Assessment of PM₁₀ and PM_2.5 Concentrations in Santo Domingo: A Comparative Study Between 2019 and 2022

Abstract

This study analyzes the spatial and temporal variability of PM₁₀ and PM_2.5 concentrations in Santo Domingo, Dominican Republic, based on short-term sampling campaigns conducted in 2019 and 2022. In 2019, PM₁₀ levels averaged 38.14 µg/m³, while in 2022 they rose significantly to 62.18 µg/m³. PM_2.5 in 2022 averaged 30.37 µg/m³. These differences are likely influenced by meteorological variability, including increased transport of Saharan dust in mid-2022, and seasonal factors. Although local emission changes were not directly assessed, they may have also played a role in the observed trends. Statistical analyses revealed that aerosol optical depth (AOD), air pressure, and rainfall were significant predictors of PM₁₀ in 2022, explaining up to 75% of the variance. Correlations and regression models confirmed a robust association between AOD and PM levels on a weekly timescale. These findings highlight the importance of integrating remote sensing and meteorological data to improve air quality monitoring and inform environmental policy in Caribbean urban areas.

1. Introduction

Air pollution remains one of the most pressing environmental and public health challenges in urban areas worldwide [1,2,3,4,5,6,7,8,9]. Particulate matter (PM) consists of tiny solid particles and liquid aerosols containing acids, organic compounds, metals, and dust [10,11,12,13,14]. PM originates from both natural sources, such as volcanic eruptions, wildfires, dust storms, and long-range transport of Saharan dust via the Saharan Air Layer (SAL)—a dry, elevated air mass that carries dust westward across the Atlantic—and human activities like fuel combustion, vehicle exhaust, and industrial emissions [11,14,15,16,17,18,19,20,21].

PM, particularly PM₁₀ (particles with a diameter less than 10 µm) and PM_2.5 (particles with a diameter less than 2.5 µm, is of special concern due to its adverse effects on human health and its ability to penetrate the respiratory system [22,23,24]. Several studies have also shown that toxicity tends to increase as particle size decreases, due to the enhanced ability of finer particles to induce genotoxic and cytotoxic effects [25,26,27,28,29,30]. Recent studies have assessed the genotoxic and cytotoxic effects of particulate matter on human lung cells, including the significant DNA damage and reduced cell viability induced by PM_2.5 collected from open-cast coal mining areas, the apoptosis and inflammatory damage caused by PM_2.5 exposure in lung and corneal epithelial cells, and the cytotoxic effects of PM₁₀ emitted from domestic activities like cooking and ironing, which increased reactive oxygen species production but showed no mutagenic effects; additionally, research on the toxic effects of PM_2.5, silica, and nanosilica on respiratory health indicates that PM_2.5 and nanosilica cause more pronounced cytotoxicity, providing valuable insights into the adverse health effects of particulate matter, particularly with regard to genotoxicity and cytotoxicity [31,32,33,34]. Elevated concentrations of PM have been associated with increased morbidity and mortality from cardiovascular and respiratory diseases, making it a significant topic of study in environmental science [35,36,37,38,39,40].

Urban environments, especially in developing regions, are particularly susceptible to elevated levels of particulate matter (PM), often exceeding the World Health Organization (WHO) recommended daily limits of 45 µg/m³ for PM₁₀ and 15 µg/m³ for PM_2.5 [3]. In cities like Santo Domingo, these thresholds are frequently surpassed due to a combination of vehicular emissions, industrial activities, construction, and the influence of meteorological conditions [7,8,41,42,43].

In this context, identifying the spatial and temporal patterns of particulate matter, as well as its key predictors, is essential to understanding the factors driving air quality in urban areas and to informing effective mitigation strategies [9,44,45,46,47,48]. Studies have shown that variables, such as wind speed, air pressure, temperature, rainfall, and aerosol optical depth (AOD) can influence PM concentrations, but their relative importance often varies by location, season, and particle size [47,49,50]. Recent studies have highlighted diverse regional factors influencing PM dynamics. For instance, long-term monitoring in Makkah revealed consistently high levels of PM₁₀ and PM_2.5, with seasonal peaks during winter attributed to reduced wind speeds and poor dispersion conditions, and also linked PM concentrations to trace element toxicity and health risks [51]. In the Nanchang region of China, PM_2.5 was found to affect urban surface temperatures and radiation balance, demonstrating the complex role of fine particles in atmospheric thermodynamics [52]. Meanwhile, in southern Italy, residence time analysis showed that both local emissions and long-range Saharan dust transport contributed significantly to PM₁₀ levels, especially during summer [53]. These studies underscore the need for localized assessments that consider meteorological conditions, emission sources, and regional transport dynamics.

