Small-scale spatial and temporal evolution characteristics of PM2.5 and PM10 concentrationsand the influence of meteorological factors: a case study of Zhenjiang Port, China

Small-scale spatial and temporal evolution characteristics of PM2.5 and PM10 concentrationsand the influence of meteorological factors: a case study of Zhenjiang Port, China | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Small-scale spatial and temporal evolution characteristics of PM2.5 and PM10 concentrationsand the influence of meteorological factors: a case study of Zhenjiang Port, China Minxue Zheng, Jingya Zhao, Yutao Jiang, Jun Zhang, Zhen Ju, Feng Jia This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6004740/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 29 Apr, 2026 Read the published version in Sustainable Environment Research → Version 1 posted 5 You are reading this latest preprint version Abstract The analysis of the small-scale spatial and temporal evolution characteristics of particulate matter (PM), especially Particulate Matter 2.5 (PM 2.5 ) and Particulate Matter 10 (PM 10 ), and the influence of meteorological factors is crucial for understanding the mechanisms of large-scale PM formation and transport, and further to develop precise dust pollution prevention measures. In this study, Zhenjiang Port was analyzed first time, which was based on hourly data of PM 2.5 and PM 10 concentrations from 15 air quality monitoring stations from January 2019 to December 2020. With respect to evolution characteristics of PM 2.5 and PM 10 concentrations within the study area, the results showed that: ( 1 ) Daily averages exhibit varying degrees of exceedances, with PM 2.5 exceeding Level 3 standards accounting for 13.68%. ( 2 ) Spatially, both PM 2.5 and PM 10 display a pattern of higher concentrations in the western areas and lower concentrations in the eastern areas. ( 3 ) Diurnal fluctuations are relatively stable during the day, with peak values occurring around 06:00 in the morning. ( 4 ) PM 2.5 has a long retention time in the air with hysteresis. ( 5 ) For the relationship of concentration and temperature, it shows positive correlation for below 6°C while negative correlation for above 6°C. ( 6 ) For the relationship of concentration and relative humidity, it shows positive correlation for below 80% and above 90%, while negative correlation for at 80–90%. The research findings can provide a theoretical basis for formulating atmospheric pollution prevention and control measures for Zhenjiang Port, while also serving as a reference for studying the evolution mechanisms of PM in Zhenjiang City and even larger-scale regions. PM2.5 and PM10 Zhenjiang Port spatial and temporal evolution characteristics meteorological factors Inverse Distance Weighting (IDW) Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 1 Introduction Zhenjiang Port is an important port in the Yangtze River Delta region, located in the western part of Zhenjiang, Jiangsu, China. Its throughput reached 329.2 million tons in 2019, ranking in the top ten ports of China for foreign trade [1]. During the logistics processes at the port, operations such as bulk cargo handling, storage and transportation have generated a large amount of dust, which exacerbated the particulate matter (PM) pollution in the port area and surrounding regions [‎2,‎3]. Particularly with the rapid increase in the throughput of easily dust-generating bulk cargo like iron ore and coal, dust pollution has become increasingly prominent, posing greater challenges for control, and resulting in adverse health effects for workers, operational environments, and surrounding ecosystems [‎4-‎6]. Among the dust pollutants, inhalable particles with diameters ≤ 10 µm (PM 10 ) and lung-inhalable particles with diameters ≤ 2.5 µm (PM 2.5 ) are the primary components. These particles are closely associated with respiratory system diseases [‎7,‎8], ecological security [‎9,‎10] and economic losses [‎11-‎13]. The concentration levels of PM 2.5 and PM 10 are influenced by various factors, including meteorological conditions [‎14], industrial emissions [‎15], urban greenery, and road distribution [‎16], and exhibit significant spatial and temporal evolution characteristics. On a temporal scale, PM 2.5 and PM 10 concentrations typically exhibit periodic daily "U-pulse" shaped variations and monthly "U" shaped fluctuations [‎17,‎18] and show seasonal characteristics of higher levels in winter and spring, and lower levels in summer and autumn [‎19,‎20]. On a spatial scale, many research studies have largely focused on PM pollution in major cities or regional clusters, such as the Beijing-Tianjin-Hebei region [‎21], the Yangtze River Delta [‎22], and the southern part of Liaoning Province [‎23]. However, studies on the spatial and temporal evolution of PM 2.5 and PM 10 in small-scale regions and their influencing factors are still relatively limited. In recent years, research on PM pollution in ports and their surrounding cities has been increasing. For example: Ship emissions at Qingdao Port significantly contribute to the seasonal variation in PM 2.5 concentrations in Qingdao City, with the highest contribution in summer (13.1%) and the lowest in winter (1.5%) [‎24]. Shipping and port emissions in Thessaloniki Port, Greece, contribute on average 9–13% to PM 2.5 concentrations [‎25]. The main sources of PM 2.5 in Kaohsiung Port include industrial emissions, secondary aerosols, ship and vehicle exhaust, marine spray, and soil dust [‎26]. However, despite being a critical port in the Yangtze River Delta region, Zhenjiang Port faces serious PM pollution issues but lacks research on the spatiotemporal variations and influencing factors. This highlights the need for further studies to understand and address the PM pollution concerns in Zhenjiang Port and its surrounding areas. Therefore, the present study is for the first time utilized hourly PM 2.5 and PM 10 concentration data from 15 monitoring points within Zhenjiang Port from January 2019 to December 2020, along with concurrent meteorological data. The analysis aimed to investigate the spatiotemporal characteristics of PM within the port area and explore the impact of meteorological factors on PM concentration variations. This study seeks to provide scientific basis for the precise control of PM pollution in the region and offer insights for the study of PM evolution mechanisms on a larger scale. 2 Data and Methods 2.1 Research Data Zhenjiang Port located between 32°11′10.25″N − 32°11′27.25″N and 119°37′56.53″E − 119°40′16.20″E, facing the Yangtze River. It experiences a subtropical monsoon climate with distinct seasons, having an average annual temperature of 17.4°C and an annual precipitation of 1248.9 mm. The PM 2.5 and PM 10 concentration data were collected from 15 automatic air quality monitoring points within Zhenjiang Port as shown in Fig. 1 and the longitudes and latitudes are listed in Table 1 . The hourly concentration was measured using the beta attenuation monitor [‎27,‎28] with measurement accuracy exceeds 90%. Meteorological data originated from the meteorological station at Zhenjiang Port, including temperature, relative humidity, wind speed, and wind direction, the measurement accuracy are ± 0.2°C, ± 2% RH, ± (0.3 + 0.03 V) m s − 1 and ± 3°, respectively. All data underwent quality control, scrutiny for redundant or anomalous data, resulting in 148,626 valid sets of PM 2.5 and PM 10 data. Table 1 The longitudes and latitudes of 15 air automatic monitoring points serial number name longitude latitude A Donggang 5 # gate 119°39′56″E 32°11′57″N B Donggang 7 # gate 119°40′16″E 32°12′09″N C Donggang 23 # approach 119°40′07″E 32°12′24″N D Donggang 18 # approach 119°39′18″E 32°12′13″N E Dagang 23 # approach 119°40′51″E 32°12′01″N F Dagang 3 # gate 119°39′39″E 32°11′50″N G Dagang basin 119°39′17″E 32°11′53″N H Dagang 1 # gate 119°39′19″E 32°11′38″N I Dagang 1 # berth 119°39′04″E 32°11′47″N J Jingang 10 # gate 119°38′24″E 32°11′18″N K Jingang Jingwu Road South 119°38′52″E 32°11′28″N L Jingang Jingwu Road North 119°38′47″E 32°11′39″N M Jingang 26 # approach 119°38′17″E 32°11′32″N N Jingang 24 # approach 119°37′57″E 32°11′26″N O Jingang 8 # gate 119°38′02″E 32°11′10″N 2.2 Research Methods The Inverse Distance Weighting (IDW) method is employed to investigate the spatial distribution characteristics of PM 2.5 and PM 10 concentrations in the region. Utilizing data from 15 monitoring points, this method estimates data at points to be evaluated based on the spatial relationships between these points and known monitoring points. By estimating interpolation, data at points to be evaluated are obtained, allowing for the determination of the spatial distribution across the entire study area and further analysis of its spatiotemporal evolution. The principle involves weighting the average based on the distances between interpolation points and sample points, where interpolation points closer to sample points are assigned greater weights, and vice versa. The method is defined as follows [‎29]: $$\:{z}_{0}=\frac{\sum\:_{i=1}^{n}zi{d}_{i}^{-\beta\:}}{\sum\:_{i=1}^{n}{d}_{i}^{-\beta\:}},i=\text{1,2},\dots\:,n.$$ 1 where, z 0 : the predicted value at the interpolation point, z i : the value at the i th sample point, d i : the distance from the i th sample point to the interpolation point, n : the number of points affect the interpolation points, β : the power of distance, typically set as 2. The Pearson correlation coefficient method was utilized to study the relationships between PM 2.5 and PM 10 concentrations and meteorological factors. The degree of linear correlation between x and y variables is calculated as follows [‎30]: $$\:r=\frac{\sum\:(x-\stackrel{-}{x})(y-\stackrel{-}{y})}{\sqrt{\sum\:{(x-\stackrel{-}{x})}^{2}\sum\:{(y-\stackrel{-}{y})}^{2}}}$$ 2 when 0 < r < 1, x and y are positively correlated; when-1 < r < 0, x and y are negatively correlated; when | r | = 1, x and y are completely related; when | r | = 0, x and y are independent. 3 Results and Discussion 3.1 PM 2.5 and PM 10 concentrations According to the “Technical Regulations of Environmental Air Quality Index (AQI)”, the daily average concentrations of PM 2.5 and PM 10 are divided into six levels, as listed in Table 2 . Table 2 Evaluation of Air Pollution Index Air quality status Air quality level PM 2.5 concentration PM 10 concentration Good Level 1 0–35 µg m − 3 0–50 µg m − 3 Moderate Level 2 35–75µg m − 3 50–150 µg m − 3 Lightly Polluted Level 3 75–115 µg m − 3 150–250 µg m − 3 Moderately Polluted Level 4 115–150 µg m − 3 250–350 µg m − 3 Heavily Polluted Level 5 150–250 µg m − 3 350–420 µg m − 3 Very Poor Level 6 250–500 µg m − 3 420–600 µg m − 3 Figure 2 illustrates the temporal evolution of PM 2.5 and PM 10 concentrations from January 2019 to December 2020. For PM 2.5 , 8.34% reach Level 1, 77.98% reach Level 2, and 13.681% reach Level 3 or higher. For PM 10 , 12.86% reach Level 1, 85.23% reach Level 2, and 1.91% reach Level 3 or higher. As can be seen, Zhenjiang Port experiences episodes of mild pollution. Among the pollutants, PM 2.5 has the greatest impact on the port and PM 2.5 has more hazardous than PM 10 due to smaller size and stronger ability to adsorb harmful substances. 3.2 Spatial distribution characteristics The monitoring points at Zhenjiang Port are approximately 700 m apart, with high coverage density and uniform distribution. Detailed analysis of the regional distribution characteristics of port PM can be conducted using ArcGIS. Figure 3 illustrates the spatial distribution of PM 2.5 and PM 10 concentrations at Zhenjiang Port using the IDW interpolation method. It can be seen PM 2.5 and PM 10 concentrations are higher at the riverside areas (I, L, M, N monitoring points) with average concentrations of 63.64 µg m − 3 and 82.15 µg m − 3 respectively, while lower at the non-riverside areas (J, K monitoring points) with average concentrations of 48.12 µg m − 3 and 62.54 µg m − 3 respectively. Both PM 2.5 and PM 10 concentrations exhibit a pattern of being higher in the west and lower in the east. Within the study area, the points with the highest concentrations of PM 2.5 and PM 10 are both at the N monitoring point, measuring 62.87 µg m − 3 and 85.75 µg m − 3 respectively, while the points with the lowest concentrations are both at the J monitoring point, measuring 40.96 µg m − 3 and 53.47 µg m − 3 respectively. Based on the actual operational conditions of the port and its layout, it is evident that monitoring points I, L, M, and N are situated near the riverside berths and connecting bridges. These areas are bustling with activities related to bulk cargo production, loading, unloading, and transshipment. These results in the significant generation of PM from operations, roads, and vehicle exhaust particles. Particularly, the N monitoring point is positioned at the junction of the main transport routes between the bulk cargo production area and the container handling area. With more frequent operations such as cargo handling and container transport at the terminal, this area exhibits higher PM concentrations compared to others. The J and K monitoring points are adjacent, and both located beside the highways in the western and central parts of the port area. The J monitoring point is close to the office area but far from the docks and material production areas. Additionally, being situated within a green belt, where green plants can capture particles, results in lower dust concentrations at this point. On the other hand, the K monitoring point is distant from the docks and major transportation paths, with nearby storage yards implementing dust control measures like dense mesh covers. Moreover, the high level of greenery near this monitoring point contributes to relatively lower dust concentrations. In summary, the spatial distribution of PM concentrations at Zhenjiang Port is primarily influenced by factors such as dust generation from production and storage operations, road dust, vehicle emissions, and loading and unloading activities. 