OA, ammonium nitrate, and ammonium sulfate were 1.0–358.1 μg/m3, 0.2–64.1 μg/m3, and 0.6–116.5 μg/m3, respectively (Figure 2). OA accounted for 57.8% of PM1, followed by ammonium nitrate and ammonium sulfate at 27.2% and 15.0%, respectively. The concentrations of PM1 and its major chemical components were significantly higher in Dezhou than in Shanghai. OA was the primary contributor, with its proportion in Dezhou exceeding that in Shanghai by 12.6%.
Figure S6 illustrates the diurnal variation characteristics of different chemical components during the winter observation period in Dezhou and Shanghai. OA exhibited a bimodal distribution in Dezhou and Shanghai. Distinct peaks were observed in the morning (8:00–9:00) and at night (19:00–20:00) in both locations. The nighttime peak is associated with coal combustion and vehicle emissions, which are likely major contributors to the increase in OA concentrations [31]. This may also explain the sharp rise in scattering coefficients observed in Dezhou during the evening. While in Shanghai, the peak concentrations of OA are primarily contributed to by vehicle and cooking emissions [32].
In both Shanghai and Dezhou, ammonium nitrate concentrations decreased during the daytime and increased in the evening. During the daytime, ammonium nitrate concentrations continued to decline, gradually increasing after 17:00. The difference between the two cities is that ammonium nitrate concentration peaked around 8:00 in the morning before gradually decreasing in Shanghai, whereas in Dezhou, it exhibited a slow decline until 17:00. Gas-particle partitioning and photochemical oxidation are the two primary factors influencing daytime nitrate concentrations. During the daytime, elevated temperatures enhance the transition of particulate nitrate to the gas phase, leading to a reduction in its concentration. Meanwhile, low nighttime temperatures and high humidity during the observation period created favorable conditions for liquid-phase reactions, thereby promoting the formation and accumulation of ammonium nitrate.
The diurnal variations in ammonium sulfate were relatively subtle in both locations, with a similar overall trend. Compared to the other two species, its concentration remained relatively stable with less fluctuation. During the morning rush hour in both locations, ammonium sulfate concentrations exhibited a brief peak, followed by a gradual decline and a slow increase in the evening.