Moreover, solar radiation parameters such as sunshine duration and clearness index—which are affected by temperature and relative humidity—can influence satellite-based aerosol measurements like AOD, potentially altering their correlation with ground-level PM concentrations [54,55]. These interactions highlight the need to consider not only conventional meteorological variables but also atmospheric optical properties when using predictors such as AOD to model particulate matter behavior.

To complement such temporal and atmospheric insights, geostatistical methods play a crucial role in mapping the spatial distribution of particulate matter. Among these, the kriging interpolation method, as described by Morphet [56], is especially effective, as it incorporates both the distance between sampling locations and the spatial autocorrelation structure of the data. This allows for the generation of reliable estimates in unsampled areas. The resulting isolines—contours of equal predicted pollutant concentrations (e.g., PM_2.5, PM₁₀)—facilitate the visualization of spatial gradients: densely clustered lines reveal abrupt concentration changes, while widely spaced lines indicate more homogeneous distributions. By integrating observed values with spatial statistical inference, kriging produces continuous pollution surfaces that better reflect underlying environmental patterns. This technique has been widely applied in recent studies to assess atmospheric pollutants, such as PM_2.5, NO₂, and ozone across diverse geographical contexts [57,58,59,60,61].

The city of Santo Domingo, located in the National District on the south-central coast of the Dominican Republic, is an ideal setting for this study due to its diverse urban environments and increasing exposure to air pollution [62,63,64,65,66,67,68]. However, comprehensive studies investigating the spatial distribution of PM and its relationship with meteorological and environmental variables in this region are still limited [63]. Previous work has highlighted the need for systematic monitoring and analysis of PM concentrations, including the use of low-cost sensors, to better understand local air quality dynamics [21,63,68,69,70]. Despite growing interest in air quality, most existing studies in the Dominican Republic are either geographically limited or lack detailed spatial analysis, particularly regarding the influence of meteorological and satellite-derived variables.

In this study, we analyze PM₁₀ and PM_2.5 concentrations collected during two distinct sampling campaigns in 2019 and 2022 across various urban environments in Santo Domingo. The primary objectives of this research are as follows: (1) to assess the spatial distribution and temporal variability of PM₁₀ and PM_2.5, (2) to investigate the relationship between PM concentrations and key meteorological, environmental variables and satellite observations using linear regression models, and (3) to determine the predictive power of these variables for explaining PM variations in both years. By identifying significant predictors of PM concentrations, this study contributes to a better understanding of air quality dynamics in Santo Domingo and provides a foundation for targeted environmental policies and interventions aimed at improving urban air quality.

2. Materials and Methods

The study was conducted in Santo Domingo, National District, Dominican Republic (ca. $18.{49}^{\circ }\mathrm{N},69.{96}^{\circ }\mathrm{W}$), focusing on various types of urban environments (Figure 1). The sampling sites included public and private schools, a university, and an urban park, selected based on a previous study [63,68].

In 2019, a total of 26 sites were selected for PM₁₀ monitoring, each sampled on at least three different days between July and December. The sampling design was systematic, targeting a separation of approximately 2 km between locations. Final site placement was refined using the nearest neighbor method, applied to idealized sampling points generated with mapping software. In contrast, the 2022 campaign consisted of 30 sites sampled once between May and July. Due to logistical and budgetary constraints, only one day of measurement was conducted per site during that year. While single-day measurements limit the capture of short-term variability, this approach enables the comparison of pollution levels across a broad spatial extent. To address this limitation, we conducted complementary statistical analyses, including linear regressions and correlations with meteorological variables, to contextualize and validate the observations. In addition, since sampling activities typically spanned several consecutive days within each week during the measurement period, the resulting data allowed the computation of representative daily, weekly, and monthly averages for time series analysis.