3.3 temporal evolution characteristics Figure 4 illustrates the overall trend of PM 2.5 and PM 10 monthly average concentration values at Zhenjiang Port from 2019 to 2020, showing a "U" shape pattern. Specifically, PM 2.5 and PM 10 concentrations exhibited a decreasing trend from January to July, reached their lowest point in August (35.47 µg m − 3 and 46.09 µg m − 3 in 2019, 37.52 µg m − 3 and 48.77 µg m − 3 in 2020, respectively), were at the bottom of the "U" shape from July to September, and then increased from September to December. The concentrations in January (85.27 µg m − 3 and 114.78 µg m − 3 in 2019, 78.32 µg m − 3 and 106.7 µg m − 3 in 2020, respectively) and December (76.85 µg m − 3 and 100.31 µg m − 3 in 2019, 74.13 µg m − 3 and 98.11 µg m − 3 in 2020, respectively) were higher, representing the two ends of the "U" shape. In 2019, PM 2.5 levels in January were 2.40 times that of August, while in December, they were 2.17 times higher. For PM 10 , January levels were 2.49 times higher than August, and December levels were 2.18 times higher. In 2020, January PM 2.5 was 2.09 times that of August, December PM 2.5 was 1.98 times higher, January PM 10 was 2.19 times higher, and December PM 10 was 2.01 times higher than August. The distribution of monthly rainfall in Zhenjiang City is highly uneven, with the city entering the plum rain season in June and July, lasting approximately 19 days and accounting for 60% of the annual rainfall. August and September see frequent typhoons and heavy rainfall, which significantly impact PM through erosion and adsorption. These findings align closely with the trends in monthly average PM 2.5 and PM 10 concentrations in Nanjing [‎31]. Among the 15 monitoring points, based on hourly concentration data of PM 2.5 and PM 10 , the two highest points, N and I, and the two lowest points, K and J, were selected. Taking a week with severe PM pollution over the two years as an example, the daily variation characteristics of PM 2.5 and PM 10 concentrations were analyzed. During the day when port activities are in progress, the daily variation curves of PM 2.5 and PM 10 concentrations generally align closely, exhibiting relatively stable fluctuations. With lower temperatures at night in the port area and weaker airflow dispersion, coupled with settling influences, PM concentrations gradually increase and peak around 06:00 in the morning. Subsequently, due to factors such as enhanced solar radiation and rising temperatures, PM disperses and dilutes, leading to a decrease in concentration. After 13:00, as solar radiation intensity decreases, PM concentrations gradually rise again, as shown in Figs. 5 and 6 . After nightfall, most production activities cease, leading to a decrease in both PM 2.5 and PM 10 concentrations. However, the ratio of PM 2.5 /PM 10 shows an increase rather than a decrease (Fig. 7 ). This phenomenon is attributed to the fact that PM 2.5 have smaller diameter compared to PM 10 , resulting in more collisions with gas molecules during movement in the air, prolonging their residence time. Additionally, sunlight can enhance the oxidation state of chromophores in atmospheric PM 2.5 , affecting their photochemical reactivity [‎32]. At the heavily polluted N and I riverside areas, PM 2.5 proportions are not high, whereas at non-riverside areas K and J, PM 2.5 proportions are higher, indicating a significant influence of PM 2.5 on pollution levels. Additionally, due to its slower settling rate and lesser impact from PM dispersion, PM 2.5 also experiences prolonged duration within the aerosol particles. 3.4 The influence of meteorological factors In environments with relatively stable dust pollution emissions, meteorological factors play a crucial role in influencing the emission, dispersion, transport, and deposition of dust. This study collects and organizes dust concentration data and related meteorological data (including temperature, humidity, wind speed, and wind direction) from various monitoring points. Using Pearson correlation analysis in SPSS 25.0 software, it investigates the impact of meteorological factors on dust, delving into the correlation between meteorological factors and dust pollution to provide effective strategies for mitigating dust pollution. Dust originates from both natural sources and anthropogenic emissions, with the latter being a key focus of control and prevention efforts through emission reduction measures. Simultaneously, meteorological factors such as temperature, humidity, wind speed, and wind direction play indispensable roles in the formation, dispersion, deposition, and transport of dust particles. Anthropogenic emissions serve as the primary driving force of air pollution in port areas, while meteorological variables contribute significantly to dispersion patterns. 3.4.1 The influence of temperature and relative humidity Figure 8 (a) analyzes the influence of temperature on PM 2.5 and PM 10 . Tables 3 − 1 and 3 − 2 present the relative coefficients obtained through SPSS software. The Pearson correlation coefficients between port PM 2.5 and PM 10 concentrations and meteorological factors (temperature and humidity) were calculated, all showing statistical significance at P < 0.01. The results indicate that before 6°C, the correlation coefficients between temperature and PM 2.5 and PM 10 concentrations were 0.301 and 0.290, respectively, indicating a direct proportionality between temperature and PM 2.5 and PM 10 concentrations. After 6°C, the correlation coefficients between temperature and PM 2.5 and PM 10 concentrations were − 0.358 and − 0.368, respectively, indicating an inverse relationship between temperature and PM 2.5 and PM 10 concentrations. Table 3 − 1 Correlation analysis of PM 2.5 and PM 10 concentrations with temperature below 6°C PM 10 PM 2.5 temperature PM 10 pearson correlation 1 0.969 ** 0.290 ** Sig. (Double-tailed) 0.0 0.0 PM 2.5 pearson correlation 0.969 ** 1 0.301 ** Sig. (Double-tailed) 0.0 0.0 **. At the 0.01 significance level (two-tailed), the correlation is statistically significant. Table 3 − 2 Correlation analysis of PM 2.5 and PM 10 concentrations with temperature above 6°C PM 10 PM 2.5 temperature PM 10 pearson correlation 1 0.982 ** -0.368** Sig. (Double-tailed) 0.0 0.0 PM 2.5 pearson correlation 0.982 ** 1 -0.358** Sig. (Double-tailed) 0.0 0.0 In August, as temperatures rise, atmospheric upward motions intensify, leading to increased atmospheric convection that facilitate the dispersion and transport of PM 2.5 and PM 10 , resulting in decreased concentrations. However, in January and December (during winter and spring), despite the lower temperatures, the lack of significant convective activity suggests a more stable climatic condition due to the absence of cold air movements. Therefore, in winter, the removal of PM 2.5 and PM 10 relies on the activity of cold air masses. When temperatures rise, signaling a lack of active cold air movements, PM 2.5 and PM 10 particles remain stagnant, leading to higher concentrations. The levels of PM 2.5 and PM 10 increase with rising temperatures; conversely, a rapid temperature drop indicates stronger and more frequent cold air activity, causing an uneven distribution of air currents and resulting in decreased concentrations of PM 2.5 and PM 10 . Additionally, in a temperature inversion layer, the temperature increases with altitude, which stabilizes the air and restricts vertical movement. Normally, warm air rises while cool air descends, promoting air mixing and the dispersion of pollutants. However, the presence of an inversion layer suppresses this mixing process, making it difficult for pollutants to disperse in the lower atmosphere. When cool air is trapped beneath the inversion layer, PM 2.5 and PM 10 accumulate in the lower levels, leading to a gradual increase in their concentrations over time and a subsequent decline in air quality [‎33]. Figure 8 (b) examines the impact of relative humidity on PM 2.5 and PM 10 . Tables 4 − 1, 4 − 2, and 4 − 3 present the respective coefficients. The results indicate that before 80% humidity, the correlation coefficients between humidity and PM 2.5 and PM 10 concentrations were 0.204 and 0.212, showing a direct proportionality between relative humidity and PM 2.5 and PM 10 concentrations. Between 80% and 90% humidity, the correlation coefficients between humidity and PM 2.5 and PM 10 concentrations were − 0.067 and − 0.077, respectively, indicating an inverse relationship between humidity and PM 2.5 and PM 10 concentrations. When humidity exceeds 90%, the correlation coefficients were 0.230 and 0.239, demonstrating a direct proportionality between humidity and PM 2.5 and PM 10 concentrations. Table 4 − 1 Correlation analysis of PM 2.5 and PM 10 concentrations with humidity below 80% PM 10 PM 2.5 temperature PM 10 pearson correlation 1 0.980 ** 0.204** Sig. (Double-tailed) 0.0 0.0 PM 2.5 pearson correlation 0.980 ** 1 0.212** Sig. (Double-tailed) 0.0 0.0 Table 4 − 2 Correlation analysis of PM 2.5 and PM 10 concentrations with humidity between 80% and 90% PM 10 PM 2.5 temperature PM 10 pearson correlation 1 0.976 ** -0.067** Sig. (Double-tailed) 0.0 0.0 PM 2.5 pearson correlation 0.976 ** 1 − .077** Sig. (Double-tailed) 0.0 0.0 Table 4-3 Correlation analysis of PM 2.5 and PM 10 concentrations with humidity above 90% PM 10 PM 2.5 temperature PM 10 pearson correlation 1 0.984 ** 0.230** Sig. (Double-tailed) 0.0 0.0 PM 2.5 pearson correlation 0.984 ** 1 0.239** Sig. (Double-tailed) 0.0 0.0 This is because under low humidity conditions (below 80%), PM 2.5 and PM 10 are typically drier, allowing them to maintain smaller particle sizes, which slows their settling rate and may result in longer residence times in the air. Low humidity is often associated with clear skies and light winds, leading to the accumulation of pollutants and potentially higher concentrations. When humidity is moderate, chemical reactions become more active, which may facilitate the formation of secondary particles and further increase PM 2.5 and PM 10 concentrations. In the medium humidity range (80% − 90%), PM 2.5 and PM 10 begin to absorb moisture, forming wet particles that increase in mass and accelerate settling, often resulting in a decrease in PM 10 concentrations. Under high humidity conditions (above 90%), PM 2.5 and PM 10 are more likely to combine with water vapor, leading to the formation of haze that reduces air quality and visibility. While high humidity may promote the settling of PM 10 , it can cause smaller PM 2.5 particles to linger longer in the air. Therefore, when using water to reduce dust, it is advisable to avoid relative humidity levels around 80% or higher than 90% [‎34]. 3.4.2 The influence of wind speed and direction The influence of wind on dust primarily manifests as physical phenomena, involving the transport and scattering of dust particles [‎35]. Changes in wind speed and direction have a significant impact on the regional trend of PM concentration. Statistical analysis reveals notable distinctions in wind speed and direction between the riverside areas (C, D, E, G, I, L, M, N) and non-riverside areas (A, B, F, H, J, K, O) near the port. To delve deeper into studying the influence of wind on PM concentration at Zhenjiang Port, the PM concentration data has been segregated based on wind direction into riverside and non-riverside sections for analysis. According to the "GB/T 28591 − 2012 Wind Grade" standard released in June 2012, the China Meteorological Administration uses the wind speed at a height of 10 meters in standard meteorological observation fields as the basis for classification. Wind levels are divided into 18 grades from low to high based on this criterion. Wind Grade 0 has a speed range of 0.0 to 0.2 m s − 1 , Wind Grade 1 ranges from 0.3 to 1.5 m s − 1 , Wind Grade 2 ranges from 1.6 to 3.3 m s − 1 , Wind Grade 3 ranges from 3.4 to 5.4 m s − 1 , Wind Grade 4 ranges from 5.5 to 7.9 m s − 1 , Wind Grade 5 ranges from 8.0 to 10.7 m s − 1 , Wind Grade 6 ranges from 10.8 to 13.8 m s − 1 , and speeds exceeding 13.8 m s − 1 are classified as Grade 7 or higher. In the riverside areas, the wind speeds are higher compared to the non-riverside areas, with wind levels exceeding Grade 6 in riverside areas and exceeding Grade 4 in non-riverside areas. However, there is only one set of data for each area, rendering the results non-representative. For the riverside areas, only the impact of wind speeds from Grade 0 to Grade 5 on PM concentration is analyzed, while for the non-riverside areas, only the impact of wind speeds from Grade 0 to Grade 3 on dust concentration is examined. Based on 148,626 valid data points, an analysis of the relationship between PM concentration and wind speed was conducted, with specific results detailed in Table 5 . Table 5 Relationship between wind level and atmospheric concentration wind scale number of samples PM 2.5 (µg m − 3 ) PM 10 (µg m − 3 ) Level 0 34860 60.12 78.10 Level 1 84012 54.91 71.84 Level 2 26400 50.57 66.82 Level 3 2928 44.36 58.92 Level 4 351 43.71 57.88 Level 5 74 51.73 67.96 Level 6 1 36.35 46.16 In the research area, Wind Grades 0, 1, and 2 are the most common, with the majority of occurrences. Specifically: There are 34,860 data points for Wind Grade 0, accounting for 23.45% of the total. Wind Grade 1 has 84,012 data points, representing 56.53% of the total. Wind Grade 2 consists of 26,400 data points, making up 10.62% of the total. Wind Grade 3 comprises 2,928 data points, accounting for 1.97% of the total. Wind Grade 4 is represented by 351 data points, only constituting 0.24% of the total. Wind Grade 5 has 74 data points, making up just 0.05% of the total. Wind Grade 6 occurred only once during the period between 2019 and 2020. This distribution indicates that lower wind speeds (Grades 0, 1, and 2) are predominant in the research area, while higher wind speeds are less common, with Grade 6 being particularly rare during the specified two-year period. In the non-riverside area of the port, the prevailing wind directions are South and Southeast, accounting for 19.27% and 19.02% respectively. In the riverside area, the primary wind directions are Southeast, Northeast, and East, with proportions of 16.74%, 15.85%, and 15.85% respectively. The variation in PM concentration in the research area is influenced by wind direction disturbances, with different monitoring points exhibiting