Qualitative and Quantitative Analyses of Meteorological Impacts on Fine Particle Pollution in Winters of Cold Region in China

Abstract

Meteorological factors are the key drivers of air pollution. Stable weather conditions, the boundary layer height, and temperature inversion significantly influence the dispersion of particulate matter, which is also associated with the aerodynamic properties of particles. However, limited studies have been conducted on this topic in northeast China. This study investigates the influence of meteorological factors on PM_2.5 pollution under cold weather conditions, employing both qualitative and quantitative methods. The key meteorological factors considered include temperature difference, relative humidity, wind speed and direction, the boundary layer height (BLH), and temperature inversion. The stable weather index (SWI) is introduced as a quantitative measure of the stability of weather based on data from the last five winters in a typical megacity of northeast China. The monthly PM_2.5 concentrations recorded during the last five Februarys ranged from 59.79 μg/m³ to 85.68 μg/m³, with the highest daily concentration reaching 417 μg/m³. A new parameter, ‘temperature difference (ΔT)’, is defined in this study as the difference in temperature between two consecutive days, calculated by subtracting the previous day’s temperature from the current day’s. The temperature differences were found to have a significantly positive correlation with the differences in PM_2.5 concentrations (p < 0.01). The results showed that PM_2.5 pollution was associated with increased temperature, higher relative humidity, and lower wind speed, or any combination of these factors. The SWI explained 65% and 64% of the variances in air quality index (AQI) and PM_2.5 pollution, respectively. When the predicted SWI exceeds 10, the likelihood of particle pollution increases. A lower BLH, in conjunction with a thicker inversion layer, contributes to the formation of severe particle pollution. In the early stages of a winter pollution episode in Harbin, the temperature inversion layer thickened and intensified, with the inversion top height reaching approximately 200 m. The boundary layer remained below 200 m, resulting in a poor vertical dispersion condition. PM_2.5 pollution, therefore, is influenced by the combined effects of multiple meteorological factors. Our study quantitatively analyzed the characteristics of weather conditions and their impacts on air quality, which could provide scientific evidence for air pollution prediction and assist in making specific policy interventions, particularly for the upcoming ninth Asian Winter Games in Harbin in February 2025.

1. Introduction

Air pollution, especially PM_2.5 pollution, has long been a persistent environmental challenge and a threat to public health [1]. Numerous studies have established strong correlations between an elevated air quality index (AQI)/PM_2.5 concentrations and various physiological diseases [1,2,3,4,5,6,7,8,9,10]. For instance, the morbidity and mortality rates of cardiovascular diseases increase as the PM_2.5 concentrations rise [7,11]. Moreover, PM_2.5 pollution has been linked to a range of social issues and economic losses [12,13].

Air pollution is primarily influenced by emission sources and quantities, geographical locations, and meteorological conditions [14,15,16,17,18,19]. Among the three drivers, meteorological conditions are external and highly variable. Therefore, it is crucial to understand the role that meteorological conditions play in exacerbating PM_2.5 pollution [20]. Many prior studies have studied the associations between PM_2.5 pollution and meteorological factors in various sites and periods. For example, in New York, the highest diurnal and seasonal PM_2.5 concentrations were found in summer under moderate southwesterly wind conditions [21]. In contrast, severe PM_2.5 pollution is more common during the winter months in China, largely due to unfavorable meteorological conditions for particle dispersion [16,22,23,24]. In Sichuan Basin, a low atmospheric layer height, a low wind speed, and strong temperature inversion contributed to serious PM_2.5 pollution in the winters of 2015–2017 [22]. Similarly, high PM_2.5 concentrations were usually associated with a lower boundary layer height and a slow wind speed nationwide between 2013 and 2019 [25]. It is obvious that the dominant meteorological factors vary by location, and various meteorological factors exert different effects on pollutant levels. Additionally, the synergistic interactions between these factors collectively affect air quality [26,27].