Although efforts were made to maintain the same sampling sites in both campaigns, in some cases, this was not possible due to access restrictions, lack of permits, or safety concerns for equipment installation. To ensure the continuity of the study, alternative locations were selected in representative areas within the same urban environment. As noted, the second campaign included more sites to improve spatial coverage and capture variability in air pollution across different areas of Santo Domingo. This adaptation allowed for a broader perspective on the distribution of particulate matter in the city, complementing the data obtained in 2019. While some sampling sites varied between 2019 and 2022, the selection followed homogeneous location criteria (urban areas with similar characteristics). Additionally, statistical analyses were applied to assess general trends in particulate matter, ensuring the comparability of results between both periods.

For both sampling campaigns, particulate matter was collected using MiniVol^TM TAS Portable Air Samplers (AirMetrics Co., Eugene, OR, USA) [71,72]. The MiniVol TAS samplers were deployed at each site for 24 h to collect representative samples of particulate matter. This device operates by drawing air through size-selective impactors, which separate PM₁₀ and PM_2.5 fractions based on their aerodynamic diameter. The measurement principle is based on inertial impaction followed by gravimetric analysis using pre-weighed 47 mm PTFE (polytetrafluoroethylene) Teflon filters. These filters were selected for their chemical inertness, low hygroscopicity, and stable mass under conditioning, making them suitable for accurate gravimetric determination and potential subsequent chemical or elemental analysis. The manufacturer specifies a nominal flow rate of 5.0 L/min with a precision of $\pm 5\%$, and a lower detection limit of approximately 3–5 µg/m³, depending on ambient conditions and sampling duration. Measurement accuracy for both PM₁₀ and PM_2.5 is reported to be within $\pm 10\%$, assuming proper calibration and maintenance of the device. Although the MiniVol^TM samplers used in this study are suitable for accurate gravimetric analysis of PM₁₀ and PM_2.5, they do not support the collection of PM₁ or the high-frequency measurements required to analyze PM ratios. Therefore, the analysis was limited to these two standard size fractions.

The samplers were installed at a height ranging from 1.5 to 3 m above ground level, either directly on the ground or on flat rooftop surfaces. This height range was maintained consistently across all sites to ensure comparability of measurements. In 2019, only PM₁₀ was measured, as the campaign was initially designed to focus on coarse particles. In 2022, both PM₁₀ and PM_2.5 were measured using the appropriate impactors for each fraction, allowing for a more comprehensive characterization of airborne particulate matter.

Data Analysis

The collected data were analyzed using a variety of statistical methods to evaluate the concentration and distribution of particulate matter (PM₁₀ and PM_2.5) in Santo Domingo. The analysis involved the calculation of descriptive statistics, correlation tests, regression modeling, and geospatial techniques.

Descriptive statistics were calculated to summarize the data, including measures of central tendency and dispersion. The analysis provided mean, median, standard deviation, and confidence intervals for PM₁₀ and PM_2.5 concentrations across different sampling periods.

Correlation analyses were performed to examine the relationships between PM₁₀ and PM_2.5 concentrations [46,73]. The Spearman correlation coefficient was calculated to assess the strength and direction of the associations. The Spearman correlation coefficient, $\rho$, is given by the Equation (1):

$$\rho =1-{\displaystyle \frac{6\sum d_i^2}{n(n^2-1)}}$$

(1)

where $x_i$ and $y_i$ are the individual sample points, $\overline{x}$ and $\overline{y}$ are the means of the sample points, $d_i$ is the difference between the ranks of corresponding variables, and n is the number of observations.

Cross-correlation functions (CCF) were calculated to examine the temporal relationships between aerosol optical depth (AOD) and PM₁₀ concentrations, using daily data along with weekly and monthly averages for temporal aggregation [20,74,75,76,77,78,79,80]. Among the temporal resolutions tested, weekly averages yielded the most interpretable and statistically significant correlations and were, therefore, used in the subsequent analyses. Time series visualizations were used to represent the temporal trends in AOD and PM₁₀ concentrations for 2019 and 2022. Weekly averages were plotted, and smoothed trends were illustrated using Locally Estimated Scatterplot Smoothing (LOESS) regression. Additionally, an animation of AOD data was created to depict temporal variations over the study period.