varying wind directions. By categorizing and analyzing the relationship between PM concentration and wind direction based on monitoring point locations (riverside and non-riverside), it is evident that the average concentrations corresponding to different wind directions undergo significant changes, indicating that PM concentration is to some extent influenced by wind direction. Table 6 illustrates that more severe PM pollution in the non-riverside area primarily occurs under east and southeast wind conditions. Under east wind conditions, the concentrations of PM 2.5 and PM 10 are 55.83 µg m − 3 and 73.27 µg m − 3 respectively, while under southeast wind conditions, they are 54.33 µg m − 3 and 72.12 µg m − 3 respectively. PM concentrations are lower under west wind conditions, with PM 2.5 and PM 10 concentrations at 46.46 µg m − 3 and 60.93 µg m − 3 respectively. Table 6 Relationship between wind direction and PM concentration in non-riverside areas wind direction PM 2.5 concentration (µg m − 3 ) PM 10 concentration (µg m − 3 ) north wind (N) 49.04 65.44 northeast wind (NE) 49.97 65.36 east wind (E) 55.83 73.27 southeast wind (SE) 54.33 72.12 south wind (S) 53.49 70.38 southwest wind (SW) 49.06 64.47 west wind (W) 46.46 60.93 northwest wind (NW) 48.47 63.78 Table 7 indicates that more severe PM pollution in the riverbank area primarily occurs under southeast and south wind conditions. The PM 2.5 and PM 10 concentrations under southeast wind conditions are 61.01 µg m − 3 and 78.20 µg m − 3 respectively, while under south wind conditions, they are 64.41 µg m − 3 and 82.17 µg m − 3 respectively. PM concentrations are lower under northeast wind conditions, with PM 2.5 and PM 10 concentrations at 52.36 µg m − 3 and 69.06 µg m − 3 respectively. Table 7 Relationship between wind direction and PM concentration in riverside areas wind direction PM 2.5 concentration (µg m − 3 ) PM 10 concentration (µg m − 3 ) north wind (N) 55.27 72.58 northeast wind (NE) 52.36 69.06 east wind (E) 55.52 72.18 southeast wind (SE) 61.01 78.20 south wind (S) 64.41 82.17 southwest wind (SW) 60.11 77.31 west wind (W) 58.47 76.91 northwest wind (NW) 58.15 76.16 Figure 9 shows the wind speed and direction rose diagram for the non-riverside and riverside areas of the port. From the figure, it is evident that in the non-riverside areas, the proportion of north wind is relatively low, at only 3.55%, while south wind and southeast wind have higher frequencies, reaching 19.63% and 18.53% respectively. The distribution of wind speed at Beaufort level 1 is the highest, ranging from 47.34–61.05% across all wind directions, while at Beaufort level 3, the proportion is the smallest, ranging from 0.01–1.33%. In the riverbank area, northeast wind, east wind, and southeast wind have higher frequencies, reaching 15.87%, 15.85%, and 16.75% respectively. The distribution of wind speed at Beaufort scale 1 is the highest, ranging from 47.34–61.05% across all wind directions, while at Beaufort scale 3, the proportion ranges from 23.74–57.72%. The PM 2.5 and PM 10 concentrations at Zhenjiang Port varies with wind speed, as shown in Fig. 10 . Overall, there is a trend of decreasing PM concentration followed by an increase as wind speed increases, with the concentration curve showing a "U" shape. Under low wind speed conditions, the diffusion speed of PM decreases, accelerating PM accumulation. At this point, the PM 2.5 and PM 10 concentrations are at highest, reaching 60.12 µg m − 3 and 78.10 µg m − 3 respectively. During the transition to wind speed level 3, the PM 2.5 and PM 10 concentrations both significantly decrease to 44.36 µg m − 3 and 58.92 µg m − 3 , indicating an effective PM removal influence at this wind speed. As the wind speed rises to level 4, there is a slight decrease in PM concentration, but the decrease is not significant, with both PM 2.5 and PM 10 concentrations decreasing by less than 2 µg m − 3 . When the wind speed reaches level 5, the PM 2.5 and PM 10 concentrations rise to 51.73 µg m − 3 and 67.96 µg m − 3 , respectively. The main reason for the decrease in PM concentration near the ground at lower wind speeds is that increased near-surface wind speed facilitates the dispersion and dilution of dust, leading to a reduction in dust concentration at the port. However, as wind speed continues to increase and surpass a certain threshold, higher wind speeds can lead to PM lifting from stockpiles and the ground, exacerbating PM pollution instead of reducing PM concentration in the environment. Research results also suggest that increasing wind speed may lead to a pattern of initially increasing and then decreasing PM concentrations [‎36]. 4 Conclusions Based on 148,626 hourly PM concentration data points provided by the Zhenjiang Port Authority from 2019 to 2020, this study thoroughly analyzes the spatial and temporal evolution characteristics of PM 2.5 and PM 10 concentrations at a small scale and their correlation with meteorological factors. The findings are as follows: ( 1 ) The daily average PM concentrations of port exceed standards to varying degrees. PM 2.5 meets the national level three standard in 12.31% of cases, with 1.37% exceeding this level. PM 10 exceeds the national level two standard in 1.91% of cases. Due to the smaller size of PM 2.5 , which easily adsorb harmful substances and pose greater risks than PM 10 , analyzing pollution in this small-scale area is essential. ( 2 ) The spatial distribution of PM 2.5 and PM 10 concentrations shows higher levels in the west and lower in the east. PM concentrations in the riverside areas (63.64 µg m − 3 for PM 2.5 and 82.15 µg m − 3 for PM 10 ) are higher than in non-riverside areas (48.12 µg m − 3 for PM 2.5 and 62.54 µg m − 3 for PM 10 ). ( 3 ) The monthly average concentrations of PM 2.5 and PM 10 exhibit a "U"-shaped distribution, with higher concentrations in January and December, and lower levels from July to September, reaching the lowest point in August. In Zhenjiang Port, PM 2.5 and PM 10 concentrations fluctuate more steadily during the day. At night, due to low temperatures and weak air flow dispersion, PM concentrations peak around 6:00 AM. After this, they decrease as solar radiation increases and temperatures rise, gradually recovering after 1:00 PM. PM 2.5 particles are smaller than PM 10 , and their smaller diameter and lower density lead to more collisions with gas molecules while moving through the air, resulting in a longer residence time. As night falls, although PM concentrations decrease, the PM 2.5 /PM 10 ratio increases. ( 4 ) Before the temperature reaches 6°C, the correlation coefficients between temperature and PM 2.5 and PM 10 concentrations are 0.301 and 0.290 respectively, showing an overall increasing trend in PM 2.5 and PM 10 concentrations. Above 6°C, the correlation coefficients between temperature and PM 2.5 and PM 10 concentrations become − 0.358 and − 0.368 respectively, indicating a decreasing trend. When the humidity falls below 80%, the correlation coefficients between humidity and PM 2.5 and PM 10 concentrations are 0.204 and 0.212, showing an increasing trend. When the relative humidity is between 80–90%, the correlation coefficients between temperature and PM 2.5 and PM 10 concentrations are − 0.067 and − 0.077, indicating an inverse relationship between temperature and PM 2.5 and PM 10 concentrations. At humidity levels above 90%, the correlation coefficients are 0.230 and 0.239 respectively, suggesting a direct proportionality between humidity and PM 2.5 and PM 10 concentrations. ( 5 ) The predominant wind directions in the study area are east wind, northeast wind, and southeast wind. This is also one of the main reasons for the higher PM concentrations in the western part and lower concentrations in the eastern part of the study area. When the wind speed is less than 4 levels, there is a negative correlation between wind speed and PM concentration. However, when the wind speed reaches level 5, there is a positive correlation between wind speed and PM concentration. ( 6 ) For this study area, it is essential to enhance real-time dust monitoring and research on long-term prevention strategies. Collecting and improving PM data from 2021 to 2024 will be crucial in further refining research on small-scale spatial and long-term regional spatiotemporal distribution characteristics. Additionally, conducting research on the evolution mechanisms of PM will be important for better understanding and addressing PM-related issues in the area. Declarations Data availability statements The datasets analyzed during the current study are available from the corresponding author on reasonable request. Acknowledgement The authors acknowledge the financial support of the Special Scientific Research Project of School of Emergency Management, Jiangsu University (KY-C-08, KY-D-10). CRediT taxonomy: Minxue Zheng: conceptualization, funding acquisition, supervision, writing – review & editing. Jingya Zhao: data curation, formal analysis, methodology, writing – original draft. Yutao Jiang: formal analysis. Jun Zhang: resources. Zhen Ju: resources. Feng Jia: conceptualization, funding acquisition, writing – review & editing. All authors have read and agreed to the published version of the manuscript. Conflicts of Interest: The authors declare no conflict of interest. References Zhang X. Zhenjiang Port: Building a green model of port economy and running against the trend to become a strong industrial city "acceleration". Public Investment Guide. 2020;22:3–5. (in chinese) Cao K, Zhang W, Liu S, Huang B, Huang W. Pareto law-based regional inequality analysis of PM 2.5 air pollution and economic development in China. Journal of Environmental Management. 2019;252:109635. Guan P, Wang X, Cheng S, Zhang H. 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Spatiotemporal Variations and Regional Transport of Air Pollutants in Two Urban Agglomerations in Northeast China Plain. Chinese Geographical Science. 2019;6:917–933. Cite Share Download PDF Status: Published Journal Publication published 29 Apr, 2026 Read the published version in Sustainable Environment Research → Version 1 posted Reviewers agreed at journal 30 Apr, 2025 Reviewers invited by journal 29 Apr, 2025 Editor assigned by journal 20 Mar, 2025 First submitted to journal 17 Mar, 2025 Editorial decision: Minor revision 13 Mar, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6004740","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":449922866,"identity":"9efc21fd-768b-4a56-9e8e-b4f96ea23798","order_by":0,"name":"Minxue Zheng","email":"","orcid":"","institution":"Jiangsu University","correspondingAuthor":false,"prefix":"","firstName":"Minxue","middleName":"","lastName":"Zheng","suffix":""},{"id":449922867,"identity":"10d0aaee-9ea7-4a92-a687-de6d30beeeae","order_by":1,"name":"Jingya Zhao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1klEQVRIiWNgGAWjYBACfmbmA4YfKth4QAzitEi2syUUS5zhk5Fsb0sgTovBeR6DD7wtcjYGZ84YEOmywzyGGyQbzHgMbuR8vPGGwU5Ot4GADsZmtmKDwh1pPJI3cjdbzmFINjY7QEALMzPzNgPJM8d4+G7kbpPmYTiQuI2QFjZmBvMfvG3/eRhu5DwjTgsPM4uBAW8bG4/AmTNsxGmRYGZLMJY4w8YDDGRjyzkGRPjF/vxhcFTaA6Py4Y03FXZyBLWgWslDbNQgaSFVxygYBaNgFIwIAAC9wD8W9lHp0gAAAABJRU5ErkJggg==","orcid":"","institution":"Jiangsu University","correspondingAuthor":true,"prefix":"","firstName":"Jingya","middleName":"","lastName":"Zhao","suffix":""},{"id":449922868,"identity":"3f4b01f1-73a2-47cc-bc6e-85b49e036936","order_by":2,"name":"Yutao Jiang","email":"","orcid":"","institution":"Jiangsu University","correspondingAuthor":false,"prefix":"","firstName":"Yutao","middleName":"","lastName":"Jiang","suffix":""},{"id":449922869,"identity":"d93811e2-7349-4225-9e5f-f236765ca094","order_by":3,"name":"Jun Zhang","email":"","orcid":"","institution":"Zhenjiang Port Group Co., Ltd.","correspondingAuthor":false,"prefix":"","firstName":"Jun","middleName":"","lastName":"Zhang","suffix":""},{"id":449922870,"identity":"e35b58b7-ee6f-4ff5-9242-a036592d1304","order_by":4,"name":"Zhen Ju","email":"","orcid":"","institution":"Zhenjiang Port Group Co., Ltd.","correspondingAuthor":false,"prefix":"","firstName":"Zhen","middleName":"","lastName":"Ju","suffix":""},{"id":449922871,"identity":"ee42b1a7-0b32-4964-8d80-b616122bcdd5","order_by":5,"name":"Feng Jia","email":"","orcid":"https://orcid.org/0000-0002-6843-429X","institution":"Jiangsu University","correspondingAuthor":false,"prefix":"","firstName":"Feng","middleName":"","lastName":"Jia","suffix":""}],"badges":[],"createdAt":"2025-02-11 07:30:08","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6004740/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6004740/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s42834-026-00280-6","type":"published","date":"2026-04-29T15:58:24+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":82151198,"identity":"38420412-2594-4720-bc57-19093c653f1e","added_by":"auto","created_at":"2025-05-07 07:19:45","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":155712,"visible":true,"origin":"","legend":"\u003cp\u003eThe distribution of 15 air automatic monitoring points within Zhenjiang Port. The blue area is the Yangtze River, and the yellow is the land.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6004740/v1/3728cb946fc38cb277be3b5d.png"},{"id":82153741,"identity":"689803c5-23be-48d9-aaa2-fcd3f727e3a4","added_by":"auto","created_at":"2025-05-07 07:27:45","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":105994,"visible":true,"origin":"","legend":"\u003cp\u003eDaily variations of PM\u003csub\u003e2.5\u003c/sub\u003e and PM\u003csub\u003e10\u003c/sub\u003e concentrations in Zhenjiang Port.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6004740/v1/368cff9ed26c6424e51e94da.png"},{"id":82151205,"identity":"26025f45-885d-465b-9415-381c7c8df877","added_by":"auto","created_at":"2025-05-07 07:19:45","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":207903,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial distribution of PM\u003csub\u003e2.5\u003c/sub\u003e and PM\u003csub\u003e10\u003c/sub\u003e concentrations in Zhenjiang Port.