Several meteorological factors have been identified as the dominant factors affecting air pollution in various locations and time periods, including relative humidity, wind speed, temperature, sunshine duration, temperature inversion, and the boundary layer height [18,25,28,29,30]. The precipitation and rising temperature are found to reduce the PM_2.5 concentrations, while increased sunshine duration and relative humidity could aggravate PM_2.5 pollution in the BTH region (2015–2020) [28]. Precipitation acts as a natural ‘cleaning’ mechanism for particulate matter, removing pollutants from the atmosphere. The inverse relationship between temperature and PM_2.5 concentrations is likely due to increased thermal movement and the evaporation of PM_2.5, which enhance particle dispersion and reduce the pollution level [31,32,33]. The positive correlation between relative humidity and PM_2.5 concentrations could be ascribed to the hygroscopic growth of particles, which could provide a larger surface area for homogeneous or heterogeneous reactions [34,35]. Similarly, the positive association between sunshine duration and PM_2.5 concentration might be linked to enhanced photochemical reactions that occur with more sunshine, which can contribute to the deterioration of air quality [36].

To better comprehend the influence of meteorological conditions on air pollution, various models have been developed and applied. For example, the Hurst index and back propagation neural network models were used to analyze the correlations between gaseous pollutants/meteorological factors and PM_2.5/PM₁₀ concentrations [37]. A combination of air dispersion modeling, nephelometric, and gravimetric studies have been employed to perform seasonal analyses of PM_2.5 dust deposition and trace metal apportionment in six Kuwait Governorate areas [38]. An interpolation model was also used to simulate pollution movement at individual monitoring points, incorporating the weather conditions [39]. Additionally, the air stagnation index (API), defined by the NCDC (National Climatic Data Center) as the percentage of steady days per month, has been used to assess the impact of climate change on static weather patterns in the 21st century [40]. The potential pollution index (PPI) and the PLAM index (which links air quality to meteorological elements) have been applied to quantitatively forecast meteorological conditions during pollution episodes. These models and indices are valuable for reflecting the comprehensive effects of meteorological conditions on air pollution, but it is difficult to apply them to environmental meteorological prediction and business contexts. Given these limitations, there is a need for a more accessible, comprehensive index of stable weather that incorporates a clear physical meaning, local features, and broad applicability. The stable weather index (SWI), which describes the stability degree of the atmosphere, serves as a useful tool for air pollution prediction. The SWI has been successfully applied in the North China Plain [41], Yangtze River Delta [42], and Twain-Hu Basin [43], where it demonstrated an effective correlation with the PM_2.5 pollution level. While more complex models, like CMAQ, WRF-Chem, and AERMOD, offer detailed chemical and physical simulations of air quality, the SWI provides a complementary and more accessible approach by focusing on weather stability. This makes the SWI particularly useful for forecasting air quality in the absence of detailed emission data or complex chemical models, as well as for early warnings and public health advisories. However, it is important to acknowledge the limitations of the SWI model. For example, its focus on current or near-future conditions may mean that it overlook longer-term atmospheric changes such as climate change; the SWI model may be calibrated for specific regions or climates, making it less reliable when applied to areas with different weather dynamics. To mitigate these issues, the SWI model could be used in conjunction with other meteorological tools and be designed with local features. For example, the TPI (local transport pollution index) + the SWI better reflected the wintertime PM_2.5 pollution level over the YRD region due to the fact that air pollution over the Yangtze River Delta region is affected not only by stable weather, but also by transport [42].

As a typical megacity in northeast China, Harbin’s air quality has improved significantly since the Air Pollution Prevention and Control Action Plan was implemented in 2013, with the PM_2.5 levels reducing from 72 µg/m³ in 2014 to 37 µg/m³ in 2023. Even so, Harbin suffers from PM_2.5 pollution, especially in cold winters, due to fossil combustion and adverse meteorological conditions. Most research in northeast China found that temperature, relative humidity, wind speed, and the boundary layer height are significant meteorological factors that could cause high-level local PM_2.5 pollution. The concentrations of PM_2.5 could be higher than 150 µg/m³ when the wind speed is less than 20 knots and the planetary boundary layer is below 500 m, along with temperature inversion in northeast China [24]. A low wind speed and boundary layer height are always observed during haze episodes in Harbin [25]. A negative correlation between PM_2.5 concentration and sunshine duration was observed in the Harbin–Changchun region, which might be due to enhanced atmospheric photolysis on the particulate matter, especially organic carbon with longer sunshine duration [21,35,36]. The boundary layer height has been identified to exhibit a negative association with PM_2.5 concentration [23,37,38,39]. Temperature inversion might also be the meteorological driver of particle pollution in winter [24,29,44,45]. And there is a lack of SWI application in cold regions. Hence, more qualitative and quantitative evidence is needed to prove the effects of stable weather, the boundary layer height, temperature inversion, and other factors on air pollution. Such evidence will help enhance the understanding of how these factors contribute to PM_2.5 pollution, particularly in colder climates, and support the development of more effective air quality management strategies.