Linear regression models were developed to investigate the relationship between PM_2.5 and PM₁₀ concentrations. The models were evaluated for normality of residuals, homoscedasticity, and goodness of fit using appropriate statistical tests, such as Shapiro–Wilk, Anderson–Darling, and Breusch–Pagan tests. To ensure the robustness of the regression results, three outliers were excluded from the analysis. These outliers were identified using Cook’s distance, a measure used to detect influential data points. Specifically, observations with Cook’s distance values exceeding a threshold, calculated as ${\displaystyle \frac{2}{n-k-1}}$ where n is the number of observations and k is the number of predictors in the model, were considered as outliers and subsequently removed from the regression analysis.

Geospatial analyses were conducted to map the distribution of PM₁₀ and PM_2.5 concentrations across the study area. Geographic coordinates of the sampling sites were used to create spatial plots. Kriging interpolation was applied to predict PM concentrations at unsampled locations. Spatial autocorrelation analysis was performed using Moran’s I to assess the degree of clustering or dispersion of particulate matter concentrations. Multiple approaches for defining spatial weights were tested, including distance-based and k-nearest neighbor methods, selecting the 5-nearest neighbors as the most appropriate spatial weighting scheme. This approach facilitated the identification of significant hotspots, influential sites and spatial outliers. Local Indicators of Spatial Association (LISA) were applied to visualize clusters of high and low values, while multiple custom functions were developed to detect influential sites, evaluate the strength of spatial relationships, and quantify spatial outliers based on Cook’s distance and Moran scatterplots [81,82,83,84].

To identify the meteorological and environmental variables that predict particulate matter (PM₁₀ and PM_2.5) concentrations, linear regression models were fitted. The predictor variables considered in this analysis included air temperature, air pressure, wind speed, rainfall, and Aerosol Optical Depth (AOD). All variables were obtained or derived from meteorological records and remote sensing products. Meteorological data, including air temperature, air pressure, wind speed, and rainfall, were sourced from the RDSD meteorological station ($18.{4614}^{\circ }\mathrm{N},69.{9114}^{\circ }\mathrm{W}$), where daily averages were computed from high-frequency measurements to ensure consistency with particulate matter sampling periods. Aerosol Optical Depth (AOD) data were retrieved from the ‘MCD19A2.061: Terra & Aqua MAIAC Land Aerosol Optical Depth Daily 1 km’ product via Google Earth Engine [85], which is publicly available for use. The ‘MAIAC’ stands for Multi-angle Implementation of Atmospheric Correction. This product provides daily AOD estimates at a spatial resolution of 1 km, which is suitable for our analysis because the distance between sampling sites exceeds this resolution, ensuring a one-to-one spatial correspondence between each site and its associated AOD pixel. The AOD data were extracted for an Area of Interest (AOI) encompassing the National District and an adjacent buffer zone. The resulting raster datasets were processed to obtain spatially averaged values at weekly intervals, ensuring comparability across sites. Before fitting the regression models, as mentioned before, spatial independence of the observations was verified using Moran’s I statistic.

The linear models were fitted separately for each period and particulate matter type. For 2019, models were evaluated during the July–August, September–October, and November–December periods. For 2022, the analysis considered the entire year for both PM_2.5 and PM₁₀ concentrations. Each model included the predictor variables as independent terms and the particulate matter concentrations as the dependent variable. The explanatory power of each model was assessed using the coefficient of determination ($R^2$), while the statistical significance of each predictor variable was evaluated through p-values. A tile plot was used to visually represent the relationships between predictor variables and particulate matter concentrations.

All analyses were conducted using R statistical software (Version 4.4.0) [86]. Packages, such as tidyverse, readxl, sf, sf, zoo, spatialreg, spdep, raster, terra, stars, forecast, caret, corrplot, GGally and gganimate, were utilized for data manipulation, statistical analysis, and visualization [76,87,88,89,90,91,92,93,94,95,96,97,98,99,100]. Custom functions were developed for specific tasks, such as performing correlation tests and fitting linear models.

