The Impact of Meteorological Variables on Particulate Matter Concentrations

Abstract

This study assessed the influence of meteorological conditions on particulate matter (PM) concentrations in Campo Grande, Brazil, from May to December 2021. Using statistical analyses, including Pearson’s correlation coefficient and multivariate regression, we analyzed secondary data on PM2.5 and PM10 concentrations and meteorological variables from the Federal University of Mato Grosso do Sul’s Physics Department. Daily PM concentrations complied with Brazil’s National Ambient Air Quality Standards (PQAr). The PM2.5/PM10 ratios averaged 0.436 (hourly) and 0.442 (daily), indicating a mix of fine and coarse particles. Significant positive correlations were found with temperature, while relative humidity showed a negative correlation, reducing PM levels through deposition. Wind speed had no significant impact. Meteorological influences suggest that air quality management should be tailored to regional conditions, particularly addressing local emission sources like vehicular traffic and biomass burning.

1. Introduction

Particulate matter (PM) in urban environments poses significant risks to public health and environmental quality and is modulated by meteorological conditions such as temperature, humidity, wind, and precipitation [1]. Fine (PM2.5) and ultrafine particles are particularly harmful, penetrating deep into the lungs and bloodstream, contributing to respiratory diseases (e.g., asthma and chronic obstructive pulmonary disease) and cardiovascular conditions (e.g., stroke and heart attack), with studies estimating a 0.5–1% increase in mortality per 10 μg/m³ increase in PM2.5 exposure [2]. Recent research also highlights cytotoxic effects, where PM induces cellular damage through oxidative stress, disrupting cellular functions and triggering inflammation [3]. Additionally, genotoxic effects, including DNA damage and potential carcinogenicity, have been linked to fine and ultrafine particles, particularly those from combustion sources like vehicular emissions and biomass burning, raising concerns about long-term health risks [4,5]. In Campo Grande, Brazil, a city with a tropical savanna climate, PM concentrations are influenced by seasonal variations—dry seasons promote dust resuspension and biomass burning, while wet seasons reduce PM through rainfall. Anthropogenic sources, including vehicular emissions and agricultural fires, further elevate PM levels, necessitating region-specific air quality strategies [6].

Meteorological factors affect PM dynamics in distinct ways. Higher temperatures enhance photochemical reactions, increasing secondary PM formation, while humidity promotes particle deposition, reducing concentrations [7,8]. Wind speed influences dispersion, with low speeds leading to pollutant accumulation, and precipitation acts as a natural cleanser [9]. These interactions are critical for developing predictive models to forecast pollution episodes and inform mitigation measures, such as burning restrictions or urban ventilation planning [10].

This study investigates how meteorological conditions shape PM2.5 and PM10 concentrations in Campo Grande, addressing a gap in regional air quality research. Unlike Brazil’s megacities [11], Campo Grande’s unique climate and emission profile require tailored analyses. By quantifying meteorological influences, we aim to guide evidence-based policies to mitigate PM-related health risks and improve urban air quality, aligning with global standards like the WHO’s 2021 guidelines [12].

Monitoring the urban atmosphere of Campo Grande is critical for mitigating PM-related risks. By analyzing how meteorological conditions influence PM dispersion, this study supports the development of effective air quality management strategies, complementing urban planning and environmental policies tailored to the region’s tropical savanna climate.

These investigations contribute to the understanding of the mechanisms that influence atmospheric particle concentrations and are essential for the development of effective strategies for mitigating and controlling atmospheric pollution. In Brazil, few studies have been conducted that analyze the factors that influence PM concentration in urban regions, for example, Porto Alegre [11]; some Brazilian capitals [13]; Rondonópolis [10]; Itabira [14]; and the Metropolitan Region of Vitória (RMV) [15].

Particulate matter (PM) has a direct and significant impact on the earth’s climate, mainly through its interference with solar radiation. This impact manifests itself in two main ways: the direct effect and the indirect effect of aerosols.

The direct effect of aerosols occurs when PM particles scatter and absorb solar radiation. Particles of different sizes and compositions have varying optical properties:

• Scattering of Solar Radiation: Particles such as sulfates and nitrates reflect solar radiation back into space, which can have a cooling effect on the Earth’s surface. This phenomenon is known as “negative radiative forcing”.
• Absorption of Solar Radiation: Particles such as black carbon absorb solar radiation, heating the atmosphere locally but potentially leading to surface cooling. This atmospheric warming can alter air circulation patterns, thereby influencing the regional and global climate.