3.3. Relationship Between Scattering Coefficient and Major Chemical Components

In Shanghai, the diurnal fluctuations in the scattering coefficient closely mirrored the patterns observed in the concentrations of ammonium sulfate and ammonium nitrate, exhibiting a single-peak pattern with a maximum occurring around 9:00 in the morning, as illustrated in Figure 1 and Figure S7. This pattern is likely influenced by the cold, humid, and stable meteorological conditions in winter, which facilitate the liquid-phase transformation of secondary inorganic components. During the winter observation period in Shanghai, the combined contribution of ammonium sulfate and ammonium nitrate to the scattering coefficient was greater than that of OA. In Dezhou, the diurnal variations in the scattering coefficient closely followed the trends of concentrations for major submicron particulate components, showing a bimodal pattern with a gradual decline during the day and a steady increase around 17:00 in the evening. At this time, both the scattering coefficient and OA concentration spiked, indicating OA as the main contributor to the scattering coefficient during winter in Dezhou.
Understanding the scattering contributions of chemical components is crucial for grasping atmospheric optical properties and aerosol effects during the winter. The mass scattering efficiencies (MSEs) for local scattering coefficients in Dezhou and Shanghai remain unquantified, making it difficult to distinguish the contributions of different components. The multiple linear regression model was employed to develop a localized framework for scattering coefficients (bsp) and to investigate the regression relationship between scattering coefficients and ammonium sulfate, ammonium nitrate, and OA. We utilized the chemical composition and scattering coefficient dataset with a 10 min time resolution collected from both sites during the observation period. The least squares method was applied to fit data from the two observation stations while minimizing the goodness-of-fit error. Prior to multiple regression analysis, the dataset was preprocessed to eliminate the influence of variable scale differences on regression coefficient estimation. Additionally, outliers that significantly deviated from the mean were removed to enhance model robustness and minimize the interference of extreme values on the fitting results. The general form of the fitting equation is expressed as:
bsp = k1 × [Component 1] + k2 × [Component 2] +…+ Constant
A previous study has thoroughly discussed the assumptions of this regression relationship [33]. Following this framework, the regression equation was formulated as:
bsp = a × [(NH4)2SO4] + b × [NH4NO3] + c × [OA]+ Constant
where a, b, and c represent the MSE of ammonium sulfate, ammonium nitrate, and OA, respectively.
It was worth noting that the coefficients (a, b, c) in Equation (2) are effective dry MSEs under the site, season, and source conditions. In addition to chemical compositions, they are sensitive to dry size distribution, mixing state, density, and complex refractive index. While PM1 cut and dried inlet minimized humidity and size-limit effects, inter-city differences in dry size distribution and mixing state can still yield different coefficients. Thus, regional MSE comparisons should be conditioned on dryness and a common size cut, and coefficients should not be interpreted as composition-only constants.
To further analyze the contributions of major chemical components to scattering under different pollution conditions, a threshold of 35 μg/m3 was used to distinguish between clean and polluted periods. Multiple linear regression analysis was then conducted using concentration data of these components from both periods.
MSE heat maps were created after conducting multiple linear regression analysis (see Figures S8–S11). These MSE data were used to develop localized equations for the scattering coefficient in Shanghai, as presented in Equations (3) and (4) as follows. Given inter-city differences, we adopted a stratified regression framework with four independent models (Dezhou—polluted/clean; Shanghai—polluted/clean). All results and conclusions were drawn from these stratified fits under dried sampling.