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6004740/v1/2b16416954076f40c4aca88e.png"},{"id":82151202,"identity":"d42a69b9-fb1f-4e4a-861a-2285120d7623","added_by":"auto","created_at":"2025-05-07 07:19:45","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":55043,"visible":true,"origin":"","legend":"\u003cp\u003eMonthly variation of PM\u003csub\u003e2.5\u003c/sub\u003e and PM\u003csub\u003e10 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influence of temperature and humidity\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-6004740/v1/32e224e47ada36ccc39ac255.png"},{"id":82151212,"identity":"e7cec246-2d7d-485d-b406-d5272d25ff41","added_by":"auto","created_at":"2025-05-07 07:19:45","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":138539,"visible":true,"origin":"","legend":"\u003cp\u003eRose diagram of wind speed and direction (a) non riverside area (b) riverside area\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-6004740/v1/e724d95002ffd31ccad77824.png"},{"id":82151203,"identity":"ff096d42-4c19-422a-87c6-f95711f6eff0","added_by":"auto","created_at":"2025-05-07 07:19:45","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":48636,"visible":true,"origin":"","legend":"\u003cp\u003eChange trend of atmospheric concentration wind\u003c/p\u003e","description":"","filename":"10.png","url":"https://assets-eu.researchsquare.com/files/rs-6004740/v1/033561bc95e419379e0c1c93.png"},{"id":108437813,"identity":"cee4ace4-6328-41c7-8c0f-857995783c58","added_by":"auto","created_at":"2026-05-04 16:03:46","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1719968,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6004740/v1/c8472ace-66b5-4570-b1c6-06344a19e496.pdf"}],"financialInterests":"","formattedTitle":"Small-scale spatial and temporal evolution characteristics of PM2.5 and PM10 concentrationsand the influence of meteorological factors: a case study of Zhenjiang Port, China","fulltext":[{"header":"1 Introduction","content":" \u003cp\u003eZhenjiang Port is an important port in the Yangtze River Delta region, located in the western part of Zhenjiang, Jiangsu, China. Its throughput reached 329.2\u0026nbsp;million tons in 2019, ranking in the top ten ports of China for foreign trade [1]. During the logistics processes at the port, operations such as bulk cargo handling, storage and transportation have generated a large amount of dust, which exacerbated the particulate matter (PM) pollution in the port area and surrounding regions [\u0026lrm;2,\u0026lrm;3]. Particularly with the rapid increase in the throughput of easily dust-generating bulk cargo like iron ore and coal, dust pollution has become increasingly prominent, posing greater challenges for control, and resulting in adverse health effects for workers, operational environments, and surrounding ecosystems [\u0026lrm;4-\u0026lrm;6]. Among the dust pollutants, inhalable particles with diameters\u0026thinsp;\u0026le;\u0026thinsp;10 \u0026micro;m (PM\u003csub\u003e10\u003c/sub\u003e) and lung-inhalable particles with diameters\u0026thinsp;\u0026le;\u0026thinsp;2.5 \u0026micro;m (PM\u003csub\u003e2.5\u003c/sub\u003e) are the primary components. These particles are closely associated with respiratory system diseases [\u0026lrm;7,\u0026lrm;8], ecological security [\u0026lrm;9,\u0026lrm;10] and economic losses [\u0026lrm;11-\u0026lrm;13]. The concentration levels of PM\u003csub\u003e2.5\u003c/sub\u003e and PM\u003csub\u003e10\u003c/sub\u003e are influenced by various factors, including meteorological conditions [\u0026lrm;14], industrial emissions [\u0026lrm;15], urban greenery, and road distribution [\u0026lrm;16], and exhibit significant spatial and temporal evolution characteristics.\u003c/p\u003e \u003cp\u003eOn a temporal scale, PM\u003csub\u003e2.5\u003c/sub\u003e and PM\u003csub\u003e10\u003c/sub\u003e concentrations typically exhibit periodic daily \"U-pulse\" shaped variations and monthly \"U\" shaped fluctuations [\u0026lrm;17,\u0026lrm;18] and show seasonal characteristics of higher levels in winter and spring, and lower levels in summer and autumn [\u0026lrm;19,\u0026lrm;20]. On a spatial scale, many research studies have largely focused on PM pollution in major cities or regional clusters, such as the Beijing-Tianjin-Hebei region [\u0026lrm;21], the Yangtze River Delta [\u0026lrm;22], and the southern part of Liaoning Province [\u0026lrm;23]. However, studies on the spatial and temporal evolution of PM\u003csub\u003e2.5\u003c/sub\u003e and PM\u003csub\u003e10\u003c/sub\u003e in small-scale regions and their influencing factors are still relatively limited.\u003c/p\u003e \u003cp\u003eIn recent years, research on PM pollution in ports and their surrounding cities has been increasing. For example: Ship emissions at Qingdao Port significantly contribute to the seasonal variation in PM\u003csub\u003e2.5\u003c/sub\u003e concentrations in Qingdao City, with the highest contribution in summer (13.1%) and the lowest in winter (1.5%) [\u0026lrm;24]. Shipping and port emissions in Thessaloniki Port, Greece, contribute on average 9\u0026ndash;13% to PM\u003csub\u003e2.5\u003c/sub\u003e concentrations [\u0026lrm;25]. The main sources of PM\u003csub\u003e2.5\u003c/sub\u003e in Kaohsiung Port include industrial emissions, secondary aerosols, ship and vehicle exhaust, marine spray, and soil dust [\u0026lrm;26]. However, despite being a critical port in the Yangtze River Delta region, Zhenjiang Port faces serious PM pollution issues but lacks research on the spatiotemporal variations and influencing factors. This highlights the need for further studies to understand and address the PM pollution concerns in Zhenjiang Port and its surrounding areas.\u003c/p\u003e \u003cp\u003eTherefore, the present study is for the first time utilized hourly PM\u003csub\u003e2.5\u003c/sub\u003e and PM\u003csub\u003e10\u003c/sub\u003e concentration data from 15 monitoring points within Zhenjiang Port from January 2019 to December 2020, along with concurrent meteorological data. The analysis aimed to investigate the spatiotemporal characteristics of PM within the port area and explore the impact of meteorological factors on PM concentration variations. This study seeks to provide scientific basis for the precise control of PM pollution in the region and offer insights for the study of PM evolution mechanisms on a larger scale.\u003c/p\u003e"},{"header":"2 Data and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Research Data\u003c/h2\u003e \u003cp\u003eZhenjiang Port located between 32\u0026deg;11\u0026prime;10.25\u0026Prime;N \u0026minus;\u0026thinsp;32\u0026deg;11\u0026prime;27.25\u0026Prime;N and 119\u0026deg;37\u0026prime;56.53\u0026Prime;E \u0026minus;\u0026thinsp;119\u0026deg;40\u0026prime;16.20\u0026Prime;E, facing the Yangtze River. It experiences a subtropical monsoon climate with distinct seasons, having an average annual temperature of 17.4\u0026deg;C and an annual precipitation of 1248.9 mm. The PM\u003csub\u003e2.5\u003c/sub\u003e and PM\u003csub\u003e10\u003c/sub\u003e concentration data were collected from 15 automatic air quality monitoring points within Zhenjiang Port as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and the longitudes and latitudes are listed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The hourly concentration was measured using the beta attenuation monitor [\u0026lrm;27,\u0026lrm;28] with measurement accuracy exceeds 90%. Meteorological data originated from the meteorological station at Zhenjiang Port, including temperature, relative humidity, wind speed, and wind direction, the measurement accuracy are \u0026plusmn;\u0026thinsp;0.2\u0026deg;C, \u0026plusmn; 2% RH, \u0026plusmn; (0.3\u0026thinsp;+\u0026thinsp;0.03 V) m s\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e and \u0026plusmn;\u0026thinsp;3\u0026deg;, respectively. All data underwent quality control, scrutiny for redundant or anomalous data, resulting in 148,626 valid sets of PM\u003csub\u003e2.5\u003c/sub\u003e and PM\u003csub\u003e10\u003c/sub\u003e data.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe longitudes and latitudes of 15 air automatic monitoring points\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eserial number\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ename\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003elongitude\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003elatitude\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDonggang 5 # gate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e119\u0026deg;39\u0026prime;56\u0026Prime;E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32\u0026deg;11\u0026prime;57\u0026Prime;N\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDonggang 7 # gate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e119\u0026deg;40\u0026prime;16\u0026Prime;E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32\u0026deg;12\u0026prime;09\u0026Prime;N\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDonggang 23 # approach\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e119\u0026deg;40\u0026prime;07\u0026Prime;E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32\u0026deg;12\u0026prime;24\u0026Prime;N\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDonggang 18 # approach\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e119\u0026deg;39\u0026prime;18\u0026Prime;E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32\u0026deg;12\u0026prime;13\u0026Prime;N\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDagang 23 # approach\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e119\u0026deg;40\u0026prime;51\u0026Prime;E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32\u0026deg;12\u0026prime;01\u0026Prime;N\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDagang 3 # gate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e119\u0026deg;39\u0026prime;39\u0026Prime;E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32\u0026deg;11\u0026prime;50\u0026Prime;N\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDagang basin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e119\u0026deg;39\u0026prime;17\u0026Prime;E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32\u0026deg;11\u0026prime;53\u0026Prime;N\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDagang 1 # gate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e119\u0026deg;39\u0026prime;19\u0026Prime;E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32\u0026deg;11\u0026prime;38\u0026Prime;N\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDagang 1 # berth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e119\u0026deg;39\u0026prime;04\u0026Prime;E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32\u0026deg;11\u0026prime;47\u0026Prime;N\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJingang 10 # gate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e119\u0026deg;38\u0026prime;24\u0026Prime;E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32\u0026deg;11\u0026prime;18\u0026Prime;N\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJingang Jingwu Road South\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e119\u0026deg;38\u0026prime;52\u0026Prime;E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32\u0026deg;11\u0026prime;28\u0026Prime;N\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJingang Jingwu Road North\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e119\u0026deg;38\u0026prime;47\u0026Prime;E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32\u0026deg;11\u0026prime;39\u0026Prime;N\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJingang 26 # approach\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e119\u0026deg;38\u0026prime;17\u0026Prime;E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32\u0026deg;11\u0026prime;32\u0026Prime;N\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJingang 24 # approach\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e119\u0026deg;37\u0026prime;57\u0026Prime;E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32\u0026deg;11\u0026prime;26\u0026Prime;N\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJingang 8 # gate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e119\u0026deg;38\u0026prime;02\u0026Prime;E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32\u0026deg;11\u0026prime;10\u0026Prime;N\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Research Methods\u003c/h2\u003e \u003cp\u003eThe Inverse Distance Weighting (IDW) method is employed to investigate the spatial distribution characteristics of PM\u003csub\u003e2.5\u003c/sub\u003e and PM\u003csub\u003e10\u003c/sub\u003e concentrations in the region. Utilizing data from 15 monitoring points, this method estimates data at points to be evaluated based on the spatial relationships between these points and known monitoring points. By estimating interpolation, data at points to be evaluated are obtained, allowing for the determination of the spatial distribution across the entire study area and further analysis of its spatiotemporal evolution. The principle involves weighting the average based on the distances between interpolation points and sample points, where interpolation points closer to sample points are assigned greater weights, and vice versa. The method is defined as follows [\u0026lrm;29]:\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\:{z}_{0}=\\frac{\\sum\\:_{i=1}^{n}zi{d}_{i}^{-\\beta\\:}}{\\sum\\:_{i=1}^{n}{d}_{i}^{-\\beta\\:}},i=\\text{1,2},\\dots\\:,n.$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere, \u003cem\u003ez\u003c/em\u003e\u003csub\u003e0\u003c/sub\u003e: the predicted value at the interpolation point, \u003cem\u003ez\u003c/em\u003e\u003csub\u003ei\u003c/sub\u003e: the value at the i\u003csup\u003eth\u003c/sup\u003e sample point, \u003cem\u003ed\u003c/em\u003e\u003csub\u003ei\u003c/sub\u003e: the distance from the i\u003csup\u003eth\u003c/sup\u003e sample point to the interpolation point, \u003cem\u003en\u003c/em\u003e: the number of points affect the interpolation points, \u003cem\u003eβ\u003c/em\u003e: the power of distance, typically set as 2.