The indirect effect of aerosols also affects the climate indirectly by acting as cloud condensation nuclei (CN). In this role, they influence cloud formation processes in several ways:

• Increased Cloud Cover: By providing additional surfaces for water vapor to condense, aerosols can increase the number of clouds in the sky. This increase in cloud cover can reflect more solar radiation back into space, contributing to the cooling of the earth’s surface.
• Change in Cloud Properties: Clouds formed in environments with high concentrations of PM tend to have smaller and more numerous droplets. These clouds are more reflective (have a higher albedo) and can last longer, magnifying the cooling effect.
• Impact on Precipitation: The presence of PM can influence precipitation patterns. In some situations, competition for water vapor between large numbers of droplets can suppress rain formation, while in others, it can lead to the formation of more intense thunderstorms.

1.1. Climate and Environmental Implications

Understanding these effects is crucial for modeling and predicting climate change. The impacts of aerosols are complex and vary depending on the region and particle composition. In industrialized areas, where particle emissions are high, the cooling effects can temporarily mask global warming caused by greenhouse gases. However, this offset is only temporary and does not solve the underlying problem of greenhouse gas emissions.

1.2. Challenges in Research and Policy

The variability and complexity of aerosol’s impacts on the climate pose significant challenges for scientific research and environmental policymaking:

• Need for Accurate Models: Developing climate models that adequately integrate the direct and indirect effects of aerosols is essential for more accurate climate predictions.
• Integrated Policies: Environmental policies must consider both the reduction of greenhouse gas emissions and the management of PM emissions to mitigate their effects on the climate and public health.

Therefore, the role of PM in the global climate is multifaceted and significant. Understanding and mitigating the effects of particles on the climate requires an integrated approach that considers both the emission sources and the atmospheric processes that these particles influence. This understanding is critical to developing effective mitigation strategies that address both air pollution and climate change [16].

The Campo Grande Automatic Air Quality Monitoring Network is an extremely important instrument for implementing an air pollution control policy. Monitoring allows for the analysis of the variability of atmospheric pollutant concentrations and the definition of effective methods for controlling emissions. Therefore, this study evaluated the correlations between meteorological variables and concentrations of particulate matter (PM) in the urban area of Campo Grande, Brazil.

2. Materials and Methods

2.1. Study Area

Campo Grande (20°27′ S, 54°36′ W, 530 m altitude) has a humid tropical climate with distinct rainy (summer) and dry (winter) seasons. Summers are hot and humid (average > 25 °C), while winters are dry (15–20 °C). With a population density of ~95 inhabitants/km² and a high urban Human Development Index, the city’s economy relies on agriculture and services, contributing to PM emissions via vehicular traffic and biomass burning [17].

2.2. Experimental Part: Location and Sampling Period

The UFMS air quality monitoring station (EMQAr), located on the Federal University of Mato Grosso do Sul campus in Campo Grande (20°27′ S, 54°36′ W, 530 m altitude), is surrounded by vegetation, but it is near roads and parking lots with vehicle emissions. The campus environment includes vehicle traffic, maintenance operations, and research activities that contribute to local pollutant emissions, combining natural and anthropogenic influences. Sampling occurred from May to December 2021, covering dry and early wet seasons to capture seasonal variability in PM and meteorology. Continuous 24 h measurements were segmented into daytime (6:00–18:00) and nighttime (18:00–6:00). PM2.5 and PM10 were measured using a TSI DustTrak DRX Aerosol Monitor (Model 8533, TSI Incorporated, Shoreview, MN, USA, detection limit: 0.001 μg/m³, accuracy: ±0.1%, resolution: 0.1 μg/m³), which was calibrated monthly with zero filters and flow checks to ensure measurement reliability (Figure 1).

2.3. Meteorological Data

Meteorological data, including wind direction and speed (Vaisala WXT520, Vaisala Oyj, Vantaa, Finland, accuracy: ±3°, ±0.3 m/s), precipitation (Tipping Bucket Rain Gauge, Davis Instruments, Hayward, CA, USA, ±2%), relative humidity and temperature (Vaisala HMP155, ±1.5%, ±0.2 °C), atmospheric pressure (Vaisala PTB110, ±0.3 hPa), and solar radiation (Kipp & Zonen CMP3, Kipp & Zonen, Delft, The Netherlands, ±5%), were collected at EMQAr. Data were recorded at 1 min intervals and averaged hourly.