3. Results

The basic statistics for particulate matter (PM) concentrations are shown in

Table 1. Average PM₁₀ and PM_2.5 concentrations (µg/m³) for the years 2019 and 2022 in Santo Domingo.

Year, PM	N	Min.	Mean ± Error	Median	Max.	Std. Dev.	Confidence Interval (95%)
2019, PM10	26	10.85	38.14 ± 3.58	33.06	77.27	18.24	(30.78, 45.51)
2022, PM2.5	30	12.50	30.37 ± 3.61	25.33	75.94	19.76	(22.99, 37.75)
2022, PM10	30	25.51	62.18 ± 4.81	57.04	113.05	26.33	(52.34, 72.01)

and Figure 2. In 2019, each site was sampled on at least three different days; site-level averages were calculated first and then used to compute the overall annual mean. In contrast, the 2022 campaign relied on single-day measurements per site, and the annual means reflect the average across all site values. The mean PM₁₀ concentration in 2019 was 38.14 µg/m³ (N = 26), while in 2022, the mean concentrations were 30.37 µg/m³ for PM_2.5 (N = 30) and 62.18 µg/m³ for PM₁₀ (N = 30).

The paired t-test comparing PM₁₀ concentrations between 2019 and 2022 indicated a significant difference, with a mean difference of −18.38 µg/m³ (t = −2.38, p = 0.029), indicating that PM₁₀ concentrations were higher in 2022 than in 2019, with a 95% confidence interval ranging from −34.66 to −2.10 µg/m³. Normality of the differences was confirmed using the Shapiro–Wilk test ($p>0.05$). In contrast, the Wilcoxon signed-rank test showed no statistically significant difference in the medians (V = 41, p = 0.054), although the result was close to the threshold for significance.

Turning to the data for 2022, the normality tests indicated that both PM_2.5 and PM₁₀ concentrations deviated significantly from normality (

Table 2. Normality testing and correlation of PM_2.5 and PM₁₀ concentrations, year 2022.

Test	Result (p-Value)
Assumption of normality (S-W test) PM2.5	$W=0.83$ (p < 0.001)
Assumption of normality (S-W test) PM10	$W=0.9$ (0.001 < p < 0.01)
Correlation between PM2.5 and PM10 concentrations	$\rho =0.52$ (0.001 < p < 0.01)

). Due to this deviation, Spearman’s rank correlation coefficient ($\rho$) was used to assess the relationship between the two pollutants. A moderate, but significant positive correlation was observed between PM_2.5 and PM₁₀ concentrations, suggesting that higher levels of one pollutant were generally associated with higher levels of the other during this period.

To further explore the relationship between PM_2.5 and PM₁₀ concentrations in 2022, a linear regression model was fitted, excluding three outliers identified by Cook’s distance. The model indicated a significant positive relationship between the two pollutants, with PM_2.5 serving as a strong predictor of PM₁₀ levels. The regression equation was $y=29+0.92·x$, where y represents PM₁₀ and x represents PM_2.5. The model explains approximately 65% of the variance in PM₁₀ concentrations (R² = 0.65, adjusted R² = 0.63, p < 0.001). The residual standard error was 13.99, and the fitted model exhibited no significant violations of the assumptions of normality or homoscedasticity. This suggests that, after excluding the identified outliers, there is a robust linear association between PM_2.5 and PM₁₀ in 2022 (Figure 3).

Figure 4 shows the isopleths of the distribution around the region of PM₁₀ and PM_2.5 measured in Santo Domingo during both campaigns, 2019 and 2022 and the air quality stations (red circles). For the 2019 campaign, PM₁₀ concentrations were generally lower, with values ranging mainly between 10 and 25 µg/m³ in the study area. Higher concentrations, exceeding 70 µg/m³, were observed in the westernmost part of the domain, as well as in some isolated spots in the north and center. Meanwhile, for the 2022 campaign, it is observed that for PM₁₀ it reaches values higher than 50 µg/m³ distributed throughout almost the entire study domain with the exception of some isolated areas in the center, south, and southwest with lower values. However, for PM_2.5, the area with the highest values is located to the south of the study area, reaching values higher than 25 µg/m³.