According to the monitoring data of fine particles and meteorological factors in the winters of 2020–2024 in a typical megacity in northeast China, we defined a new parameter, temperature difference (ΔT), to investigate the effects of rising temperature over a short period on PM_2.5 pollution. We also developed the SWI with local features to quantify the impacts of stable weather on air quality; the influences of the boundary layer height and temperature inversion on PM_2.5 pollution are identified based on pollution episodes. Our findings, particularly the influence of the SWI on PM_2.5 pollution, are expected to provide an applicable reference for air pollution control and improve accuracy for PM_2.5 pollution prediction in winter in cold regions, especially for the upcoming ninth Asian Winter Games in Harbin in February 2025.

2. Materials and Methods

2.1. Study Sites

This study was conducted in Harbin, a typical city in northeast China (125°42′~130°10′ E, 44°04′~46°40′ N), which is the northernmost provincial capital of China. Harbin is a representative city, with manufacturing as the main industry and coal as the dominating energy. Due to its location on the southeastern edge of the continental climate zone in northeast Asia, Harbin is directly affected by cold air masses from Siberia during winter months. As a result, Harbin experiences exceptionally cold and prolonged winters, with minimum temperatures reaching as low as −30 °C. Coal combustion is the predominant energy used for heating in Harbin. The heating period of Harbin lasts for 6 months from October to April. PM_2.5 is the dominant pollutant during the heating period.

2.2. Data Sources and Monitoring Instruments

The data on air quality and air pollutants were from the website of the Atmospheric Super Station in Harbin (http://222.171.242.19:10011/asda/login.jsp, accessed on 30 June 2024). The website contained the data from the last 5 Februaries. The meteorological data were obtained from Harbin Meteorological Bureau (http://hl.cma.gov.cn/bmgk/gdsqxj/hebsqxj/, accessed on 30 June 2024), including daily temperature, relative humidity, wind speed and direction, and radar monitoring results. Raman Lidar’s data analysis was supported by Hefei Science of Light Technology Co., Ltd. (Hefei, China). The Raman Lidar station is located at Northeast Agricultural University (126°73′ E, 45°74′ N) in Harbin.

2.3. Statistical Analysis

SPSS (IBM SPSS Statistics 25, USA) was used to conduct regression analysis and Spearman’s correlation. The regression analysis results were used to investigate the influences of meteorological factors on air pollution, while Spearman’s correlation explored if there are significant correlations between the meteorological conditions and PM_2.5 concentrations or AQI values. Figure 1 and Figure 9 was created using a Raman lidar monitoring instrument (Hefei Science of Light Technology Co., Ltd., Hefei, China).

2.4. Calculation of Local Stable Weather Index (SWI)

2.4.1. Data Sources

The meteorological data at 08:00 and 20:00 daily in February from 2020 to 2024 in Harbin were obtained from Heilongjiang Meteorological Bureau (http://hl.cma.gov.cn/bmgk/gdsqxj/hebsqxj/, accessed on 30 June 2024).

2.4.2. Selection of Meteorological Factors

Based on the data from other research projects, correlation coefficients between the mostly used meteorological factors and the PM_2.5 concentration were calculated, showing significant correlations at the 0.01 level (pair-tailed). Considering practical needs and references, sea level pressure, temperature, the temperature dew point difference, the 10 min average wind speed, the mixing layer height, the temperature difference between 925 hPa and the surface, the temperature difference between 850 hPa and the surface, and the wind dispersion coefficient were determined as 8 meteorological factors in the construction of the SWI in Harbin. Among them, the wind dispersion coefficient was defined as the multiplier of wind speed at the ground, 925 hPa, and 850 hPa. The mixing layer height was calculated using Equation (1), where MH is the height of the mixed layer, m; P is the value of the stability level of Pascual; (T − T_d) is the temperature dew point difference, °C; U_z is the average wind speed observed at the height of Z, m/s; Z₀ is ground roughness; and f is the geostrophic parameter.