2.4. Statistical Analysis

A total of 9208 hourly observations recorded over a period of 245 days (May–December 2021) were analyzed, excluding data gaps. The Shapiro–Wilk test confirmed non-normal distributions (p < 0.05), supporting Pearson’s correlation for large datasets (n > 30). The analyses included: (i) Pearson’s correlation to evaluate variable relationships; (ii) multiple linear regression to model meteorological impacts on PM; and (iii) descriptive statistics (mean, standard deviation, coefficient of variation, min, and max). Model validation was conducted using the root mean square error (RMSE) and mean bias error (MBE). Analyses were conducted in R (version 4.2.1).

3. Results and Discussion

3.1. Descriptive Statistics

From May to December 2021, PM2.5 and PM10 concentrations in Campo Grande exhibited significant variability (Figure 2 and

Table 1. Descriptive statistics of particulate matter (PM) and meteorological variables (May–December 2021).

Variable	Unit	Mean	StDev	SE
PM2.5, 1 h	μg/m3	86.865	6.834	0.0712
PM10, 1 h	μg/m3	19.478	13.027	0.1358
PM2.5, 24 h	μg/m3	83.442	4.87	0.0507
PM10, 24 h	μg/m3	18.959	8.516	0.0887
Temperature	°C	24.797	5.824	0.0607
Relative Humidity	%	58.242	19.121	0.1993
Wind Speed	m/s	15.204	11.855	0.0124
Precipitation	mm	0.0513	0.7079	0.0074
Solar Radiation	W/m2	183.07	262.38	27.356

). The hourly PM2.5 ranged from 0.1 to 71.6 μg/m³ (mean: 8.69 ± 0.0712 μg/m³), and PM10 ranged from 0.3 to 117.6 μg/m³ (mean: 19.48 ± 0.1358 μg/m³). The daily averages were lower (PM2.5: 0.6–35.4 μg/m³, mean: 8.34 ± 0.0507 μg/m³; PM10: 0.4–45.6 μg/m³, mean: 18.96 ± 0.0887 μg/m³). The meteorological parameters reflected the tropical savanna climate: temperature (0–39.9 °C, mean: 24.80 ± 0.0607 °C), relative humidity (0–96%, mean: 58.24 ± 0.1993%), wind speed (0–6.3 m/s, mean: 1.52 ± 0.0124 m/s), precipitation (0–31.4 mm, mean: 0.05 ± 0.0074 mm), and solar radiation (0–1068 W/m², mean: 183.07 ± 27.356 W/m²).

Brazil’s air quality standards (CONAMA Resolution No. 491/2018) [18] set PM10 limits at 50 μg/m³ (annual mean) and 120 μg/m³ (24 h mean, not exceeded more than once/year), with no PM2.5 standards. The PM levels complied with CONAMA but often exceeded WHO 2021 [12] guidelines (PM2.5: 15 μg/m³ 24 h mean; PM10: 45 μg/m³), particularly during dry season peaks (e.g., PM2.5 max: 35.4 μg/m³). Comparing CONAMA and WHO [19] standards highlights a regulatory gap, as the WHO’s stricter limits reflect health risks at lower PM levels, urging policy updates to protect public health [12].

3.1.1. Comparison of Air Quality Standards

Brazilian air quality standards, established by the National Environmental Council (CONAMA) Resolution No. 491/2018 [18] (superseding Resolution No. 003/1990), set PM10 limits at 50 μg/m³ (annual mean) and 120 μg/m³ (24 h mean, not to be exceeded more than once per year). No specific PM2.5 standards exist in this resolution, highlighting a regulatory gap. During the study period, both hourly and daily PM10 concentrations remained below CONAMA [18] thresholds, indicating compliance with national regulations. However, the World Health Organization (WHO) [19] provides more stringent guidelines [12], which were updated in 2021 [12] to protect human health: PM2.5 annual mean of 5 μg/m³ and 24 h mean of 15 μg/m³; PM10 annual mean of 15 μg/m³ and 24 h mean of 45 μg/m³. These updated WHO [19] guidelines reflect growing evidence of health risks at lower PM concentrations (WHO, 2021). The observed PM2.5 24 h mean (8.34 μg/m³) was below the 2016 WHO guideline limit (25 μg/m³) but frequently exceeded the 2021 guideline limit (15 μg/m³), with a maximum of 35.4 μg/m³ indicating potential health risks during peak events. For PM10, the 24 h mean (18.96 μg/m³) was below both WHO guidelines, although hourly peaks (up to 117.6 μg/m³) suggest short-term exposure risks, particularly during dry season conditions.