The temporal trends of the weekly mean aerosol optical depth (AOD) for 2019 and 2022 are shown in Figure 5. Both years exhibit a clear seasonal pattern, with AOD values increasing steadily from January to a peak around July–August and subsequently declining toward December. In 2022, AOD values were consistently higher compared to 2019 throughout most of the year, particularly during the first half. This difference is more pronounced during the March to August period, where 2022 shows sharper increases, reaching maximum AOD values of approximately 0.4. The smoothed trends, visualized with LOESS regression, further emphasize these seasonal patterns, with confidence intervals highlighting significant deviations between the two years. Notably, AOD variability, as indicated by the error bars, is greater in 2019 during the middle of the year but becomes more stable in 2022. These results suggest a distinct difference in aerosol optical depth behavior between the two years.

The cross-correlation function (CCF) between weekly averaged AOD and PM₁₀ concentrations for 2019 and 2022 is presented in Figure 6. The analysis reveals a significant positive correlation at lag 0, suggesting that changes in AOD are contemporaneously associated with changes in PM₁₀. The correlation is strong at lag 0, with a coefficient close to 0.5, indicating a moderate association. At lag −1, the correlation is negative and relatively strong, while at lag −2, it becomes positive and strong. In contrast, the correlations at other lags, both positive and negative, are weak and not statistically significant. These findings indicate that variations in AOD and PM₁₀ concentrations occur synchronously on a weekly timescale, with minimal temporal displacement. This contemporaneous relationship highlights the potential of AOD as a reliable predictor for PM₁₀ concentrations when analyzed at a weekly resolution.

The local spatial autocorrelation analysis identified several influential sites and spatial outliers, along with a minimal number of hotspots, across different years and particulate matter (PM) sizes (

Table 3. Spatial autocorrelation diagnostics of PM₁₀ and PM_2.5 concentrations in Santo Domingo for the years 2019 and 2022 (see Figure 1 and Table A1 for site reference). An “X” indicates that the corresponding site was identified under the respective spatial category (e.g., influential point, spatial outlier, or hotspot).

Year	Particulate Matter (µm)	Site	Influential −	Influential +	Spatial Outlier −	Spatial Outlier +	LISA Hotspot
2019	10	8	X
2019	10	22	X
2019	10	23		X		X
2022	2.5	10	X		X
2022	2.5	18		X
2022	2.5	33		X		X	X
2022	10	29					X
2022	10	31	X

and Figure 7). However, as shown further, these localized patterns were not reflected in the global spatial autocorrelation analysis. For PM₁₀ in 2019, sites 8 and 22 were negatively influential, while site 23 was both positively influential and a positive spatial outlier, indicating higher PM₁₀ concentrations than expected. In 2022, site 10 for PM_2.5 was negatively influential and a negative spatial outlier, with sites 18 and 33 being positively influential; site 33 was also a positive spatial outlier and LISA hotspot, indicating a cluster of high PM_2.5 values. For PM₁₀ in 2022, site 29 was a LISA hotspot, and site 31 was negatively influential, revealing localized patterns of influence and clustering.

The spatial autocorrelation analysis for particulate matter (PM) using the Global Moran’s I statistic revealed non-significant results across all periods and particulate matter types assessed. Specifically, after log-transforming the particulate matter variables to better meet the assumption of normality, none of the Moran’s I tests yielded significant results. This indicates a lack of spatial dependence in the observed PM concentrations.

These results indicate that the particulate matter measurements do not exhibit significant spatial clustering or patterns, which aligns with the minimal number of LISA hotspots identified in previous analyses. This lack of spatial autocorrelation justifies the use of traditional statistical models that assume independent observations. Consequently, subsequent analyses can explore the relationships between PM levels and potential meteorological predictors without needing to account for any underlying spatial structure, thereby avoiding violations of the independence assumption.

The analysis of meteorological variables and Aerosol Optical Depth (AOD) as predictors of particulate matter concentrations revealed some interesting patterns. The results of the analysis were summarized visually in a tile plot, which highlights the significant predictor variables and their corresponding R² values (Figure 8). This figure enables a visual interpretation of the predictive strength of each variable across the analyzed periods and particulate matter types.

