Patterns and Dynamics of PM_2.5 and PM₁₀ Across Portugal: A Twelve-Year Perspective

Abstract

This paper aims to assess the temporal and spatial variability of particulate matter (PM) concentrations (PM_2.5 and PM₁₀) at several rural and urban monitoring sites located in Portugal between 2011 and 2022. The exceedances to European Union Directive limits and World Health Organization (WHO) air quality guidelines were also evaluated. Higher PM concentrations were observed mainly at urban sites (e.g., up to 156 exceedances of the WHO PM_2.5 guideline for daily average concentrations were recorded in a year), with the main contributions being from traffic emissions and industrial activities. On the other hand, the lower number of exceedances at rural sites can be attributed to long-range transport (e.g., Saharan dust) and wildfires. Temporal trends showed that PM_2.5 concentrations decreased by up to 0.6 µg/m³ per year, while PM₁₀ reductions reached 1.0 µg/m³ per year at certain sites, showing the effectiveness of air quality policies and clean technology advancements. Also, the number of exceedances of the air quality guideline of WHO for PM_2.5 at urban traffic sites like Entrecampos decreased from 140 in 2015 to 15 in 2022. Principal component analysis grouped the air monitoring sites based on PM variability. These findings provide a comprehensive understanding of the temporal variation of PM concentration, contributing to air quality management strategies and the design of mitigation measures.

1. Introduction

Particulate matter (PM) consists of solid and liquid particles suspended in the atmosphere. They can be emitted by natural sources (e.g., soil resuspension, wildfires, and natural dust intrusions) or anthropogenic sources (e.g., traffic emissions, industrial activities, and residential heating) [1,2,3]. PM, including PM_2.5 and PM₁₀, is a major air pollutant in Europe with significant impacts on human health, ecosystems, and climate [4,5,6]. PM_2.5 comprises particles with aerodynamic diameters of 2.5 µm or smaller (fine particles), while PM₁₀ includes PM with aerodynamic diameters of up to 10 µm (fine and coarse particles). Fine particles can penetrate deeper into the respiratory system, being associated with respiratory and cardiovascular diseases [7,8]. For human health protection, the European Union (EU) and World Health Organization (WHO) defined air quality standards to limit PM concentrations to mitigate its negative effects. EU Directive 2008/50/EC established the following limits: (i) a daily limit of 50 µg m⁻³ for PM₁₀; and (ii) annual limits of 40 and 25 µg m⁻³ for PM₁₀ and PM_2.5, respectively. On the other hand, WHO established more stringent limits for human health protection [9], as follows: (i) daily limits of 45 and 15 µg m⁻³ for PM₁₀ and PM_2.5, respectively; and (ii) annual limits of 15 and 5 µg m⁻³ PM₁₀ and PM_2.5, respectively.

Several studies have been performed to characterize the spatial and temporal variability of PM concentrations in different environments, considering the effect of local emission sources, meteorological conditions, and episodic events [10,11,12]. Hand et al. [10] analyzed the temporal and spatial variability of coarse particles (PM_2.5–10) in rural and urban environments in the United States from 2000 to 2016. A significant decrease of 1.8%/yr was calculated for urban sites, while a slight increase was observed in rural environments. The fraction of coarse particles in PM₁₀ increased in the analyzed period due to the strong reductions in fine particle (PM_2.5) concentration. Bigi and Ghermandi [11] assessed trends and emission sources of PM₁₀ concentrations in the Po Valley (Italy), considering data collected in 41 air quality monitoring sites (urban, suburban, and rural). A constant weekly pattern was observed for this pollutant, mainly in summer, due to anthropogenic emissions. Cluster analysis was applied, grouping the monitoring sites into five clusters based on similar pollution conditions. PM concentrations presented only slight reductions and only in a few locations, contrary to a significant reduction in gaseous pollutant concentrations. Pivato et al. [12] analyzed air quality data in the Po Valley from 2011 to 2021, investigating the impact of meteorological conditions, environmental policies, and the COVID-19 pandemic on the concentrations of air pollutants. Environmental policies like the “New Agreement of Po Valley Basin” contributed to the reduction in nitrogen oxide concentrations, contrary to PM₁₀, influenced by less-regulated sources (e.g., domestic heating, agriculture, and animal farming).

Unsal et al. [13] studied the air quality in Vilnius (Lithuania) and observed an increasing trend in the PM_2.5/PM₁₀ ratio over time, with values often exceeding 0.5, indicating a dominance of fine particles primarily from anthropogenic sources such as combustion-related pollution. The study also highlighted the significant impact of meteorological factors on PM levels, noting that weather conditions can lead to substantial differences in pollution levels. In Portugal, Gomes et al. [14] assessed the influence of Saharan dust events on air quality. PM_2.5/PM₁₀ ratios ranging from 0.20 to 0.50 were reported during these events, suggesting the prevalence of coarse particles. This natural occurrence is recognized as one of the main PM sources in southern Europe. Also, Cipoli et al. [15] evaluated PM_2.5 and PM₁₀ concentrations during daylight and night-time periods in an urban environment. Mean PM_2.5/PM₁₀ ratios were higher at night (0.46) than during the daytime (0.37), maybe due to nocturnal sources (e.g., biomass burning) and lower atmospheric dispersion of pollutants at night. PM_2.5 concentrations exceeded the WHO air quality guidelines in 45% of the monitored period.