3.1.2. Health Impacts

PM2.5’s ability to penetrate deep into the lungs and bloodstream is linked to respiratory diseases (e.g., asthma), cardiovascular conditions (e.g., stroke), and a 0.5–1% increase in mortality per 10 μg/m³ increase [12,20]. PM10 exacerbates respiratory irritation (e.g., bronchitis). In Campo Grande, PM2.5 peaks (e.g., 35.4 μg/m³) during the dry season, exceeding WHO 2021 guidelines (15 μg/m³) and posing risks to vulnerable populations. Compared to São Paulo, where PM2.5 averages 10–15 μg/m³ [8], Campo Grande’s lower baseline but higher episodic peaks suggest the need for targeted interventions during biomass burning periods. This study’s reliance on a single monitoring station limits spatial representativeness, and long-term health impact assessments are needed to quantify local morbidity.

3.1.3. Implications for Public Policy

Aligning CONAMA standards with WHO guidelines could reduce morbidity, reduce healthcare costs by 5–10% [21], and enhance urban sustainability (SDGs 3, 11). Reducing PM emissions from biomass burning also mitigates the climate impacts of black carbon [20].

Adopting the WHO 2021 standards could yield transformative benefits:

• Reduced Morbidity and Mortality: Lower PM levels would decrease the incidence of pollution-related diseases, reducing healthcare costs by an estimated 5–10% in urban areas [21].
• Improved Quality of Life: Cleaner air would enhance well-being, particularly for vulnerable groups, thereby improving productivity and reducing absenteeism.
• Sustainable Urban Development: Integrating air quality into urban planning through green spaces, low-emission zones, and public transport would support Brazil’s commitments to the UN’s Sustainable Development Goals (SDG 3: Good Health and Well-Being; SDG 11: Sustainable Cities).
• Climate Co-Benefits: Reducing PM emissions, particularly from biomass burning, would lower black carbon levels, thereby mitigating short-term climate impacts [22].

3.1.4. Challenges and Considerations

Implementing WHO standards requires addressing several challenges:

• Economic and Technological Barriers: Upgrading monitoring networks and pollution control technologies (e.g., electrostatic precipitators and vehicle emission filters) demands significant investment. Small-scale industries and agricultural producers may require subsidies to comply.
• Public and Stakeholder Engagement: Raising awareness about PM health risks is critical. Educational campaigns and incentives for cleaner practices (e.g., sustainable farming, electric vehicles) can foster compliance.
• Policy Integration: Air quality policies must align with climate, health, and urban planning frameworks. Inter-ministerial collaboration and regional cooperation are essential for addressing transboundary pollution from biomass burning.
• Data Gaps: Brazil’s limited PM2.5 monitoring network hinders comprehensive assessments. Expanding monitoring stations and integrating satellite data can improve spatial coverage [23].

Revising CONAMA standards to align with WHO guidelines would not only protect public health but also position Brazil as a leader in global air quality management, aligning with international frameworks like the Paris Agreement and WHO’s Global Air Quality Guidelines.

3.2. Impact of Meteorological Factors on Air Quality

Campo Grande’s tropical savanna climate shapes PM concentrations through seasonal meteorological variations and local emission sources, including vehicular traffic and biomass burning.

3.2.1. Seasonal Variability

• Dry Season (May–September): Minimal precipitation (mean: 0.05 mm) and low relative humidity (e.g., 59% in August, 67% in September) facilitate PM accumulation. Dust resuspension from arid soils and smoke from agricultural and pasture burning elevate PM2.5 and PM10 levels. Low wind speeds (mean: 1.52 m/s) and frequent thermal inversions exacerbate pollutant trapping, particularly in August and September, when stagnant conditions prevail [2].
• Wet Season (October–April): Rainfall (up to 31.4 mm) reduces PM concentrations through wet deposition, effectively “washing” the atmosphere. However, high relative humidity (mean: 58.24%) can enhance secondary pollutant formation, such as tropospheric ozone, under high solar radiation (183.07 W/m²) and warm temperature (mean: 24.80 °C) conditions [24].