bsp,clean = 4.1 × [(NH4)2SO4] + 4.6 × [NH4NO3] + 2.8 × [OA] + 0.49
with a coefficient of determination R2 = 0.83 (n = 4161).
bsp,polluted = 4.3 × [(NH4)2SO4] + 3.1 × [NH4NO3] + 3.2 × [OA] + 29.6
with a coefficient of determination R2 = 0.43 (n = 2303).
Similarly, the localized equations for the scattering coefficient in Dezhou are given in Equations (5) and (6):
bsp,clean = 9.8 × [(NH4)2SO4] + 9.5 × [NH4NO3] + 4.1 × [OA]+ 0.51
with a coefficient of determination R2 = 0.59 (n = 2187).
bsp,polluted = 11.7 × [(NH4)2SO4] + 10.8 × [NH4NO3] + 5.7 × [OA] + 1.8
with a coefficient of determination R2 = 0.83 (n = 7536).
To minimize uncertainties in the derived MSEs, we harmonized the PM1 inlet with a diffusion dryer at both sites (inlet RH < 30–40%) and constrained optical biases by cross-validating the Shanghai PAX with co-located CAPS while performing routine zero/span checks and angular corrections on the Dezhou Aurora-1000. Based on AMS calibration, internal consistency via mass closure (AMS five species + BC vs. PM2.5) and ion balance (sulfate and nitrate as (NH4)2SO4 and NH4NO3) were verified, respectively. In addition, we standardized pre-dictors, screened outliers, and used stratified city- and regime-specific regressions rather than a pooled model to limit cross-regime confounding, thereby reducing measurement and modeling errors and yielding robust dry MSE estimates with minimized uncertainty. On this basis, the MSE fitting values for each chemical component were determined for Shanghai and Dezhou. In Shanghai, the average MSE values for ammonium sulfate, ammonium nitrate, and OA were 4.2 m2/g, 3.85 m2/g, and 3.00 m2/g, respectively. While in Dezhou, the corresponding values were 10.75 m2/g, 10.15 m2/g, and 4.9 m2/g.
Figure 3 presents the MSEs of particulate matter obtained using different methods across various locations. Detailed data are provided in Table S4. The MSEs estimated for ammonium sulfate, ammonium nitrate, and OA exceeded those calculated using the old version of the IMPROVE algorithm by 40%, 28.3%, and −25.0% in Shanghai and 258%, 238%, and 22.5% in Dezhou, respectively. This indicates that applying the old IMPROVE algorithm to calculate the scattering coefficient of submicron aerosols in Shanghai and Dezhou would lead to significant underestimation or overestimation of the results. Dry size distribution and mixing state from those algorithm assumptions can cause systematic biases even for f(RH) ≈ 1. Our results show consistent yet regionally varying underestimation by the IMPROVE formulation, supporting the need for region-specific parameterization.
MSE fitting values for chemical species in Dezhou differ significantly from those in other regions, likely due to the high concentration of chemical species during the winter season in Dezhou. Dezhou exhibited the highest mass scattering efficiency (MSE) values for ammonium nitrate and ammonium sulfate, reaching 10.15 and 10.75 m2/g, respectively. These values were substantially higher than those observed in other regions, particularly Shanghai in this study, with increases of 164% and 156%, respectively.
OA showed the highest MSE in Chengdu (7.42 m2/g), which was 147% higher than the value in Shanghai and 51% higher than that in Dezhou. In contrast, both France and Arizona showed considerably lower MSE values overall. Compared with Dezhou, MSE for OA in France and Arizona decreased by 55% and 37%, respectively; ammonium nitrate MSE decreased by 64% and 75%; and ammonium sulfate MSE decreased by 47% and 75%. Significant variations were also observed within the same city under different methods or particle size classifications. For example, the Mie theory-based estimate for Shanghai, as reported in the literature, yielded an MSE for OA that was 77% higher than that in this study. In Guangzhou, two MLR-based estimates for PM2.5 showed differences of 10–20% in MSE values for OA, ammonium nitrate, and ammonium sulfate, indicating that both the choice of estimation method and particle size fraction have a substantial impact on MSE determination.