\u003c/p\u003e \u003cp\u003eThe Pearson correlation coefficient method was utilized to study the relationships between PM\u003csub\u003e2.5\u003c/sub\u003e and PM\u003csub\u003e10\u003c/sub\u003e concentrations and meteorological factors. The degree of linear correlation between \u003cem\u003ex\u003c/em\u003e and \u003cem\u003ey\u003c/em\u003e variables is calculated as follows [\u0026lrm;30]:\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$$\\:r=\\frac{\\sum\\:(x-\\stackrel{-}{x})(y-\\stackrel{-}{y})}{\\sqrt{\\sum\\:{(x-\\stackrel{-}{x})}^{2}\\sum\\:{(y-\\stackrel{-}{y})}^{2}}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhen 0\u0026thinsp;\u0026lt;\u0026thinsp;\u003cem\u003er\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;1, \u003cem\u003ex\u003c/em\u003e and \u003cem\u003ey\u003c/em\u003e are positively correlated; when-1\u0026thinsp;\u0026lt;\u0026thinsp;\u003cem\u003er\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0, \u003cem\u003ex\u003c/em\u003e and \u003cem\u003ey\u003c/em\u003e are negatively correlated; when |\u003cem\u003er\u003c/em\u003e| = 1, \u003cem\u003ex\u003c/em\u003e and \u003cem\u003ey\u003c/em\u003e are completely related; when |\u003cem\u003er\u003c/em\u003e| = 0, \u003cem\u003ex\u003c/em\u003e and \u003cem\u003ey\u003c/em\u003e are independent.\u003c/p\u003e \u003c/div\u003e"},{"header":"3 Results and Discussion","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e3.1 PM\u003csub\u003e2.5\u003c/sub\u003e and PM\u003csub\u003e10\u003c/sub\u003e concentrations\u003c/h2\u003e \u003cp\u003eAccording to the \u0026ldquo;Technical Regulations of Environmental Air Quality Index (AQI)\u0026rdquo;, the daily average concentrations of PM\u003csub\u003e2.5\u003c/sub\u003e and PM\u003csub\u003e10\u003c/sub\u003e are divided into six levels, as listed in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEvaluation of Air Pollution Index\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAir quality status\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAir quality level\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePM\u003csub\u003e2.5\u003c/sub\u003e concentration\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePM\u003csub\u003e10\u003c/sub\u003e concentration\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGood\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLevel 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u0026ndash;35 \u0026micro;g m\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u0026ndash;50 \u0026micro;g m\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLevel 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35\u0026ndash;75\u0026micro;g m\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e50\u0026ndash;150 \u0026micro;g m\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLightly Polluted\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLevel 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e75\u0026ndash;115 \u0026micro;g m\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e150\u0026ndash;250 \u0026micro;g m\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerately Polluted\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLevel 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e115\u0026ndash;150 \u0026micro;g m\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e250\u0026ndash;350 \u0026micro;g m\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeavily Polluted\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLevel 5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e150\u0026ndash;250 \u0026micro;g m\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e350\u0026ndash;420 \u0026micro;g m\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVery Poor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLevel 6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e250\u0026ndash;500 \u0026micro;g m\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e420\u0026ndash;600 \u0026micro;g m\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e illustrates the temporal evolution of PM\u003csub\u003e2.5\u003c/sub\u003e and PM\u003csub\u003e10\u003c/sub\u003e concentrations from January 2019 to December 2020. For PM\u003csub\u003e2.5\u003c/sub\u003e, 8.34% reach Level 1, 77.98% reach Level 2, and 13.681% reach Level 3 or higher. For PM\u003csub\u003e10\u003c/sub\u003e, 12.86% reach Level 1, 85.23% reach Level 2, and 1.91% reach Level 3 or higher. As can be seen, Zhenjiang Port experiences episodes of mild pollution. Among the pollutants, PM\u003csub\u003e2.5\u003c/sub\u003e has the greatest impact on the port and PM\u003csub\u003e2.5\u003c/sub\u003e has more hazardous than PM\u003csub\u003e10\u003c/sub\u003e due to smaller size and stronger ability to adsorb harmful substances.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Spatial distribution characteristics\u003c/h2\u003e \u003cp\u003eThe monitoring points at Zhenjiang Port are approximately 700 m apart, with high coverage density and uniform distribution. Detailed analysis of the regional distribution characteristics of port PM can be conducted using ArcGIS. Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e illustrates the spatial distribution of PM\u003csub\u003e2.5\u003c/sub\u003e and PM\u003csub\u003e10\u003c/sub\u003e concentrations at Zhenjiang Port using the IDW interpolation method. It can be seen PM\u003csub\u003e2.5\u003c/sub\u003e and PM\u003csub\u003e10\u003c/sub\u003e concentrations are higher at the riverside areas (I, L, M, N monitoring points) with average concentrations of 63.64 \u0026micro;g m\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e and 82.15 \u0026micro;g m\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e respectively, while lower at the non-riverside areas (J, K monitoring points) with average concentrations of 48.12 \u0026micro;g m\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e and 62.54 \u0026micro;g m\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e respectively. Both PM\u003csub\u003e2.5\u003c/sub\u003e and PM\u003csub\u003e10\u003c/sub\u003e concentrations exhibit a pattern of being higher in the west and lower in the east. Within the study area, the points with the highest concentrations of PM\u003csub\u003e2.5\u003c/sub\u003e and PM\u003csub\u003e10\u003c/sub\u003e are both at the N monitoring point, measuring 62.87 \u0026micro;g m\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e and 85.75 \u0026micro;g m\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e respectively, while the points with the lowest concentrations are both at the J monitoring point, measuring 40.96 \u0026micro;g m\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e and 53.47 \u0026micro;g m\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e respectively.\u003c/p\u003e \u003cp\u003eBased on the actual operational conditions of the port and its layout, it is evident that monitoring points I, L, M, and N are situated near the riverside berths and connecting bridges. These areas are bustling with activities related to bulk cargo production, loading, unloading, and transshipment. These results in the significant generation of PM from operations, roads, and vehicle exhaust particles. Particularly, the N monitoring point is positioned at the junction of the main transport routes between the bulk cargo production area and the container handling area. With more frequent operations such as cargo handling and container transport at the terminal, this area exhibits higher PM concentrations compared to others.\u003c/p\u003e \u003cp\u003eThe J and K monitoring points are adjacent, and both located beside the highways in the western and central parts of the port area. The J monitoring point is close to the office area but far from the docks and material production areas. Additionally, being situated within a green belt, where green plants can capture particles, results in lower dust concentrations at this point. On the other hand, the K monitoring point is distant from the docks and major transportation paths, with nearby storage yards implementing dust control measures like dense mesh covers. Moreover, the high level of greenery near this monitoring point contributes to relatively lower dust concentrations. In summary, the spatial distribution of PM concentrations at Zhenjiang Port is primarily influenced by factors such as dust generation from production and storage operations, road dust, vehicle emissions, and loading and unloading activities.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.3 temporal evolution characteristics\u003c/h2\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e illustrates the overall trend of PM\u003csub\u003e2.5\u003c/sub\u003e and PM\u003csub\u003e10\u003c/sub\u003e monthly average concentration values at Zhenjiang Port from 2019 to 2020, showing a \"U\" shape pattern. Specifically, PM\u003csub\u003e2.5\u003c/sub\u003e and PM\u003csub\u003e10\u003c/sub\u003e concentrations exhibited a decreasing trend from January to July, reached their lowest point in August (35.47 \u0026micro;g m\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e and 46.09 \u0026micro;g m\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e in 2019, 37.52 \u0026micro;g m\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e and 48.77 \u0026micro;g m\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e in 2020, respectively), were at the bottom of the \"U\" shape from July to September, and then increased from September to December. The concentrations in January (85.27 \u0026micro;g m\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e and 114.78 \u0026micro;g m\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e in 2019, 78.32 \u0026micro;g m\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e and 106.7 \u0026micro;g m\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e in 2020, respectively) and December (76.85 \u0026micro;g m\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e and 100.31 \u0026micro;g m\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e in 2019, 74.13 \u0026micro;g m\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e and 98.11 \u0026micro;g m\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e in 2020, respectively) were higher, representing the two ends of the \"U\" shape. In 2019, PM\u003csub\u003e2.5\u003c/sub\u003e levels in January were 2.40 times that of August, while in December, they were 2.17 times higher. For PM\u003csub\u003e10\u003c/sub\u003e, January levels were 2.49 times higher than August, and December levels were 2.18 times higher. In 2020, January PM\u003csub\u003e2.5\u003c/sub\u003e was 2.09 times that of August, December PM\u003csub\u003e2.5\u003c/sub\u003e was 1.98 times higher, January PM\u003csub\u003e10\u003c/sub\u003e was 2.19 times higher, and December PM\u003csub\u003e10\u003c/sub\u003e was 2.01 times higher than August. The distribution of monthly rainfall in Zhenjiang City is highly uneven, with the city entering the plum rain season in June and July, lasting approximately 19 days and accounting for 60% of the annual rainfall. August and September see frequent typhoons and heavy rainfall, which significantly impact PM through erosion and adsorption. These findings align closely with the trends in monthly average PM\u003csub\u003e2.5\u003c/sub\u003e and PM\u003csub\u003e10\u003c/sub\u003e concentrations in Nanjing [\u0026lrm;31].\u003c/p\u003e\u003cp\u003eAmong the 15 monitoring points, based on hourly concentration data of PM\u003csub\u003e2.5\u003c/sub\u003e and PM\u003csub\u003e10\u003c/sub\u003e, the two highest points, N and I, and the two lowest points, K and J, were selected. Taking a week with severe PM pollution over the two years as an example, the daily variation characteristics of PM\u003csub\u003e2.5\u003c/sub\u003e and PM\u003csub\u003e10\u003c/sub\u003e concentrations were analyzed. During the day when port activities are in progress, the daily variation curves of PM\u003csub\u003e2.5\u003c/sub\u003e and PM\u003csub\u003e10\u003c/sub\u003e concentrations generally align closely, exhibiting relatively stable fluctuations. With lower temperatures at night in the port area and weaker airflow dispersion, coupled with settling influences, PM concentrations gradually increase and peak around 06:00 in the morning. Subsequently, due to factors such as enhanced solar radiation and rising temperatures, PM disperses and dilutes, leading to a decrease in concentration. After 13:00, as solar radiation intensity decreases, PM concentrations gradually rise again, as shown in Figs.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e and \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eAfter nightfall, most production activities cease, leading to a decrease in both PM\u003csub\u003e2.5\u003c/sub\u003e and PM\u003csub\u003e10\u003c/sub\u003e concentrations. However, the ratio of PM\u003csub\u003e2.5\u003c/sub\u003e/PM\u003csub\u003e10\u003c/sub\u003e shows an increase rather than a decrease (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). This phenomenon is attributed to the fact that PM\u003csub\u003e2.5\u003c/sub\u003e have smaller diameter compared to PM\u003csub\u003e10\u003c/sub\u003e, resulting in more collisions with gas molecules during movement in the air, prolonging their residence time. Additionally, sunlight can enhance the oxidation state of chromophores in atmospheric PM\u003csub\u003e2.5\u003c/sub\u003e, affecting their photochemical reactivity [\u0026lrm;32]. At the heavily polluted N and I riverside areas, PM\u003csub\u003e2.5\u003c/sub\u003e proportions are not high, whereas at non-riverside areas K and J, PM\u003csub\u003e2.5\u003c/sub\u003e proportions are higher, indicating a significant influence of PM\u003csub\u003e2.5\u003c/sub\u003e on pollution levels. Additionally, due to its slower settling rate and lesser impact from PM dispersion, PM\u003csub\u003e2.5\u003c/sub\u003e also experiences prolonged duration within the aerosol particles.