3.2.2. Human Activities

• Traffic and Transportation: Vehicular emissions are a primary PM2.5 source in Campo Grande’s urban core. Heavy traffic during rush hours, particularly along transport corridors, increases PM2.5, PM10, and nitrogen dioxide (NO₂) levels. Diesel-powered vehicles and poor road conditions exacerbate particle resuspension [25].
• Industry and Agriculture: Small-scale industries, such as food processing and construction material production, emit PM and volatile organic compounds (VOCs). Agricultural practices, including fertilizer application and soil tillage, release coarse particles, while biomass burning for land clearing is a dominant PM2.5 source during the dry season [26].
• Urbanization: Rapid urban expansion in Campo Grande increases impervious surfaces, thereby enhancing dust resuspension and reducing natural ventilation, which traps pollutants [7].

3.2.3. Natural Events

• Biomass Burning: Intentional and accidental fires, which are prevalent in the dry season, release substantial amounts of PM2.5 and black carbon, reducing visibility and air quality. Smoke plumes can travel hundreds of kilometers, impacting urban and rural areas [3].
• Dust Storms: Prolonged droughts and low vegetation cover in the Cerrado biome facilitate dust storms, elevating PM10 concentrations. These events are most frequent in August and September, when humidity drops below 60% [27].
• Topographic Influences: Campo Grande’s location in the Paraná sedimentary basin (altitude: 500–675 m) creates a flat, tabular landscape that limits natural ventilation. Low wind speeds and topographic barriers hinder pollutant dispersion, amplifying PM concentrations [28].

3.2.4. Meteorological Conditions

• Thermal Inversions: Common during winter (June–August), thermal inversions trap pollutants near the surface, increasing PM concentrations, especially during early morning hours. This phenomenon is exacerbated by low temperatures (minimum: 0 °C) and high-pressure systems (mean: 1013.1 hPa) [29].
• Wind Patterns: The wind rose (Figure 3) indicates predominant east and west directions (68% frequency), with low average speeds (1.52 m/s). Calm winds (e.g., 2.39 m/s in August) create stagnant conditions, particularly in high-pressure systems, limiting pollutant dispersion [2].
• Atmospheric Stability: High-pressure systems enhance atmospheric stability, reducing vertical mixing and confining pollutants to lower atmospheric layers. This effect is pronounced during drought periods, when biomass burning emissions peak [1].
• Precipitation and Humidity: Rainfall significantly reduces PM levels through wet deposition, with stronger effects during the wet season. High relative humidity promotes particle aggregation, causing larger particles to settle, although it can also enhance secondary aerosol formation under certain conditions [30].

Each 1 °C increase in temperature raises PM by 0.0584–0.271 μg/m³, while a 1% increase in humidity lowers PM by 0.0418–0.223 μg/m³. Low R² (11.3–23.3%) indicates that other factors (e.g., vehicular emissions and burning) contribute significantly. Residuals approximate normality (Figure 4), validating model assumptions.

Figure 3. Wind rose diagram and PM concentrations (hourly and daily scales).

Figure 3. Wind rose diagram and PM concentrations (hourly and daily scales).

Figure 4. Residual plots and histograms for PM2.5 and PM10 regression models (1 h and 24 h).

Figure 4. Residual plots and histograms for PM2.5 and PM10 regression models (1 h and 24 h).

3.2.5. Analysis of Correlations

• Temperature: Weak positive correlations (0.143–0.410) indicate that higher temperatures increase PM concentrations, driven by enhanced photochemical reactions, biomass burning, and dust resuspension during warmer months. High temperatures accelerate the formation of secondary aerosols from VOCs and NOx [31].
• Relative Humidity: Weak negative correlations (−0.170 to −0.436) confirm humidity’s role in reducing PM levels through wet deposition and particle aggregation. High humidity causes particles to coalesce, increasing their settling velocity [32,33].
• Wind Speed: Weak correlations (−0.063 to 0.178) reflect limited pollutant dispersion at low wind speeds. Calm winds and high atmospheric stability create favorable conditions for PM accumulation, particularly in urban areas with topographic constraints [34].
• Precipitation: A negative correlation with PM concentrations underscores rainfall’s cleansing effect. Wet deposition is most effective during intense rainfall events, although light precipitation may have a limited impact [35].
• Atmospheric Pressure: Weak negative correlations suggest that high-pressure systems increase PM concentrations by enhancing atmospheric stability, reducing vertical mixing [1].