Notably, no significant predictors were identified for PM₁₀ levels during the months of July–August and November–December 2019 using linear models. However, the results indicated that for PM₁₀ concentrations during September–October 2019, wind speed emerged as a significant predictor, albeit with a relatively low explanatory power (R² = 0.181).

In contrast, for the year 2022, multiple variables showed predictive power for both PM_2.5 and PM₁₀ levels. Specifically, air pressure was identified as a significant predictor for PM_2.5, although with moderate explanatory power (R² = 0.361). For PM₁₀ in 2022, both AOD and air pressure exhibited strong predictive capabilities, with the model achieving a high R² of 0.752. Additionally, rainfall was also a significant predictor for PM₁₀ during this period, further indicating that meteorological conditions played a more influential role in explaining variations in particulate matter concentrations in 2022 compared to 2019.

4. Discussion

This study evaluated PM₁₀ and PM_2.5 concentrations in Santo Domingo, focusing on their spatial distribution, temporal variability, and relationships with meteorological and environmental variables. The findings reveal significant insights into particulate matter dynamics, particularly the role of aerosol optical depth (AOD), meteorological predictors, and localized spatial patterns.

The elevated PM₁₀ concentrations observed in 2022 can be partially attributed to the transatlantic transport of Saharan dust, which typically intensifies between May and August. During this period, large plumes of mineral dust originating in North Africa are lifted into the atmosphere by strong surface winds and carried westward across the Atlantic Ocean by the easterly trade winds and the Saharan Air Layer (SAL) [101,102]. When these dust-laden air masses reach the Caribbean, including the Dominican Republic, they can increase the background concentration of coarse particles (PM₁₀) in the troposphere [103]. This regional-scale phenomenon may contribute to the “dustiness” of the local atmosphere, independently of local emissions, and is typically reflected in elevated Aerosol Optical Depth (AOD) values. The timing of our 2022 campaign coincided with peak Saharan dust transport activity, which likely explains the higher PM levels observed compared to the 2019 campaign, conducted later in the year when dust transport tends to wane [101].

Despite a substantial increase in the national vehicle fleet from 2019 to 2022 [104], the minimal growth in the National District and Santo Domingo province suggests that vehicular emissions are unlikely to explain the observed PM₁₀ increase in 2022. Instead, meteorological conditions, such as air pressure and rainfall, alongside elevated AOD values, emerged as significant predictors, as demonstrated by the regression models. The strong explanatory power of these variables, particularly for PM₁₀ (R² = 0.752), underscores their critical role in influencing particulate matter levels.

The cross-correlation analysis revealed a synchronous relationship between AOD and PM₁₀ at lag 0, indicating that real-time AOD data could serve as a reliable proxy for estimating PM₁₀ concentrations, consistent with findings from previous studies that demonstrated strong correlations between AOD and surface particulate matter levels [80,105,106]. The temporal alignment suggests that satellite-derived AOD products could enhance air quality monitoring in regions with limited ground-based observations, as supported by studies showing that integrating AOD into air quality models significantly improves PM₁₀ and PM_2.5 estimations, particularly in areas with sparse monitoring networks [107,108,109,110,111,112,113,114].

Spatial autocorrelation analysis identified localized patterns, including influential sites, hotspots, and spatial outliers. For instance, site 23 in 2019 exhibited high PM₁₀ concentrations, serving as both a positive spatial outlier and an influential site. In 2022, site 33 was a LISA hotspot for PM_2.5, with significant clustering of high values. However, the absence of global spatial autocorrelation suggests that PM concentrations are not driven by broader spatial patterns, allowing the use of traditional regression models that assume independent observations. This lack of spatial autocorrelation is expected when observations are taken on different dates, particularly for particulate matter concentrations, which represent a highly dynamic phenomenon strongly influenced by atmospheric conditions [115,116]. This further underscores the need to model its behavior using physical atmospheric models to complement statistical approaches [117,118].

The analysis of predictor variables revealed that meteorological factors and AOD had varying explanatory power for PM concentrations depending on the year and particle size fraction. The limited number of significant predictor variables identified for the 2019 campaign—only wind speed during the September–October period—may reflect constraints related to sample size, temporal coverage, or meteorological variability during that year. Compared to 2022, where multiple variables showed significant associations with PM concentrations, the weaker statistical relationships observed in 2019 suggest reduced explanatory power, potentially due to less variability in environmental conditions. These factors may have limited the ability to detect consistent patterns between PM levels and the predictor variables. Despite this, the isolated significance of wind speed in 2019 supports its potential relevance under certain seasonal conditions and warrants further investigation in future studies.