$$MH={\displaystyle \frac{121}{6}}\left(6-P\right)\left(T-T_d\right)+{\displaystyle \frac{0.169P\left(U_z\right)+0.257}{12f \mathrm{l}\mathrm{n}(Z-Z_0)}}$$

(1)

2.4.3. Calculation of Weight Coefficients and SWI

To enhance the correlation between the SWI and PM_2.5 concentrations, the local optimization of weight coefficients of the selected 8 meteorological factors proceeded as follows: Each factor was divided into 9 equal parts based on their distribution, and the PM_2.5 concentration mean corresponding to each interval was calculated. The ratio of each interval’s PM_2.5 concentration mean to the overall average PM_2.5 concentration was taken as the weight coefficient for the factor’s distribution segment, as shown in Equation (2), where W_i represents the weight of the index in a certain segment of meteorological element i, a_i is the PM_2.5 concentration mean in a certain segment of meteorological element i, and b is the overall average PM_2.5 concentration.

$$W_i=a_i/b,$$

(2)

$$\mathrm{S}\mathrm{W}\mathrm{I}={\sum }_{i=1}^8{W}_i$$

(3)

Finally, the sum of the weight coefficients of the 8 factors in a certain period was calculated as the final SWI (Equation (3)), reflecting the comprehensive atmospheric diffusion conditions. The greater the weight of one meteorological element is, the more significantly this element affects the formation of stable weather. The calculation results are shown in Table S1.

2.5. Calculation of T and PM_2.5 Differences

ΔT refers to the difference in temperature between two adjacent days, calculated by the temperature of a day minus that of the previous day (Equation (4)).

ΔT = T_d − T_d−1,

(4)

ΔPM_2.5 refers to the difference in PM_2.5 concentration between two adjacent days, calculated by the PM_2.5 concentration of a day minus that of the previous day (Equation (5)).

ΔPM_2.5 = PM_{2.5 d} − PM_{2.5 d−1},

(5)

2.6. Lidar Monitoring

2.6.1. Principle of Radar Monitoring Technology

Lidar, light detection and ranging with laser as the light source, remotely senses the atmosphere by monitoring radiative signals resulting from the interaction between the laser and the atmosphere. The interaction of light waves with the atmosphere generates radiative signals that contain information related to gas atoms, molecules, atmospheric aerosol particles, clouds, etc. Utilizing corresponding inversion methods, information about gas atoms, molecules, atmospheric aerosol particles, clouds, and other atmospheric components can be derived. Thus, the technical foundation of lidar lies in the various physical processes resulting from the interaction between light radiation and atmospheric components.

Lidar mainly consists of three parts: the light source emission system, the optical receiving system, and the data acquisition and analysis system. An Nd: YAG laser emits 1064 nm near-infrared light, frequency-doubles to generate 532 nm green light, and frequency-triples to produce 355 nm ultraviolet light. During the propagation of the laser in the atmosphere, it undergoes elastic scattering among the atmospheric molecules and particulates. The backscattered signals from the atmospheric molecules and particulates are received by the lidar telescope receiving system. After passing through the aperture, beam splitter, and interference filter, they are divided into three channels (532 nm horizontal and vertical polarization channels and a 355 nm channel) and received by a photomultiplier tube (PMT). The acquisition system continuously samples the signals at a sampling rate of 5 kHz and a vertical distance resolution of 7.5 m and transmits the results to a computer for inversion, ultimately obtaining the spatial and temporal distribution maps of the extinction coefficient and depolarization ratio of the aerosol at this observation point.

2.6.2. Data Quality Assessment

The signal-to-noise ratio (SNR) of lidar can reflect the quality of radar signals (Figure 1). According to the SNR results from each channel evaluation, except under special weather conditions, the effective detection heights (where SNR ≥ 3) for both the 355 nm and 532 nm channels were greater than 5.0 km during the monitoring period. The values 532p and 532s typically refer to two different polarizations or configurations of a 532 nm wavelength laser, commonly green, where ‘p’ indicates polarization in the plane of the page, and ‘s’ refers to polarization perpendicular to the page. The value 355 refers to a 355 nm wavelength, which is ultraviolet.