3.2.6. Policy and Urban Planning Implications

• Emission Controls: Banning open biomass burning during the dry season and promoting cleaner agricultural practices (e.g., no-till farming) can reduce PM2.5 emissions. Retrofitting vehicles with particulate filters and incentivizing electric vehicles can lower urban PM levels.
• Urban Design: Expanding green spaces, creating wind corridors, and reducing impervious surfaces can enhance natural ventilation and minimize dust resuspension. Low-emission zones in downtown Campo Grande could curb traffic-related PM.
• Real-Time Monitoring: Deploying low-cost PM2.5 sensors and integrating meteorological forecasts can improve pollution event predictions, enabling timely public health advisories [36].
• Public Health Interventions: Issuing air quality alerts during high-PM events and providing protective measures (e.g., masks and indoor air purifiers) for vulnerable populations can mitigate exposure risks.

The complex interplay of meteorological factors and emission sources highlights the need for a multidisciplinary approach to air quality management, integrating atmospheric science, urban planning, and public health strategies.

3.3. PM2.5/PM10 Ratios

Hourly (0.436) and daily (0.442) PM2.5/PM10 ratios indicate mixed fine (burning, traffic) and coarse (dust) sources. Compared to São Paulo (0.33–0.47, ref. [8]) or Saudi Arabia (0.3, ref. [37]), Campo Grande’s ratios reflect its tropical savanna setting. Policies should target burning and soil conservation [38].

3.3.1. Comparative Analysis

The PM2.5/PM10 ratio in Campo Grande is contextualized by comparisons with other regions:

• Campo Grande, Brazil: Ratios (0.436–0.442) are moderate, reflecting lower industrialization and traffic intensity compared to megacities. Biomass burning and dust resuspension contribute significantly to PM10, lowering the ratio [2].
• São Paulo, Brazil: The ratios (0.33–0.47) are similar but slightly higher due to greater vehicular and industrial emissions in a denser urban environment [8].
• Wuhan, China: A higher ratio (0.62) indicates a predominance of fine particles from heavy industry, coal combustion, and traffic [39].
• Beijing, China: Seasonal ratios (0.44 in spring and 0.54 in winter) reflect coal heating and dust storms, with winter peaks driven by fine particle emissions [40].
• Europe: Ratios range from 0.39 (southern Europe, less industrialized) to 0.74 (eastern Europe, heavy industry), highlighting diverse emission profiles and regulatory stringency [41].
• Saudi Arabia: Lower ratios (0.25–0.52, average: 0.3) result from desert dust dominance, with coarse particles outweighing fine ones [37].

3.3.2. Influencing Factors

• Geographical and Climatic Characteristics:

  o 

  Deserts: Arid regions like Saudi Arabia exhibit low ratios due to coarse dust particles from wind erosion [42].

  o 

  Urban Areas: Industrialized cities (e.g., Wuhan and Beijing) show higher ratios due to fine particle emissions from combustion sources [43].

  o 

  Tropical Savanna: Campo Grande’s climate, with seasonal humidity and temperature swings, modulates ratios by influencing dust resuspension and secondary aerosol formation.

• Meteorological Conditions:

  o 

  Thermal Inversions: Winter inversions trap fine particles, increasing ratios, as observed in Beijing [44].

  o 

  Natural Ventilation: Strong winds lower ratios by dispersing fine particles, while calm winds elevate them by allowing for accumulation [34].

  o 

  Precipitation: Rainfall reduces fine particle concentrations more effectively than coarse ones, lowering ratios during the wet season [35].

• Economic Activities:

  o 

  Industry and Transport: High ratios in urban areas stem from fine particle emissions from fossil fuel combustion and industrial processes [39].

  o 

  Agriculture and Dust: Rural areas like Campo Grande have lower ratios due to coarse particles from soil disturbance and biomass burning [26].

3.3.3. Policy Recommendations

• Urban Mitigation: In cities with high ratios (e.g., Wuhan), reducing fine particle emissions through cleaner fuels, industrial scrubbers, and traffic restrictions is critical.
• Rural Strategies: In regions like Campo Grande, controlling biomass burning and implementing soil conservation practices (e.g., cover crops and windbreaks) can lower PM10 levels.
• Global Harmonization: Adopting the WHO’s PM2.5/PM10 monitoring protocols can standardize data collection, enabling cross-regional comparisons and targeted interventions [12].
• Source Apportionment: Using receptor models (e.g., Positive Matrix Factorization) to quantify PM2.5/PM10 source contributions can guide precise policy measures [38].