This study presents valuable insights into the spatial and temporal dynamics of PM₁₀ and PM_2.5 concentrations in Santo Domingo; however, several limitations must be acknowledged. The sampling strategy, based on short-term and single-day measurements, restricts the ability to capture daily or seasonal variability in particulate matter concentrations. The spatial coverage, while sufficient to highlight broad patterns, does not fully resolve fine-scale heterogeneity, particularly in areas with dense urban activity. Additionally, the use of portable gravimetric samplers, although suitable for comparative studies, does not allow for continuous monitoring and may miss transient pollution events. These limitations may affect the generalization of the findings and the detection of consistent predictors, especially under varying meteorological conditions. Finally, the absence of PM₁ data and real-time measurements prevented the analysis of particle size ratios (e.g., PM_2.5/PM₁₀), which can provide further insight into pollution sources and health risks. The lack of “indicator gases”, such as nitrogen oxides and ozone, also limits the ability to definitively confirm the source of the dust, particularly in relation to Saharan dust transport. Future studies could integrate such gases to better assess pollution sources and their range. Moreover, integrating low-cost sensors capable of recording PM₁, PM_2.5, and PM₁₀ simultaneously and at high temporal resolution will help address this gap [119].

Despite these constraints, the study emphasizes the value of integrating spatial and meteorological analyses to uncover localized phenomena and validate predictive models. To strengthen future research, we recommend expanding the temporal coverage through repeated or continuous sampling, incorporating data from fixed monitoring stations, and including additional variables, such as land use, industrial activity, and localized emission sources. From a policy perspective, our findings support strategies to improve air quality in Santo Domingo, including the expansion of the monitoring network, stricter control of vehicular and industrial emissions, urban planning measures to mitigate pollution hotspots, and the promotion of public awareness and interagency coordination.

5. Conclusions

This study provides a comparative assessment of PM₁₀ and PM_2.5 concentrations in Santo Domingo for 2019 and 2022, offering key insights into their temporal trends, meteorological predictors, and spatial variability. The significant differences in particulate matter levels between the two years are closely linked to seasonal meteorological conditions and Saharan dust activity, rather than local anthropogenic sources.

The regression models identified AOD, air pressure, and rainfall as robust predictors of PM concentrations, highlighting the potential of integrating remote sensing data with ground-based measurements to enhance air quality monitoring. The cross-correlation analysis further supports the use of AOD as a real-time proxy for PM₁₀, providing valuable tools for policymakers and researchers.

Spatial analyses revealed localized influences, with certain sites demonstrating significant clustering and outlier behavior. However, the lack of global spatial autocorrelation underscores the need for targeted interventions rather than broad regional strategies. The study underscores the importance of incorporating localized meteorological and environmental data into air quality management plans.

Future studies should aim to expand the temporal and spatial scope of sampling, incorporate high-resolution emission inventories, and assess the health implications of observed PM concentrations. These efforts will enhance the ability to design effective mitigation strategies and improve urban air quality in Santo Domingo and similar urban environments.

6. Recommendations

While this study provides a comparative assessment of PM₁₀ and PM_2.5 levels in different areas of Santo Domingo, future research should incorporate continuous measurements or repeated sampling to assess temporal variability with greater precision. To improve continuity in future studies, it is recommended to establish a fixed monitoring network to analyze the evolution of pollutants at the same sites over time.

The results of this study suggest that, in addition to meteorological factors, anthropogenic sources such as traffic and industrial activity may play a significant role in PM levels in Santo Domingo. Future research should include detailed emission inventories and impact analyses of traffic control policies and industrial regulations to better understand their influence. Implementing mitigation strategies will require a multi-sectoral approach involving government agencies, industries, academia, and local communities. By integrating better monitoring, stricter regulations, sustainable transportation, and urban greening, Santo Domingo can significantly reduce air pollution and improve public health.