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e\u003cb\u003e3.4 The influence of meteorological factors\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eIn environments with relatively stable dust pollution emissions, meteorological factors play a crucial role in influencing the emission, dispersion, transport, and deposition of dust. This study collects and organizes dust concentration data and related meteorological data (including temperature, humidity, wind speed, and wind direction) from various monitoring points. Using Pearson correlation analysis in SPSS 25.0 software, it investigates the impact of meteorological factors on dust, delving into the correlation between meteorological factors and dust pollution to provide effective strategies for mitigating dust pollution. Dust originates from both natural sources and anthropogenic emissions, with the latter being a key focus of control and prevention efforts through emission reduction measures. Simultaneously, meteorological factors such as temperature, humidity, wind speed, and wind direction play indispensable roles in the formation, dispersion, deposition, and transport of dust particles. Anthropogenic emissions serve as the primary driving force of air pollution in port areas, while meteorological variables contribute significantly to dispersion patterns.\u003c/p\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e3.4.1 The influence of temperature and relative humidity\u003c/h2\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e(a) analyzes the influence of temperature on PM\u003csub\u003e2.5\u003c/sub\u003e and PM\u003csub\u003e10\u003c/sub\u003e. Tables\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e3\u003c/span\u003e\u0026thinsp;\u0026minus;\u0026thinsp;1 and 3\u0026thinsp;\u0026minus;\u0026thinsp;2 present the relative coefficients obtained through SPSS software. The Pearson correlation coefficients between port PM\u003csub\u003e2.5\u003c/sub\u003e and PM\u003csub\u003e10\u003c/sub\u003e concentrations and meteorological factors (temperature and humidity) were calculated, all showing statistical significance at \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01. The results indicate that before 6\u0026deg;C, the correlation coefficients between temperature and PM\u003csub\u003e2.5\u003c/sub\u003e and PM\u003csub\u003e10\u003c/sub\u003e concentrations were 0.301 and 0.290, respectively, indicating a direct proportionality between temperature and PM\u003csub\u003e2.5\u003c/sub\u003e and PM\u003csub\u003e10\u003c/sub\u003e concentrations. After 6\u0026deg;C, the correlation coefficients between temperature and PM\u003csub\u003e2.5\u003c/sub\u003e and PM\u003csub\u003e10\u003c/sub\u003e concentrations were \u0026minus;\u0026thinsp;0.358 and \u0026minus;\u0026thinsp;0.368, respectively, indicating an inverse relationship between temperature and PM\u003csub\u003e2.5\u003c/sub\u003e and PM\u003csub\u003e10\u003c/sub\u003e concentrations.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u0026thinsp;\u003cb\u003e\u0026minus;\u0026thinsp;1\u003c/b\u003e Correlation analysis of PM\u003csub\u003e2.5\u003c/sub\u003e and PM\u003csub\u003e10\u003c/sub\u003e concentrations with temperature below 6\u0026deg;C\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePM\u003csub\u003e10\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePM\u003csub\u003e2.5\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003etemperature\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePM\u003csub\u003e10\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003epearson correlation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.969\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.290\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSig. (Double-tailed)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePM\u003csub\u003e2.5\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003epearson correlation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.969\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.301\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSig. (Double-tailed)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e**. At the 0.01 significance level (two-tailed), the correlation is statistically significant.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u0026thinsp;\u003cb\u003e\u0026minus;\u0026thinsp;2\u003c/b\u003e Correlation analysis of PM\u003csub\u003e2.5\u003c/sub\u003e and PM\u003csub\u003e10\u003c/sub\u003e concentrations with temperature above 6\u0026deg;C\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePM\u003csub\u003e10\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePM\u003csub\u003e2.5\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003etemperature\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePM\u003csub\u003e10\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003epearson correlation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.982\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.368**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSig. (Double-tailed)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePM\u003csub\u003e2.5\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003epearson correlation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.982\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.358**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSig. (Double-tailed)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn August, as temperatures rise, atmospheric upward motions intensify, leading to increased atmospheric convection that facilitate the dispersion and transport of PM\u003csub\u003e2.5\u003c/sub\u003e and PM\u003csub\u003e10\u003c/sub\u003e, resulting in decreased concentrations. However, in January and December (during winter and spring), despite the lower temperatures, the lack of significant convective activity suggests a more stable climatic condition due to the absence of cold air movements. Therefore, in winter, the removal of PM\u003csub\u003e2.5\u003c/sub\u003e and PM\u003csub\u003e10\u003c/sub\u003e relies on the activity of cold air masses. When temperatures rise, signaling a lack of active cold air movements, PM\u003csub\u003e2.5\u003c/sub\u003e and PM\u003csub\u003e10\u003c/sub\u003e particles remain stagnant, leading to higher concentrations. The levels of PM\u003csub\u003e2.5\u003c/sub\u003e and PM\u003csub\u003e10\u003c/sub\u003e increase with rising temperatures; conversely, a rapid temperature drop indicates stronger and more frequent cold air activity, causing an uneven distribution of air currents and resulting in decreased concentrations of PM\u003csub\u003e2.5\u003c/sub\u003e and PM\u003csub\u003e10\u003c/sub\u003e.\u003c/p\u003e \u003cp\u003eAdditionally, in a temperature inversion layer, the temperature increases with altitude, which stabilizes the air and restricts vertical movement. Normally, warm air rises while cool air descends, promoting air mixing and the dispersion of pollutants. However, the presence of an inversion layer suppresses this mixing process, making it difficult for pollutants to disperse in the lower atmosphere. When cool air is trapped beneath the inversion layer, PM\u003csub\u003e2.5\u003c/sub\u003e and PM\u003csub\u003e10\u003c/sub\u003e accumulate in the lower levels, leading to a gradual increase in their concentrations over time and a subsequent decline in air quality [\u0026lrm;33].\u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e(b) examines the impact of relative humidity on PM\u003csub\u003e2.5\u003c/sub\u003e and PM\u003csub\u003e10\u003c/sub\u003e. Tables\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e4\u003c/span\u003e\u0026thinsp;\u0026minus;\u0026thinsp;1, 4\u0026thinsp;\u0026minus;\u0026thinsp;2, and 4\u0026thinsp;\u0026minus;\u0026thinsp;3 present the respective coefficients. The results indicate that before 80% humidity, the correlation coefficients between humidity and PM\u003csub\u003e2.5\u003c/sub\u003e and PM\u003csub\u003e10\u003c/sub\u003e concentrations were 0.204 and 0.212, showing a direct proportionality between relative humidity and PM\u003csub\u003e2.5\u003c/sub\u003e and PM\u003csub\u003e10\u003c/sub\u003e concentrations. Between 80% and 90% humidity, the correlation coefficients between humidity and PM\u003csub\u003e2.5\u003c/sub\u003e and PM\u003csub\u003e10\u003c/sub\u003e concentrations were \u0026minus;\u0026thinsp;0.067 and \u0026minus;\u0026thinsp;0.077, respectively, indicating an inverse relationship between humidity and PM\u003csub\u003e2.5\u003c/sub\u003e and PM\u003csub\u003e10\u003c/sub\u003e concentrations. When humidity exceeds 90%, the correlation coefficients were 0.230 and 0.239, demonstrating a direct proportionality between humidity and PM\u003csub\u003e2.5\u003c/sub\u003e and PM\u003csub\u003e10\u003c/sub\u003e concentrations.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u0026thinsp;\u003cb\u003e\u0026minus;\u0026thinsp;1\u003c/b\u003e Correlation analysis of PM\u003csub\u003e2.5\u003c/sub\u003e and PM\u003csub\u003e10\u003c/sub\u003e concentrations with humidity below 80%\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePM\u003csub\u003e10\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePM\u003csub\u003e2.5\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003etemperature\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePM\u003csub\u003e10\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003epearson correlation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.980\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.204**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSig. (Double-tailed)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePM\u003csub\u003e2.5\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003epearson correlation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.980\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.212**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSig. (Double-tailed)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u0026thinsp;\u003cb\u003e\u0026minus;\u0026thinsp;2\u003c/b\u003e Correlation analysis of PM\u003csub\u003e2.5\u003c/sub\u003e and PM\u003csub\u003e10\u003c/sub\u003e concentrations with humidity between 80% and 90%\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePM\u003csub\u003e10\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePM\u003csub\u003e2.5\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003etemperature\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePM\u003csub\u003e10\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003epearson correlation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.976\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.067**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSig. (Double-tailed)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePM\u003csub\u003e2.5\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003epearson correlation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.976\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.077**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSig. (Double-tailed)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e\u003cstrong\u003eTable 4-3\u003c/strong\u003e Correlation analysis of PM\u003csub\u003e2.5\u003c/sub\u003e and PM\u003csub\u003e10\u003c/sub\u003e concentrations with humidity above 90%\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 200px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003ePM\u003csub\u003e10\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003ePM\u003csub\u003e2.5\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003etemperature\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003ePM\u003csub\u003e10\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 200px;\"\u003e\n \u003cp\u003epearson correlation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e0.984\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e0.230**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 200px;\"\u003e\n \u003cp\u003eSig. (Double-tailed)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003ePM\u003csub\u003e2.5\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 200px;\"\u003e\n \u003cp\u003epearson correlation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e0.984\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e0.239**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 200px;\"\u003e\n \u003cp\u003eSig. (Double-tailed)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eThis is because under low humidity conditions (below 80%), PM\u003csub\u003e2.5\u003c/sub\u003e and PM\u003csub\u003e10\u003c/sub\u003e are typically drier, allowing them to maintain smaller particle sizes, which slows their settling rate and may result in longer residence times in the air. Low humidity is often associated with clear skies and light winds, leading to the accumulation of pollutants and potentially higher concentrations.\u003c/p\u003e \u003cp\u003eWhen humidity is moderate, chemical reactions become more active, which may facilitate the formation of secondary particles and further increase PM\u003csub\u003e2.5\u003c/sub\u003e and PM\u003csub\u003e10\u003c/sub\u003e concentrations. In the medium humidity range (80% \u0026minus;\u0026thinsp;90%), PM\u003csub\u003e2.5\u003c/sub\u003e and PM\u003csub\u003e10\u003c/sub\u003e begin to absorb moisture, forming wet particles that increase in mass and accelerate settling, often resulting in a decrease in PM\u003csub\u003e10\u003c/sub\u003e concentrations. Under high humidity conditions (above 90%), PM\u003csub\u003e2.5\u003c/sub\u003e and PM\u003csub\u003e10\u003c/sub\u003e are more likely to combine with water vapor, leading to the formation of haze that reduces air quality and visibility. While high humidity may promote the settling of PM\u003csub\u003e10\u003c/sub\u003e, it can cause smaller PM\u003csub\u003e2.5\u003c/sub\u003e particles to linger longer in the air. Therefore, when using water to reduce dust, it is advisable to avoid relative humidity levels around 80% or higher than 90% [\u0026lrm;34].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e3.4.2 The influence of wind speed and direction\u003c/h2\u003e \u003cp\u003eThe influence of wind on dust primarily manifests as physical phenomena, involving the transport and scattering of dust particles [\u0026lrm;35]. Changes in wind speed and direction have a significant impact on the regional trend of PM concentration. Statistical analysis reveals notable distinctions in wind speed and direction between the riverside areas (C, D, E, G, I, L, M, N) and non-riverside areas (A, B, F, H, J, K, O) near the port. To delve deeper into studying the influence of wind on PM concentration at Zhenjiang Port, the PM concentration data has been segregated based on wind direction into riverside and non-riverside sections for analysis.