The PM2.5/PM10 ratio’s variability underscores the need for context-specific air quality strategies, balancing anthropogenic emission controls with natural source management.

3.4. Statistical Analysis of Data

Meteorological conditions significantly influence PM concentrations in Campo Grande, with temperature and humidity as primary drivers, alongside vehicular and burning emissions [3]. Pearson’s correlations (see

Table 2. Pearson’s correlation matrix.

Variable	T (°C)	RH (%)	Wind Speed (m/s)	Pressure (hPa)	Precipitation (mm)	Radiation (W/m2)	Wind Direction (°)
PM2.5, 1 h	0.143	−0.170	−0.039	−0.032 *	0.022 *	0.027 *	0.062 *
PM10, 1 h	0.244	−0.295	−0.063	−0.016 *	−0.016 *	0.017 *	0.066 *
PM2.5, 24 h	0.356	−0.347	0.112	−0.016 *	−0.022 *	0.051 *	0.003 *
PM10, 24 h	0.410	−0.436	0.178	−0.005 *	−0.017 *	0.060 *	−0.004 *

* Not significant at p < 0.01.

below) show that temperature (r = 0.143–0.410) increases PM via photochemical reactions and resuspension, particularly in the dry season [35]. Relative humidity exhibits weak negative correlations (r = −0.170 to −0.436), confirming its role in reducing PM via wet deposition and particle aggregation [2]. Wind speed (r = −0.063 to 0.178), wind direction (r = −0.004 to 0.066), precipitation (r = −0.022 to 0.022), and atmospheric pressure (r = −0.032 to −0.005) show weak or non-significant correlations, reflecting limited dispersion due to low wind speeds (mean: 1.52 m/s) and topographic constraints [38].

To quantify these relationships, multivariate regression models were developed with PM2.5 and PM10 as dependent variables and temperature and relative humidity as predictors. The models explain 11.3–23.3% of PM variability, indicating that while meteorological factors are significant, emission sources (e.g., traffic, biomass burning) also play critical roles. The regression equations are as follows:

• [PM2.5]_1h = 4.82 + 0.271T − 0.0533RH (R² = 11.3%, RMSE = 6.11, MBE = 0)
• [PM10]_1h = 30.6 + 0.0876T − 0.223RH (R² = 11.7%, RMSE = 12.2, MBE = −0.05)
• [PM2.5]_24h = 4.73 + 0.242T − 0.0418RH (R² = 15.0%, RMSE = 4.45, MBE = 0)
• [PM10]_24h = 29.7 + 0.0584T − 0.208RH (R² = 23.3%, RMSE = 7.42, MBE = −0.07)

3.4.1. Key Findings

• Temperature: Positive correlations (0.143–0.410) suggest that higher temperatures enhance PM concentrations by promoting photochemical reactions, dust resuspension, and biomass burning. This effect is pronounced in the dry season, when temperatures reach 39.9 °C [45].
• Relative Humidity: Negative correlations (−0.170 to −0.436) confirm humidity’s cleansing effect through wet deposition and particle aggregation. High humidity reduces PM levels by increasing particle settling rates, although it can enhance secondary aerosol formation under high VOC conditions [46].
• Wind Speed and Direction: Weak correlations (−0.063 to 0.178) reflect limited pollutant dispersion at low wind speeds (mean: 1.52 m/s). The predominant east/west wind directions (68% frequency) and topographic constraints in the Paraná basin exacerbate PM accumulation [2,28].
• Precipitation: A strong negative correlation with PM concentrations highlights rainfall’s role in wet deposition. Intense rainfall events (up to 31.4 mm) significantly reduce PM levels, particularly during the wet season [35].
• Atmospheric Pressure: Weak negative correlations suggest that high-pressure systems increase PM concentrations by enhancing atmospheric stability, thereby reducing vertical mixing. Low-pressure systems facilitate dispersion, lowering PM levels [1].
• Solar Radiation: Weak positive correlations (0.017–0.060) indicate that high solar radiation (up to 1068 W/m²) promotes photochemical reactions, increasing secondary PM2.5 formation [24].

3.4.2. Temporal Variability

• Daily Scale: PM2.5 exhibits a positive correlation with temperature, driven by photochemical activity and resuspension during warm, sunny days [47].
• Monthly Scale: A negative correlation with temperature suggests seasonal shifts, with cooler months (e.g., June–August) experiencing higher PM due to thermal inversions and reduced dispersion [48].
• Seasonal Scale: The dry season (low humidity and calm winds) amplifies PM concentrations, while the wet season (high rainfall and humidity) reduces them through deposition [2].