3.4. Variations in the Scattering Coefficient of Each Component in Pollution Episode

Using the multiple linear regression fitting equations (Equations (3)–(6)), the average scattering contributions of ammonium sulfate, ammonium nitrate, and OA during the observation period in Shanghai and Dezhou were determined, as shown in Figure 4.
During the winter observation period in Shanghai, the atmospheric scattering coefficient exhibited a sawtooth-like pattern. The scattering coefficient of ammonium nitrate ranged from 1.3 to 174.7 Mm−1, with an average of 39.2 ± 36.1 Mm−1, accounting for 30.0% of the total scattering coefficient. The scattering coefficient of ammonium sulfate ranged from 5.4 to 105.5 Mm−1, averaging 30.7 ± 18.8 Mm−1, and contributing 31.7% to the total scattering coefficient. The scattering coefficient of OA ranged from 3.1 to 301.2 Mm−1, with an average of 40.0 ± 31.6 Mm−1, accounting for 37.3% of the total scattering coefficient. OA and ammonium sulfate were the primary contributors to the scattering coefficient during the Shanghai observation period. Furthermore, the combined contribution of ammonium sulfate and ammonium nitrate to the scattering coefficient was 61.7%, significantly exceeding that of OA, indicating that the scattering coefficient was influenced by secondary formation in winter in Shanghai.
During the winter observation period in Dezhou, the atmospheric scattering coefficient exhibited a sawtooth-like pattern with significantly greater fluctuations compared to the Shanghai observation period. The scattering coefficient of ammonium nitrate ranged from 2.0 to 692.3 Mm−1, with an average of 153.4 ± 139.8 Mm−1, accounting for 36.5% of the total scattering coefficient. The scattering coefficient of OA ranged from 4.7 to 2041.0 Mm−1, with an average of 170.00 ± 170.3 Mm−1, accounting for 42.1% of the total scattering coefficient. The average scattering coefficient of ammonium sulfate was 94.1 ± 115.5 Mm−1. This indicates that OA was the primary contributor to the scattering coefficient during the Dezhou observation period.
In both Dezhou and Shanghai, the scattering coefficients of each chemical species exhibited distinct diurnal variation patterns, as shown in Figure 5. In both locations, OA exhibited a multi-peak distribution, with peak concentrations occurring around 9:00–10:00 AM and 19:00–20:00 PM. In Dezhou, OA concentrations showed a significant increase in the evening, primarily due to emissions from biomass and coal combustion during the heating season. The widespread use of solid fuels for heating in northern regions during the heating season contributed to the rapid accumulation and increase in organic matter concentrations [34,35]. In Dezhou, the scattering coefficient of ammonium nitrate peaked during the early morning and dawn hours, gradually declined in the afternoon, and showed a slight increase in the evening. This pattern reflects the combined influence of heating emissions and atmospheric stability on the scattering coefficient.
In Shanghai, the scattering coefficient of ammonium nitrate exhibited a multi-peak pattern, with a distinct peak around 8:00 AM influenced by vehicle emissions during the morning rush hour. As the atmospheric layer rose, the scattering coefficient gradually decreased. In Dezhou, the scattering coefficient gradually increased in the evening, likely influenced by the evening rush hour and heating emissions. In Dezhou, the scattering coefficient of ammonium sulfate showed a slight increase from early morning to noon, gradually declined in the afternoon, and rose again in the evening. Overall, it exhibited significant fluctuations, with a pronounced scattering effect. In contrast, Shanghai exhibited lower scattering levels, with a slight increase in the morning, followed by a stable trend with minimal fluctuations.
As the total scattering coefficient increased, the relative contributions of major chemical components to scattering varied (Figure 6). At low scattering coefficient conditions (<400 Mm−1), ammonium nitrate was the dominant contributor to particulate scattering in both regions. In Dezhou, as the particulate scattering coefficient increased, the contributions of OA and ammonium sulfate gradually increased, while the contribution of ammonium nitrate decreased [36]. When the scattering coefficient exceeded 2200 Mm−1, OA became the primary contributor to the extinction coefficient in Dezhou.
The classification of pollution and clean periods during the observation period in Shanghai and Dezhou is provided in Table S5. This study captured a prolonged heavy pollution episode from December 4 to December 9 in Shanghai (Figure 7). On December 6, strong daytime winds temporarily improved air quality, but by evening, particulate levels surged to severe pollution due to stable weather conditions that allowed rapid accumulation. During this period, the average scattering coefficient was 157.6 ± 83.0 Mm−1, with a maximum value of 337.1 Mm−1. OA contributed the most to the scattering coefficient at about 39.5%, with ammonium sulfate and ammonium nitrate contributing 25.1% and 35.4%, respectively. At the peak of the pollution episode, OA concentrations accumulated under high humidity and stable weather conditions, promoting the formation of secondary organic aerosols and making it the dominant contributor to overall scattering. During clean periods, dry air masses increased the proportion of OA and ammonium sulfate in PM1. Conversely, during polluted periods, high humidity and stable conditions increased ammonium nitrate and ammonium sulfate levels due to enhanced liquid-phase transformation of secondary inorganic components.
A typical heavy pollution episode was observed in Dezhou from 14 January to 22 January. During this period, the average scattering coefficient was 820.5 ± 510.5 Mm−1, with a maximum value reaching 2635.1 Mm−1. Ammonium nitrate and OA remained the primary contributors to the scattering coefficient during the pollution period, accounting for 39.1% and 34.4%, respectively. The results suggest that coal combustion and other stationary emission sources were the dominant contributors to pollution in Dezhou during the heating period, resulting in substantial amounts of nitrogen oxide and organic pollutant emissions. The intensity and composition of these emissions remained relatively stable and were less affected by short-term external fluctuations. During the heating period in northern regions (Dezhou), the atmosphere is typically characterized by low temperatures, low humidity, and stable conditions, which inhibit atmospheric motion and hinder the horizontal dispersion of pollutants. As a result, particulate matter accumulates in the near-surface atmosphere and becomes difficult to disperse, leading to relatively small fluctuations in the scattering contributions of the three major chemical species [37,38]. In winter pollution episodes, OA was the main contributor to the scattering coefficient of submicron particulate matter in Dezhou and Shanghai. Thus, reducing organic pollutant emissions is essential to prevent significant visibility deterioration in winter.