Figure 1. Pseudo-color map of signal-to-noise ratio (SNR) results of Raman radar channels (top: 532p; middle: 532s; lower: 355).

Figure 1. Pseudo-color map of signal-to-noise ratio (SNR) results of Raman radar channels (top: 532p; middle: 532s; lower: 355).

2.6.3. Temperature and Humidity Consistency Calibration

Temperature Calibration: During clear weather conditions without significant temperature inversion, the radar monitoring data between 100 and 200 m were calibrated against the ground environmental temperatures measured using a small-scale temperature and humidity meteorological instrument. Two calibration periods were selected. Calibration Period 1: From 31 January to 3 February, showing a maximum correlation of 0.95 between the radar temperature monitoring results at 105 m and the ground temperature data. Calibration Period 2: From 18:00 5 February to 12:00 7 February, showing a maximum correlation of 0.98 (Figure 2).

Humidity Calibration: During clear weather conditions without significant cloud cover, the radar monitoring data between 100 and 200 m were calibrated against ground environmental humidity measured using a small-scale temperature and humidity meteorological instrument. The selected calibration period ranged from 10:00 30 January to 0:00 2 February, showing a maximum correlation of 0.98 between the radar humidity results at 105 m and the ground humidity data (Figure 3).

3. Results and Discussion

3.1. Influences of Meteorological Factors on PM_2.5 Concentrations

3.1.1. Temperature Differences

Increased temperature differences (ΔT), defined as the difference in temperature between two adjacent days, are associated with higher PM_2.5 concentrations. ΔPM_2.5 is positively correlated with ΔT, according to the positive slope (k = 2.1036) (Figure 4a) and the significantly positive correlation coefficient (p = 0.003 < 0.01) (Figure 4b). In general, the increases in PM_2.5 concentrations mostly happened when ΔT rose. The rise in temperature in a short period likely enhanced the photochemical reactions, leading to the formation of more secondary particles. The mean of the daily maximum temperatures in February of the last five years was −6.17 °C, with heavy pollution typically occurring at −5 °C. A notable nine-day continuous pollution episode happened on 2–11 February 2024, coincided with a warming period during winter.

Moreover, the positive correlations of the ΔT and ΔPM_2.5 concentrations represent a sudden and intermittent temperature rise that might lead to temperature inversion that impedes the diffusion of particles. Compared to the other studies in northeast China, which have observed both negative or positive correlations between the T and PM_2.5 concentrations over a larger time scale (e.g., throughout the heating period or over a whole year) [24,26,29,44,46], this study emphasized the influence of ΔT rises only in the winter months. On a seasonal or yearly scale, a negative correlation between the T and PM_2.5 concentrations is often attributed to fossil energy consumption and stable weather under lower temperatures. Higher temperatures might promote airflow circulation and accelerate PM_2.5 dispersion [22]. On the other hand, the positive correlation observed between T and PM_2.5 is likely due to enhanced photochemical reactions at higher temperatures. These reactions promote the production and accumulation of secondary organic and inorganic aerosols, which subsequently lead to an increase in the PM_2.5 levels [47].

3.1.2. Relative Humidity

Higher relative humidity (RH) played an important role in PM_2.5 pollution in February over the last 5 years. The monthly PM_2.5 concentrations ranged from 59.79 μg/m³ to 85.68 μg/m³. The trends of RH and PM_2.5 were mostly consistent during the period in Harbin (Figure 5). The peaks in PM_2.5 concentration often coincided with peaks in RH. For example, the highest PM_2.5 level (417 μg/m³) appeared on 10th Feb, 2024, when RH reached its highest level, 77.5% in this month. The PM_2.5 and AQI values had significantly positive correlations with RH (r = 0.47 ** with PM_2.5, r = 0.44 ** with AQI) (Figure S1), which aligns with the findings in other studies [23,44]. RH has been identified as a dominant meteorological factor affecting PM_2.5 in the cold winters of the Harbin–Changchun megalopolis in China, where a higher RH leads to a rapid increase in the PM_2.5 levels [24]. It is understood that RH enhances PM_2.5 pollution by accelerating the formation of secondary aerosols from heterogeneous reactions [48]. Additionally, an elevated RH could stabilize the atmosphere, reducing vertical mixing and inhibiting the dispersion of pollutants [44].