\u003c/p\u003e \u003cp\u003eAccording to the \"GB/T 28591\u0026thinsp;\u0026minus;\u0026thinsp;2012 Wind Grade\" standard released in June 2012, the China Meteorological Administration uses the wind speed at a height of 10 meters in standard meteorological observation fields as the basis for classification. Wind levels are divided into 18 grades from low to high based on this criterion. Wind Grade 0 has a speed range of 0.0 to 0.2 m s\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, Wind Grade 1 ranges from 0.3 to 1.5 m s\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, Wind Grade 2 ranges from 1.6 to 3.3 m s\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, Wind Grade 3 ranges from 3.4 to 5.4 m s\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, Wind Grade 4 ranges from 5.5 to 7.9 m s\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, Wind Grade 5 ranges from 8.0 to 10.7 m s\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, Wind Grade 6 ranges from 10.8 to 13.8 m s\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, and speeds exceeding 13.8 m s\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e are classified as Grade 7 or higher. In the riverside areas, the wind speeds are higher compared to the non-riverside areas, with wind levels exceeding Grade 6 in riverside areas and exceeding Grade 4 in non-riverside areas. However, there is only one set of data for each area, rendering the results non-representative. For the riverside areas, only the impact of wind speeds from Grade 0 to Grade 5 on PM concentration is analyzed, while for the non-riverside areas, only the impact of wind speeds from Grade 0 to Grade 3 on dust concentration is examined. Based on 148,626 valid data points, an analysis of the relationship between PM concentration and wind speed was conducted, with specific results detailed in Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRelationship between wind level and atmospheric concentration\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ewind scale\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003enumber of samples\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePM\u003csub\u003e2.5\u003c/sub\u003e(\u0026micro;g m\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePM\u003csub\u003e10\u003c/sub\u003e(\u0026micro;g m\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLevel 0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e34860\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e60.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e78.10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLevel 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e84012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e54.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e71.84\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLevel 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e26400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e50.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e66.82\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLevel 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2928\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e44.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e58.92\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLevel 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e351\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e43.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e57.88\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLevel 5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e51.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e67.96\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLevel 6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e36.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e46.16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn the research area, Wind Grades 0, 1, and 2 are the most common, with the majority of occurrences. Specifically: There are 34,860 data points for Wind Grade 0, accounting for 23.45% of the total. Wind Grade 1 has 84,012 data points, representing 56.53% of the total. Wind Grade 2 consists of 26,400 data points, making up 10.62% of the total. Wind Grade 3 comprises 2,928 data points, accounting for 1.97% of the total. Wind Grade 4 is represented by 351 data points, only constituting 0.24% of the total. Wind Grade 5 has 74 data points, making up just 0.05% of the total. Wind Grade 6 occurred only once during the period between 2019 and 2020. This distribution indicates that lower wind speeds (Grades 0, 1, and 2) are predominant in the research area, while higher wind speeds are less common, with Grade 6 being particularly rare during the specified two-year period.\u003c/p\u003e \u003cp\u003eIn the non-riverside area of the port, the prevailing wind directions are South and Southeast, accounting for 19.27% and 19.02% respectively. In the riverside area, the primary wind directions are Southeast, Northeast, and East, with proportions of 16.74%, 15.85%, and 15.85% respectively.\u003c/p\u003e \u003cp\u003eThe variation in PM concentration in the research area is influenced by wind direction disturbances, with different monitoring points exhibiting varying wind directions. By categorizing and analyzing the relationship between PM concentration and wind direction based on monitoring point locations (riverside and non-riverside), it is evident that the average concentrations corresponding to different wind directions undergo significant changes, indicating that PM concentration is to some extent influenced by wind direction. Table\u0026nbsp;\u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e6\u003c/span\u003e illustrates that more severe PM pollution in the non-riverside area primarily occurs under east and southeast wind conditions. Under east wind conditions, the concentrations of PM\u003csub\u003e2.5\u003c/sub\u003e and PM\u003csub\u003e10\u003c/sub\u003e are 55.83 \u0026micro;g m\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e and 73.27 \u0026micro;g m\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e respectively, while under southeast wind conditions, they are 54.33 \u0026micro;g m\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e and 72.12 \u0026micro;g m\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e respectively. PM concentrations are lower under west wind conditions, with PM\u003csub\u003e2.5\u003c/sub\u003e and PM\u003csub\u003e10\u003c/sub\u003e concentrations at 46.46 \u0026micro;g m\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e and 60.93 \u0026micro;g m\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e respectively.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab9\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRelationship between wind direction and PM concentration in non-riverside areas\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ewind direction\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePM\u003csub\u003e2.5\u003c/sub\u003e concentration (\u0026micro;g m\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePM\u003csub\u003e10\u003c/sub\u003e concentration (\u0026micro;g m\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003enorth wind (N)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e49.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e65.44\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003enortheast wind (NE)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e49.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e65.36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eeast wind (E)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e55.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e73.27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003esoutheast wind (SE)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e54.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e72.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003esouth wind (S)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e53.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e70.38\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003esouthwest wind (SW)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e49.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e64.47\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ewest wind (W)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e46.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e60.93\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003enorthwest wind (NW)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e48.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e63.78\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab10\" class=\"InternalRef\"\u003e7\u003c/span\u003e indicates that more severe PM pollution in the riverbank area primarily occurs under southeast and south wind conditions. The PM\u003csub\u003e2.5\u003c/sub\u003e and PM\u003csub\u003e10\u003c/sub\u003e concentrations under southeast wind conditions are 61.01 \u0026micro;g m\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e and 78.20 \u0026micro;g m\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e respectively, while under south wind conditions, they are 64.41 \u0026micro;g m\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e and 82.17 \u0026micro;g m\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e respectively. PM concentrations are lower under northeast wind conditions, with PM\u003csub\u003e2.5\u003c/sub\u003e and PM\u003csub\u003e10\u003c/sub\u003e concentrations at 52.36 \u0026micro;g m\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e and 69.06 \u0026micro;g m\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e respectively.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab10\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRelationship between wind direction and PM concentration in riverside areas\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ewind direction\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePM\u003csub\u003e2.5\u003c/sub\u003e concentration (\u0026micro;g m\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePM\u003csub\u003e10\u003c/sub\u003e concentration (\u0026micro;g m\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003enorth wind (N)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e55.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e72.58\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003enortheast wind (NE)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e52.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e69.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eeast wind (E)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e55.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e72.18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003esoutheast wind (SE)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e61.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e78.20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003esouth wind (S)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e64.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e82.17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003esouthwest wind (SW)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e60.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e77.31\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ewest wind (W)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e58.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e76.91\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003enorthwest wind (NW)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e58.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e76.16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e shows the wind speed and direction rose diagram for the non-riverside and riverside areas of the port. From the figure, it is evident that in the non-riverside areas, the proportion of north wind is relatively low, at only 3.55%, while south wind and southeast wind have higher frequencies, reaching 19.63% and 18.53% respectively. The distribution of wind speed at Beaufort level 1 is the highest, ranging from 47.34\u0026ndash;61.05% across all wind directions, while at Beaufort level 3, the proportion is the smallest, ranging from 0.01\u0026ndash;1.33%. In the riverbank area, northeast wind, east wind, and southeast wind have higher frequencies, reaching 15.87%, 15.85%, and 16.75% respectively. The distribution of wind speed at Beaufort scale 1 is the highest, ranging from 47.34\u0026ndash;61.05% across all wind directions, while at Beaufort scale 3, the proportion ranges from 23.74\u0026ndash;57.72%.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe PM\u003csub\u003e2.5\u003c/sub\u003e and PM\u003csub\u003e10\u003c/sub\u003e concentrations at Zhenjiang Port varies with wind speed, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e. Overall, there is a trend of decreasing PM concentration followed by an increase as wind speed increases, with the concentration curve showing a \"U\" shape. Under low wind speed conditions, the diffusion speed of PM decreases, accelerating PM accumulation. At this point, the PM\u003csub\u003e2.5\u003c/sub\u003e and PM\u003csub\u003e10\u003c/sub\u003e concentrations are at highest, reaching 60.12 \u0026micro;g m\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e and 78.10 \u0026micro;g m\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e respectively. During the transition to wind speed level 3, the PM\u003csub\u003e2.5\u003c/sub\u003e and PM\u003csub\u003e10\u003c/sub\u003e concentrations both significantly decrease to 44.36 \u0026micro;g m\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e and 58.92 \u0026micro;g m\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e, indicating an effective PM removal influence at this wind speed. As the wind speed rises to level 4, there is a slight decrease in PM concentration, but the decrease is not significant, with both PM\u003csub\u003e2.5\u003c/sub\u003e and PM\u003csub\u003e10\u003c/sub\u003e concentrations decreasing by less than 2 \u0026micro;g m\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e. When the wind speed reaches level 5, the PM\u003csub\u003e2.5\u003c/sub\u003e and PM\u003csub\u003e10\u003c/sub\u003e concentrations rise to 51.73 \u0026micro;g m\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e and 67.96 \u0026micro;g m\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e, respectively. The main reason for the decrease in PM concentration near the ground at lower wind speeds is that increased near-surface wind speed facilitates the dispersion and dilution of dust, leading to a reduction in dust concentration at the port. However, as wind speed continues to increase and surpass a certain threshold, higher wind speeds can lead to PM lifting from stockpiles and the ground, exacerbating PM pollution instead of reducing PM concentration in the environment. Research results also suggest that increasing wind speed may lead to a pattern of initially increasing and then decreasing PM concentrations [\u0026lrm;36].