4. Conclusions and Implications

In this study, the scattering characteristics and chemical determinants of submicron aerosols were systematically investigated in Shanghai and Dezhou during the winter season. The scattering coefficient exhibited distinct regional variations, with Shanghai displaying a sawtooth-like fluctuation ranging from 10.9 to 549.8 Mm−1 (averaged at 120.2 ± 92.3 Mm−1), while Dezhou presented significantly higher values ranging from 3.5 to 2635.1 Mm−1 (averaged at 420.3 ± 399.3 Mm−1). Both cities exhibited characteristic diurnal patterns, with Shanghai experiencing a peak between 9:00 and 10:00 AM, primarily under traffic-influenced conditions. Chemical speciation identified OA as the main component of PM1 in both cities, making up 45.2% in Shanghai and 57.8% in Dezhou. Secondary inorganic species varied regionally: ammonium nitrate and sulfate were equal in Shanghai (27.4% each), while Dezhou had more nitrate (27.2%) than sulfate (15.0%). Multiple linear regression analysis yielded high goodness-of-fit values for the MSEs of major submicron particulate components in the winter atmosphere of both Shanghai and Dezhou. In Shanghai, the average MSEs of ammonium sulfate, ammonium nitrate, and organic matter were 4.2 m2/g, 3.85 m2/g, and 3.00 m2/g, respectively, while in Dezhou, the corresponding values were 10.75 m2/g, 10.15 m2/g, and 4.9 m2/g. OA was the main scattering contributor, accounting for 37.3% in Shanghai and 42.1% in Dezhou, with inorganic species playing secondary roles. In Shanghai, ammonium nitrate and sulfate each contributed 31.0% and 30.7% to the scattering budget. In Dezhou, higher pollution highlighted a nitrate-organic synergy, with 36.5% from nitrate and 42.1% from OA. Meteorological conditions influenced pollution events: Shanghai event (157.6 ± 83.0 Mm−1) was driven by humidity-enhanced OA formation (39.5%), while Dezhou event (820.5 ± 510.5 Mm−1) was due to nitrate-organic dominance. The study identifies two pollution patterns: Shanghai’s moderate pollution is influenced by secondary formation, while Dezhou experiences severe visibility degradation due to nitrate-organic interactions. The humidity sensitivity of OA in Shanghai and nitrate dominance in winter periods in Dezhou suggest targeted strategies: reducing VOCs in cities to lower secondary organic aerosols, and enhancing NOx controls in industrial northern areas. These findings offer insights into aerosol optical properties for improving visibility in winter in China.
This study effectively quantified the impact of ammonium sulfate, ammonium nitrate, and OA concentrations on the scattering coefficient. However, the model has certain limitations. Notably, the regression model did not account for potential interactions among components, such as the synergistic effect of ammonium sulfate and ammonium nitrate on scattering due to their combined hygroscopic growth. Additionally, the MSE for OA may not follow a strictly linear distribution, potentially approaching a saturation point as concentration increases. To improve the explanatory power and predictive accuracy of the model, future studies should explore nonlinear regression models or machine learning algorithms to capture the complex relationships between scattering coefficients and multiple atmospheric factors. Such approaches can provide a more comprehensive understanding of the light-scattering mechanisms of particulate matter. In addition, we did not explicitly include time-resolved size distributions and mixing states in this study. Hence, the “effective dry MSE” reflects coupled impacts of composition and dry size, and mixing state. Future work will incorporate concurrent size distribution and κ-based hygroscopicity to build a size–composition–humidity constrained parameterization and better apportion their contributions to MSE.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/atmos16111302/s1, Figures S1 and S2: Location of observation sites in Shanghai and Dezhou; Figure S3: Linear regression of scattering coefficients measured by CAPS and PAX in Shanghai; Figure S4: Time series and scatter plot of aerosol mass concentrations detected by the AMS plus BC in (a) Dezhou and (b) Shanghai; Figure S5: Relationship between simulated NH4+ and measured NH4+ during the sampling period; Figure S6: Fraction of major scattering components in PM1 mass; Figure S7: Diurnal variations in chemical composition concentrations of submicron particulate matter (PM1) observed in (a) Dezhou and (b) Shanghai; Figure S8: Modeling of thermal distribution patterns during Shanghai’s clean air episodes; Figure S9: Modeling of thermal distribution patterns during Shanghai’s polluted episodes; Figure S10. Modeling of thermal distribution patterns during Dezhou’s clean air episodes; Figure S11. Modeling of thermal distribution patterns during Dezhou’s polluted episodes; Tables S1–S3: Ionization efficiency, particle size correction parameters, and Relative ionization efficiency of AMS during two campaigns in Shanghai and Dezhou; Table S4: Regional variability in mass scattering efficiency of atmospheric chemical components; Table S5: Observation period classification for Dezhou and Shanghai.

Author Contributions

J.S.: data curation, methodology, writing—original draft M.L.: review and editing. Q.W.: data curation, formal analysis. W.Z.: conceptualization, methodology, project administration, supervision, funding acquisition, writing—review and editing. L.Q.: conceptualization, data curation, formal analysis. S.L.: conceptualization. S.G.: conceptualization, data curation, formal analysis. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the National Natural Science Foundation of China (Grant No. 42207118), the Opening Project of Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention (LAP3) (FDLAP24005).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data supporting the findings of this study are available from the corresponding authors upon request.