3.1.3. Wind Direction and Speed

Wind speed (WS) and wind direction (WD) could affect the transport and dispersion of pollutants in the atmosphere. During the last five years, high PM_2.5 concentrations were mostly observed at a low WS (0–2 m/s) and a southwest WD in February in Harbin (Figure 6). A low WS could lead to atmospheric stability and pollutant accumulation. The significantly negative correlations of WS vs. PM_2.5 and WS vs. AQI (PM_2.5: r = −0.54 **; AQI: r = −0.49 **) (Figure S2) indicate the strong influence of wind speed. The prevailing winds were from the west and southwest. Most pollution episodes occurred under westerly wind conditions, suggesting that there might be pollution transport from southeast Harbin [35,49]. Other studies in the BTH region reported the PM_2.5 concentrations declined when the wind speed increased until they reached a tipping point, and then the PM_2.5 concentrations increased while the wind speed rose. This could be attributed to dust events caused by strong winds from arid and semiarid areas in northwest China [28,50]. Weaker wind speeds (0–4 m/s) were predominant in Harbin in February; meanwhile, the earth was covered by ice and snow. Thus, wind speed and the PM_2.5 concentration maintain a negative correlation.

3.2. Effects of Stable Weather on PM_2.5 Concentrations

Stable weather has exerted a significant influence on the air quality and particulate matter concentration in Harbin during the last five Februarys. The stable weather index (SWI) was applied to investigate the influence of stable weather on air pollution. A higher SWI value represents less atmospheric motion, while more stability induces the poorer dispersion of air pollutants. The time variations in the SWI, AQI, and PM_2.5 presented a similar trend (Figure 7). The PM_2.5 and AQI values were significantly positively correlated with the SWI; in this case, higher AQI and PM_2.5 corresponded to higher SWI values. Hence, the air quality becomes worse as the SWI values rise (Figure S2).

The SWI, AQI, PM_2.5, and PM₁₀ concentrations were grouped within each air quality level according to the air quality classification of China. The averages are listed in

Table 1. SWI, AQI, PM_2.5, and PM₁₀ means in each air quality classification.

	SWI	AQI Mean	PM2.5 Mean	PM10 Mean
0 < AQI ≤ 50	8.15	37.83	25.33	32.80
50 < AQI ≤ 100	9.40	72.25	52.10	62.93
100 < AQI ≤ 150	10.04	116.22	87.69	99.82
150 < AQI ≤ 200	10.40	172.85	130.65	143.85
200 < AQI ≤ 300	9.43	227.71	177.71	193.94
AQI > 300	11.23	382.67	277.67	413.22

. The SWI is positively associated with the degree of air pollution (Table 1). When the SWI is less than nine on average, the air quality reaches a good level, and pollution is not inclined to occur. However, pollution is prone to occur when the SWI is above 11. The regression analysis results demonstrated that the SWI explained 65.03% and 63.95% of the variance in AQI and PM_2.5 (Figure 8) [46]. Therefore, stable weather affects air pollution significantly, and the SWI could be applied for more accurate air quality prediction, especially during major events [51]. In the future, the SWI should be applied in different regions according to their local meteorological elements, thresholds, and weights. Comparative studies of the SWI between cities, along with other environmental and meteorological factors, could further improve our understanding of the air quality dynamics across different regions.

3.3. Effects of Boundary Layer Height and Temperature Inversion on PM_2.5 Concentrations

The boundary layer determines the vertical mixing capacity of the atmosphere. This is an important factor in the diffusion and dispersion of pollutants at the ground level. According to Figure 6, the boundary layer approached the lowest height (200 m) in Episode 1 (E1) (Figure 9a). By that time, particles started to accumulate (Figure 9b). The PM_2.5 concentrations rose from 98 to 136 μg/m³ in E2. By the end of E2, the boundary layer was raised to around 500 m, and particle pollution was mitigated, owing to better vertical dispersion conditions. When the BLH stayed under 500 m again at the beginning of E3, the particle concentrations rose and maintained high.