These previous research studies showed the complex interaction between anthropogenic and natural emission sources and meteorological conditions that determine PM concentrations, their size distribution, and chemical composition. Continuous monitoring must be performed to obtain relevant information for the definition of air quality management strategies, which are specific in regional and temporal contexts. However, comprehensive analyses combining spatial and temporal patterns, exceedance frequencies, and source apportionment for rural and urban sites remain limited, particularly in Portugal. It is crucial to understand these patterns to obtain more information about PM source contributions and atmospheric processes in different regions/environments. Therefore, this study aims to fill these gaps by providing an in-depth analysis of the spatial and temporal variability of PM_2.5 and PM₁₀ concentrations across diverse monitoring sites in Portugal from 2011 to 2022. The objectives of this study are as follows: (i) to evaluate the exceedances of PM concentrations to EU Directive limits and WHO air quality guidelines; (ii) to analyze daily and seasonal profiles in PM concentrations, showing differences between rural and urban environments; (iii) to assess the variability of the PM_2.5/PM₁₀ ratio; and (iv) to group monitoring sites using principal component analysis (PCA) based on the variability of PM_2.5 and PM₁₀ concentrations.

2. Materials and Methods

2.1. PM Measurements

This study analyzes the temporal and spatial variability of PM_2.5 and PM₁₀ concentrations in Portugal, covering the period from January 2011 to December 2022. Data were obtained from air quality monitoring sites managed by the following different regional environmental agencies:

• Comissão de Coordenação e Desenvolvimento Regional (CCDR) do Norte: D. Manuel II (DM), Douro Norte (DN), Paços de Ferreira (PF), and Sobreiras (SB);
• CCDR do Centro: Ervedeira (ER), Estarreja (ES), and Fundão (FU);
• CCDR de Lisboa e Vale do Tejo: Camarinha (CA), Chamusca (CH), Entrecampos (EN), Fernando Pó (FP), Laranjeiro (LA), Lourinhã (LO), Mem Martins (MM), Olivais (OL), and Paio Pires (PP);
• CCDR do Alentejo: Monte Chãos (MC), Monte Velho (MV), Santiago do Cacém (SC), Sonega (SN), and Terena (TE);
• CCDR do Algarve: Cerro (CE) and Joaquim Magalhães (JM);
• Direção Regional do Ambiente dos Açores: Faial (FA);
• Direção Regional do Ambiente da Região Autónoma da Madeira: Santana (SA), São Gonçalo (SG), and São João (SJ).

The type of environment, geographical coordinates, and altitudes of the monitoring sites are provided in

Table 1. Characteristics of the air quality monitoring sites.

Site	Type	Longitude	Latitude	Altitude (m)
CA—Camarinha	Urban Background	−8.8732	38.5315	15
CE—Cerro	Rural Background	−7.6786	37.3125	300
CH—Chamusca	Rural Background	−8.4674	39.3541	143
DM—D. Manuel II	Urban Traffic	−8.6187	41.2356	90
DN—Douro Norte	Rural Background	−7.7908	41.3713	1086
EN—Entrecampos	Urban Traffic	−9.1490	38.7486	86
ER—Ervedeira	Rural Background	−8.8929	39.9246	68
ES—Estarreja	Suburban Background	−8.5672	40.7586	14
FA—Faial	Rural Background	−28.6314	38.6050	310
FP—Fernando Pó	Rural Background	−8.6918	38.6373	57
FU—Fundão	Rural Background	−7.2996	40.2331	461
JM—Joaquim Magalhães	Urban Background	−7.9267	37.0150	4
LA—Laranjeiro	Urban Background	−9.1576	38.6635	63
LO—Lourinhã	Rural Background	−9.2470	39.2800	143
MM—Mem Martins	Urban Background	−9.3485	38.7865	173
MC—Monte Chãos	Suburban Industrial	−8.8380	37.9546	129
MV—Monte Velho	Rural Background	−8.7986	38.0770	53
OL—Olivais	Urban Background	−9.1073	38.7698	32
PF—Paços de Ferreira	Urban Background	−8.3759	41.2741	300
PP—Paio Pires	Suburban Industrial	−9.0821	38.6210	47
SA—Santana	Rural Background	−16.8867	32.8089	n.a.
SC—Santiago do Cacém	Urban Industrial	−8.6977	38.0201	261
SG—São Gonçalo	Urban Background	−16.8832	32.6489	n.a.
SJ—São João	Urban Traffic	−16.9183	32.6497	n.a.
SB—Sobreiras	Urban Background	−8.6590	41.1474	17
SN—Sonega	Rural Industrial	−8.7240	37.8708	235
TE—Terena	Rural Background	−7.3989	38.6168	187

n.a.—not available.