Acknowledgments

We would like to thank Min Hu and her aerosol group for valuable discussions. We also acknowledge Shanghai Academy of Environmental Sciences for providing instruments assistance.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Time series and diurnal variations in submicron aerosol scattering coefficients observed in (a) Shanghai and (b) Dezhou.
Figure 1. Time series and diurnal variations in submicron aerosol scattering coefficients observed in (a) Shanghai and (b) Dezhou.
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Figure 2. Major chemical components of submicron particulate matter (PM1) during winter in (a) Shanghai and (b) Dezhou.
Figure 2. Major chemical components of submicron particulate matter (PM1) during winter in (a) Shanghai and (b) Dezhou.
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Figure 3. Mass scattering efficiency (MSE) of particulate matter (PM) measured by different methods at multiple sampling sites. The values and references are shown in Table S5.
Figure 3. Mass scattering efficiency (MSE) of particulate matter (PM) measured by different methods at multiple sampling sites. The values and references are shown in Table S5.
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Figure 4. Time series of scattering coefficients and relative contributions of major chemical components in PM2.5 and submicron particles (PM1) during the (a) Shanghai and (b) Dezhou observation period. Period classifications: (P) polluted, (LP) lightly polluted, and (C) clean episodes.
Figure 4. Time series of scattering coefficients and relative contributions of major chemical components in PM2.5 and submicron particles (PM1) during the (a) Shanghai and (b) Dezhou observation period. Period classifications: (P) polluted, (LP) lightly polluted, and (C) clean episodes.
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Figure 5. Diurnal variations in scattering coefficients for submicron aerosol (PM1) chemical components in (a) Dezhou and (b) Shanghai. Circles denote mean values; red box-whisker plots indicate median (central line), interquartile range (25th–75th percentiles; box), and 10th–90th percentiles (whiskers).
Figure 5. Diurnal variations in scattering coefficients for submicron aerosol (PM1) chemical components in (a) Dezhou and (b) Shanghai. Circles denote mean values; red box-whisker plots indicate median (central line), interquartile range (25th–75th percentiles; box), and 10th–90th percentiles (whiskers).
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Figure 6. Variations in scattering contributions of major chemical components in submicron aerosols (PM1) with increasing total scattering coefficients during the observation period: (a) Shanghai and (b) Dezhou.
Figure 6. Variations in scattering contributions of major chemical components in submicron aerosols (PM1) with increasing total scattering coefficients during the observation period: (a) Shanghai and (b) Dezhou.
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Figure 7. Submicron particle scattering contributes during different episodes. (The center of each pie chart corresponds to the average scattering coefficient for the respective episode) in (a) Shanghai and (b) Dezhou.
Figure 7. Submicron particle scattering contributes during different episodes. (The center of each pie chart corresponds to the average scattering coefficient for the respective episode) in (a) Shanghai and (b) Dezhou.
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Shi, J.; Li, M.; Wang, Q.; Zhu, W.; Qiao, L.; Lou, S.; Guo, S. Scattering Characteristics of Submicron Particulate Chemical Components During Winter in Northern and Southern Chinese Cities. Atmosphere 2025, 16, 1302. https://doi.org/10.3390/atmos16111302

AMA Style

Shi J, Li M, Wang Q, Zhu W, Qiao L, Lou S, Guo S. Scattering Characteristics of Submicron Particulate Chemical Components During Winter in Northern and Southern Chinese Cities. Atmosphere. 2025; 16(11):1302. https://doi.org/10.3390/atmos16111302

Chicago/Turabian Style

Shi, Jialin, Mingzhe Li, Qinghong Wang, Wenfei Zhu, Liping Qiao, Shengrong Lou, and Song Guo. 2025. "Scattering Characteristics of Submicron Particulate Chemical Components During Winter in Northern and Southern Chinese Cities" Atmosphere 16, no. 11: 1302. https://doi.org/10.3390/atmos16111302

APA Style

Shi, J., Li, M., Wang, Q., Zhu, W., Qiao, L., Lou, S., & Guo, S. (2025). Scattering Characteristics of Submicron Particulate Chemical Components During Winter in Northern and Southern Chinese Cities. Atmosphere, 16(11), 1302. https://doi.org/10.3390/atmos16111302

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