Moreover, temperature inversion happened at the same time (Figure 9c), which could also shape the pollution levels. Temperatures rising with altitude could lead to the formation of a temperature inversion layer, which slows down the vertical movement of air and traps pollutants close to the ground. In this case, the air pollutants accumulate, worsening the air quality in the region. At the beginning of the event, the inversion layer became thicker, and its intensity became stronger. The top height of the inversion layer was about 200 m, and the boundary layer was lower than 200 m; thus, the vertical dispersion conditions were poor. By that time, fine particles gradually accumulated near the ground. The altitude below 500 m became stable, the horizontal dispersion conditions deteriorated, and temperature inversion accelerated the accumulation of fine particles below 400 m.

A low BLH and temperature inversion enhance pollution during special events. An intense pollution episode happened during the Chinese New Year in February 2024, induced by fireworks. K⁺, as an indicator of fireworks, accounted for the largest proportion (68.02%) of elements of PM_2.5. Stable weather and westerly winds led to poor horizontal dispersion conditions on New Year’s Eve. A boundary layer of less than 200 m in height aggravated fine particle accumulation. And temperature inversion happened at the same time at a height of 400 m. Superimposed on the joint impact of the fireworks display during the Chinese New Year, the air experienced a severe pollution on 10 February.

The levels of particle pollution are significantly related to the height of the boundary layer and the inversion layer. A lower BLH, associated with a thicker inversion layer, is prone to form serious particle pollution. According to the analysis of pollution episodes, PM_2.5 pollution is influenced by the efforts of multiple meteorological factors simultaneously [26], including high relative humidity, stable weather, a low boundary layer height, and temperature inversion, that could shape extremely severe pollution events.

This study focused exclusively on winter data and PM_2.5 pollution for the following reasons. Harbin suffers from PM_2.5 pollution in winter because of fossil consumption and adverse meteorological conditions, while air pollution is minimal in the other seasons. PM_2.5 is the primary pollutant in Harbin’s winter. Thus, this study concentrated on the influences of meteorological conditions on PM_2.5 pollution specifically in winter, which could assist in local air quality management, especially for the upcoming ninth Asian Winter Game in Harbin in February 2025.

4. Conclusions

This study focused on the influence of meteorological factors on PM_2.5 pollution in the last five winters in a typical megacity in northeast China. It investigated the combined efforts of multiple factors, including temperature differences, relative humidity, wind speed and direction, temperature inversion, the boundary layer height, and atmospheric stability. Using quantitative methods, the dominant meteorological factors affecting air quality were identified, and the trends in air quality were explained based on these key parameters. The main findings are as follows: A new parameter, temperature difference (ΔT), was defined as the difference in temperature between two consecutive days, calculated by subtracting the previous day’s temperature from the current day’s temperature. This study found that ΔT was positively correlated with the differences in PM_2.5 concentration (p < 0.01), indicating a sharp rise in temperature over a short period could lead to the emergence of PM_2.5 pollution. A model of the stable weather index (SWI) was introduced to quantify the influence of stable weather conditions on PM_2.5 pollution based on the features of local weather. The SWI explained 65% and 64% of the variance in AQI and PM_2.5 pollution, respectively. When the predicted SWI exceeds 10, particle pollution has a high probability of happening. Lidar technology, an advanced tool for monitoring air quality and atmospheric conditions, was used to investigate the boundary layer height (BLH) and temperature inversion. A lower BLH remarkedly enhanced air pollution by limiting vertical dispersion. Additionally, temperature inversion, which is common in winters in cold regions, was identified as the dominant factor contributing to severe air pollution. Temperature inversion inhibits vertical air movement, restricting the dispersion of pollutants. Moreover, PM_2.5 was also enhanced by a higher RH and a lower wind speed. Hence, these meteorological factors contribute to air quality jointly, and some of them can occur simultaneously during a single pollution event. Meteorological factors are not easily interrupted by human activities. Investigations of their characteristics in target regions would help local authorities to control emissions from anthropogenic sources when the weather conditions are unfavorable for pollution dispersion. Therefore, to mitigate heavy pollution in Harbin’s winters, it is essential to strengthen the pollution prevention and control measures according to meteorological forecasts. Thus, the findings of this study could promote the prediction accuracy for PM_2.5 pollution and assist in making specific strategies in Harbin’s winter, especially during the ninth Asian Winter Games in February 2025.