\u003c/p\u003e\u003c/div\u003e \u003c/div\u003e"},{"header":"4 Conclusions","content":"\u003cp\u003eBased on 148,626 hourly PM concentration data points provided by the Zhenjiang Port Authority from 2019 to 2020, this study thoroughly analyzes the spatial and temporal evolution characteristics of PM\u003csub\u003e2.5\u003c/sub\u003e and PM\u003csub\u003e10\u003c/sub\u003e concentrations at a small scale and their correlation with meteorological factors. The findings are as follows:\u003c/p\u003e \u003cp\u003e(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) The daily average PM concentrations of port exceed standards to varying degrees. PM\u003csub\u003e2.5\u003c/sub\u003e meets the national level three standard in 12.31% of cases, with 1.37% exceeding this level. PM\u003csub\u003e10\u003c/sub\u003e exceeds the national level two standard in 1.91% of cases. Due to the smaller size of PM\u003csub\u003e2.5\u003c/sub\u003e, which easily adsorb harmful substances and pose greater risks than PM\u003csub\u003e10\u003c/sub\u003e, analyzing pollution in this small-scale area is essential.\u003c/p\u003e \u003cp\u003e(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) The spatial distribution of PM\u003csub\u003e2.5\u003c/sub\u003e and PM\u003csub\u003e10\u003c/sub\u003e concentrations shows higher levels in the west and lower in the east. PM concentrations in the riverside areas (63.64 \u0026micro;g m\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e for PM\u003csub\u003e2.5\u003c/sub\u003e and 82.15 \u0026micro;g m\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e for PM\u003csub\u003e10\u003c/sub\u003e) are higher than in non-riverside areas (48.12 \u0026micro;g m\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e for PM\u003csub\u003e2.5\u003c/sub\u003e and 62.54 \u0026micro;g m\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e for PM\u003csub\u003e10\u003c/sub\u003e).\u003c/p\u003e \u003cp\u003e(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) The monthly average concentrations of PM\u003csub\u003e2.5\u003c/sub\u003e and PM\u003csub\u003e10\u003c/sub\u003e exhibit a \"U\"-shaped distribution, with higher concentrations in January and December, and lower levels from July to September, reaching the lowest point in August. In Zhenjiang Port, PM\u003csub\u003e2.5\u003c/sub\u003e and PM\u003csub\u003e10\u003c/sub\u003e concentrations fluctuate more steadily during the day. At night, due to low temperatures and weak air flow dispersion, PM concentrations peak around 6:00 AM. After this, they decrease as solar radiation increases and temperatures rise, gradually recovering after 1:00 PM. PM\u003csub\u003e2.5\u003c/sub\u003e particles are smaller than PM\u003csub\u003e10\u003c/sub\u003e, and their smaller diameter and lower density lead to more collisions with gas molecules while moving through the air, resulting in a longer residence time. As night falls, although PM concentrations decrease, the PM\u003csub\u003e2.5\u003c/sub\u003e/PM\u003csub\u003e10\u003c/sub\u003e ratio increases.\u003c/p\u003e \u003cp\u003e(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) Before the temperature reaches 6\u0026deg;C, the correlation coefficients between temperature and PM\u003csub\u003e2.5\u003c/sub\u003e and PM\u003csub\u003e10\u003c/sub\u003e concentrations are 0.301 and 0.290 respectively, showing an overall increasing trend in PM\u003csub\u003e2.5\u003c/sub\u003e and PM\u003csub\u003e10\u003c/sub\u003e concentrations. Above 6\u0026deg;C, the correlation coefficients between temperature and PM\u003csub\u003e2.5\u003c/sub\u003e and PM\u003csub\u003e10\u003c/sub\u003e concentrations become \u0026minus;\u0026thinsp;0.358 and \u0026minus;\u0026thinsp;0.368 respectively, indicating a decreasing trend. When the humidity falls below 80%, the correlation coefficients between humidity and PM\u003csub\u003e2.5\u003c/sub\u003e and PM\u003csub\u003e10\u003c/sub\u003e concentrations are 0.204 and 0.212, showing an increasing trend. When the relative humidity is between 80\u0026ndash;90%, the correlation coefficients between temperature and PM\u003csub\u003e2.5\u003c/sub\u003e and PM\u003csub\u003e10\u003c/sub\u003e concentrations are \u0026minus;\u0026thinsp;0.067 and \u0026minus;\u0026thinsp;0.077, indicating an inverse relationship between temperature and PM\u003csub\u003e2.5\u003c/sub\u003e and PM\u003csub\u003e10\u003c/sub\u003e concentrations. At humidity levels above 90%, the correlation coefficients are 0.230 and 0.239 respectively, suggesting a direct proportionality between humidity and PM\u003csub\u003e2.5\u003c/sub\u003e and PM\u003csub\u003e10\u003c/sub\u003e concentrations.\u003c/p\u003e \u003cp\u003e(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e) The predominant wind directions in the study area are east wind, northeast wind, and southeast wind. This is also one of the main reasons for the higher PM concentrations in the western part and lower concentrations in the eastern part of the study area. When the wind speed is less than 4 levels, there is a negative correlation between wind speed and PM concentration. However, when the wind speed reaches level 5, there is a positive correlation between wind speed and PM concentration.\u003c/p\u003e \u003cp\u003e(\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e) For this study area, it is essential to enhance real-time dust monitoring and research on long-term prevention strategies. Collecting and improving PM data from 2021 to 2024 will be crucial in further refining research on small-scale spatial and long-term regional spatiotemporal distribution characteristics. Additionally, conducting research on the evolution mechanisms of PM will be important for better understanding and addressing PM-related issues in the area.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability statements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors acknowledge the financial support of the Special Scientific Research Project of School of Emergency Management, Jiangsu University (KY-C-08, KY-D-10).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCRediT taxonomy:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMinxue Zheng: conceptualization, funding acquisition, supervision, writing \u0026ndash; review \u0026amp; editing. Jingya Zhao: data curation, formal analysis, methodology, writing \u0026ndash; original draft. Yutao Jiang: formal analysis. Jun Zhang: resources. Zhen Ju: resources. Feng Jia: conceptualization, funding acquisition, writing \u0026ndash; review \u0026amp; editing.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAll authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of Interest:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflict of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eZhang X. Zhenjiang Port: Building a green model of port economy and running against the trend to become a strong industrial city \"acceleration\". Public Investment Guide. 2020;22:3\u0026ndash;5. (in chinese)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCao K, Zhang W, Liu S, Huang B, Huang W. 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Analysis of Winter Air PM\u003csub\u003e10\u003c/sub\u003e Transport Paths and Transport Contributions in the Central Liaoning Urban Agglomeration. Environmental Ecology. 2021;7:84\u0026ndash;88. (in chinese)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen D, Wang X, Nelson P. Ship emission inventory and its impact on the PM\u003csub\u003e2.5\u003c/sub\u003e air pollution in Qingdao Port, North China. Atmospheric Environment. 2017;166:351\u0026ndash;361.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSaraga D, Tolis E, Maggos T. PM\u003csub\u003e2.5\u003c/sub\u003e source apportionment for the port city of Thessaloniki, Greece. Science of the Total Environment. 2019;650:2337\u0026ndash;2354.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYuan C, Wong K, Tseng Y. Chemical significance and source apportionment of fine particles (PM\u003csub\u003e2.5\u003c/sub\u003e) in an industrial port area in East Asia. 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Chinese Geographical Science. 2019;6:917\u0026ndash;933.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"sustainable-environment-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"sere","sideBox":"Learn more about [Sustainable Environment Research](https://sustainenvironres.biomedcentral.com)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/sere/default.aspx","title":"Sustainable Environment Research","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"PM2.5 and PM10, Zhenjiang Port, spatial and temporal evolution characteristics, meteorological factors, Inverse Distance Weighting (IDW)","lastPublishedDoi":"10.21203/rs.3.rs-6004740/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6004740/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe analysis of the small-scale spatial and temporal evolution characteristics of particulate matter (PM), especially Particulate Matter 2.5 (PM\u003csub\u003e2.5\u003c/sub\u003e) and Particulate Matter 10 (PM\u003csub\u003e10\u003c/sub\u003e), and the influence of meteorological factors is crucial for understanding the mechanisms of large-scale PM formation and transport, and further to develop precise dust pollution prevention measures. In this study, Zhenjiang Port was analyzed first time, which was based on hourly data of PM\u003csub\u003e2.5\u003c/sub\u003e and PM\u003csub\u003e10\u003c/sub\u003e concentrations from 15 air quality monitoring stations from January 2019 to December 2020. With respect to evolution characteristics of PM\u003csub\u003e2.5\u003c/sub\u003e and PM\u003csub\u003e10\u003c/sub\u003e concentrations within the study area, the results showed that: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) Daily averages exhibit varying degrees of exceedances, with PM\u003csub\u003e2.5\u003c/sub\u003e exceeding Level 3 standards accounting for 13.68%. (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) Spatially, both PM\u003csub\u003e2.5\u003c/sub\u003e and PM\u003csub\u003e10\u003c/sub\u003e display a pattern of higher concentrations in the western areas and lower concentrations in the eastern areas. (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) Diurnal fluctuations are relatively stable during the day, with peak values occurring around 06:00 in the morning. (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) PM\u003csub\u003e2.5\u003c/sub\u003e has a long retention time in the air with hysteresis. (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e) For the relationship of concentration and temperature, it shows positive correlation for below 6\u0026deg;C while negative correlation for above 6\u0026deg;C. (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e) For the relationship of concentration and relative humidity, it shows positive correlation for below 80% and above 90%, while negative correlation for at 80\u0026ndash;90%. The research findings can provide a theoretical basis for formulating atmospheric pollution prevention and control measures for Zhenjiang Port, while also serving as a reference for studying the evolution mechanisms of PM in Zhenjiang City and even larger-scale regions.\u003c/p\u003e","manuscriptTitle":"Small-scale spatial and temporal evolution characteristics of PM2.5 and PM10 concentrationsand the influence of meteorological factors: a case study of Zhenjiang Port, China","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-07 07:19:40","doi":"10.21203/rs.3.rs-6004740/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2025-04-30T05:19:30+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-04-30T00:20:46+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-03-20T09:16:01+00:00","index":"","fulltext":""},{"type":"submitted","content":"Sustainable Environment Research","date":"2025-03-18T02:53:02+00:00","index":"","fulltext":""},{"type":"decision","content":"Minor revision","date":"2025-03-14T02:27:52+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"sustainable-environment-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"sere","sideBox":"Learn more about [Sustainable Environment Research](https://sustainenvironres.biomedcentral.com)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/sere/default.aspx","title":"Sustainable Environment Research","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"34656185-b3b9-4aa8-b787-2ea9ef6fce5b","owner":[],"postedDate":"May 7th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-05-04T16:02:58+00:00","versionOfRecord":{"articleIdentity":"rs-6004740","link":"https://doi.org/10.1186/s42834-026-00280-6","journal":{"identity":"sustainable-environment-research","isVorOnly":false,"title":"Sustainable Environment Research"},"publishedOn":"2026-04-29 15:58:24","publishedOnDateReadable":"April 29th, 2026"},"versionCreatedAt":"2025-05-07 07:19:40","video":"","vorDoi":"10.1186/s42834-026-00280-6","vorDoiUrl":"https://doi.org/10.1186/s42834-026-00280-6","workflowStages":[]},"version":"v1","identity":"rs-6004740","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6004740","identity":"rs-6004740","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}