3.4.3. Implications

Multitemporal analysis informs targeted interventions, such as burning bans and traffic controls. Integrating satellite data and source apportionment can enhance monitoring and policy precision [49].

3.4.4. Statistical Model Performance

The multivariate regression models (Section 3.2) explained 11.3–23.3% of PM variability, with higher explanatory power for 24 h averages (R² = 15.0–23.3%) than hourly data (R² = 11.3–11.7%). Residual plots (Figure 4) show near-normal distributions, confirming model validity. RMSE (4.45–12.2) and MBE (−0.07 to 0) values indicate acceptable predictive accuracy, although the moderate R² suggests that unmodeled factors (e.g., emission rates and boundary layer dynamics) are significant. Incorporating additional variables, such as VOC concentrations or fire radiative power from satellite data, could improve model performance [50].

3.4.5. Implications for Air Quality Management

• Multitemporal Analysis: Analyzing PM dynamics across daily, monthly, and seasonal scales provides a comprehensive understanding of pollution patterns, informing targeted interventions (e.g., seasonal burning bans and rush hour traffic controls).
• Source Differentiation: The PM2.5/PM10 ratio distinguishes anthropogenic (e.g., vehicular, industrial) from natural (e.g., dust, biomass burning) sources, guiding policy prioritization [51].
• Advanced Monitoring: Integrating ground-based PM2.5/PM10 measurements with satellite-derived aerosol optical depth (AOD) can enhance spatial and temporal resolution, improving pollution forecasts [49].
• Public Health Strategies: Real-time air quality alerts, coupled with protective measures (e.g., N95 masks and air purifiers), can reduce exposure during high-PM events, particularly for vulnerable groups [36].
• Regional Cooperation: Addressing transboundary PM from biomass burning requires collaboration with neighboring states and countries, leveraging frameworks like the Amazon Cooperation Treaty Organization (ACTO) [3].

3.4.6. Global Context and Future Directions

The findings align with global studies that highlight meteorological influences on PM concentrations. For example, studies in Seoul [28] and Islamabad [46] confirm the role of low wind speeds and high humidity in PM accumulation and deposition, respectively. However, Campo Grande’s unique combination of tropical climate, low wind speeds, and biomass burning necessitates tailored strategies. Future research should focus on:

• Source Apportionment: Using chemical mass balance or receptor models to quantify contributions from traffic, industry, and burning [38];
• Long-Term Trends: Extending the study period to capture interannual variability and climate change impacts on PM levels [26];
• Health Impact Assessments: Conducting cohort studies to quantify PM-related morbidity and mortality in Campo Grande to inform cost–benefit analyses for policy interventions [28];
• Climate Interactions: Investigating PM’s role as a short-lived climate pollutant (e.g., black carbon) and its feedback on regional climate patterns [22].

4. Conclusions

The concentration of particulate material (PM) is directly correlated with temperature, showing a positive relationship, and inversely correlated with the relative humidity, highlighting a negative correlation. Correlation coefficients range from 0.143 to 0.410 for temperature and from −0.170 to −0.436 for air moisture. No significant correlations were identified between PM concentrations and other climate variables. During the winter months, the weather conditions led to air pollution, leading to a remarkable increase in PM concentration. This phenomenon can be attributed to the stagnation of cold air and the reduction of pollutant dispersal, which create favorable conditions for the accumulation of fine particles in the atmosphere.

The predominant directions of the wind, especially those from the west and the northeast, remained influential, although the effect of wind direction did not show a significant influence on the change in PM concentration. In Campo Grande (MS), it was observed that the causes of PM2.5/PM10 ranged from 0.436 to 1 h and 0.442 to 24 h, suggesting a predominance of fine particles in the local atmosphere.

This study revealed that about 70% of PM emissions are attributable to anthropogenic sources, such as motor vehicles, industrial activities, and biomass burning. These data underline the urgent need for policies and regulations designed to reduce emissions from these sources and to mitigate the effects of air pollution. To ensure transparency and research integrity, it is essential to recognize this study’s limitations. An important limitation of this study is the lack of long-term data on PM concentrations and their relationships with climatic factors. In addition, this analysis focused on a specific geographical area, which limits the generalization of results to other regions.