(the map of Portugal with spatial distribution of monitoring sites are presented in Figure S1). The hourly average concentrations of PM_2.5 and PM₁₀ (in µg m⁻³) were extracted from the Portuguese Environmental Agency database (http://www.qualar.org, accessed in 25 October 2024). The PM measurements (hourly average concentrations) were performed using the beta radiation attenuation method (MPSI 100 I et E equipment from Environment SA, Poissy, France), which provides continuous monitoring data. The equipment was submitted to a rigid maintenance program, calibrated every 4 weeks. The data used were provided by the Portuguese entities after validation. Only monitoring sites with a minimum annual monitoring efficiency of 75% were included for each year of the study period (see Tables S1 and S2). Outliers on the data were identified and deleted. The daily averages were calculated to investigate exceedances of the EU limits for PM_2.5 and PM₁₀. The PM_2.5/PM₁₀ ratio was used to indicate fine particulate dominance over coarser particles at each site.

2.2. Statistical Methods

Principal component analysis (PCA) was employed to group air quality monitoring sites based on the variability of PM_2.5 and PM₁₀ concentrations during the study period. PCA is a statistical technique that creates new orthogonal and uncorrelated variables, called principal components (PCs), which are linear combinations of the original variables. These PCs are arranged such that the first PC explains the largest proportion of the variance, followed by subsequent PCs explaining progressively smaller proportions.

Rotated factor loadings (RFL) are used to simplify the interpretation of the components by redistributing the variance in a way that highlights clearer patterns in the data. Initially, loadings represent the correlation coefficients between the original variables and the principal components (PCs). These loadings indicate how strongly each variable contributes to a given component, with higher absolute values reflecting a greater influence. Varimax rotation was applied to adjust the axes of the components, making the relationships between the variables and components more distinct. This process does not alter the total variance explained by the components but redistributes it among them, often enhancing clarity. This rotation preserves the independence of the components, ensuring that they remain uncorrelated, while oblique rotation allows the components to be correlated. This process simplifies the loadings by concentrating high values on specific components, effectively grouping variables under individual components. While PCA is commonly used for dimensionality reduction, in this study, it was applied as a non-parametric classification tool to group monitoring sites with similar patterns of PM variability.

The first step in PCA is selecting the appropriate number of principal components (PCs). The Kaiser criterion, which retains PCs with eigenvalues greater than 1, is a commonly used approach [16,17,18]. However, this criterion often fails to capture a high percentage of the original data variance [17,18,19], which is critical for air quality assessments as it helps to address the localized behavior of air pollutants. To address this limitation, an alternative criterion was employed in this study, ensuring the selection of PCs that collectively account for at least 90% of the original data variance (ODV₉₀). This approach allows for a more comprehensive representation of the dataset, incorporating a greater proportion of its variability and enhancing the reliability of the PCA results.

The dataset was structured with each column representing the hourly average PM concentrations from a specific monitoring site. PCA requires complete data for all monitoring sites at the same time intervals; therefore, gaps in the dataset were addressed by excluding periods with insufficient data. The PCA results were evaluated with correlations between the original variables and PCs using RFLs, with significant loadings being identified to understand how the sites were grouped. Relative frequencies of site groupings in the same PC were computed to evaluate the spatial relationships between monitoring locations.

3. Results and Discussion

3.1. Temporal and Spatial Analysis of PM_2.5 and PM₁₀ Concentrations

The temporal variability of PM_2.5 and PM₁₀ concentrations in different locations in Portugal showed improved air quality due to specific policy decisions and events that occurred during the studied period. A high number of exceedances of the WHO air quality guideline for daily average PM_2.5 concentrations (15 µg m⁻³) were consistently observed at several sites, notably ER, ES, and EN sites (see Table S3), due to traffic emissions and industrial activities. These locations exhibited some of the highest exceedances throughout the study period, with ER peaking at 156 exceedances in 2011. Urban sites, such as LA and OL, also recorded persistently high exceedances, reflecting the significant impact of traffic emissions. Conversely, rural and remote sites (e.g., FA) showed minimal exceedances, benefiting from limited human activity and cleaner environmental conditions (e.g., atmospheric dispersion). A significant decrease in PM_2.5 exceedances over time was verified in almost all monitoring sites. For example, at the EN site, the number of exceedances was reduced from 140 in 2015 to 15 in 2022, and simultaneously, at the OL site, this number decreased from 126 in 2011 to 30 in 2022. These air quality improvements reflect a positive impact of the EU air quality policies and the adoption of low-emission vehicles. Also, the EN and OL sites are located in the Lisbon region in which the air quality benefited from the implementation of the Low Emission Zone (LEZ) in 2011, which contributed significantly to the reduction in traffic emissions (reduction in background air pollution of up 30.5%) [20]. However, at the ES and ER sites, several exceedances occurred in later years, mainly influenced by local industrial activities, requiring additional mitigation strategies for air pollution.

Table 2. Annual average PM_2.5 concentrations.

Site	2011	2012	2013	2014	2015	2016	2017	2018	2019	2020	2021	2022
CA												4.7
CE				4.5	6.7	6.9	7.9				2.6
CH	9.5	7.0	8.6	7.5	10.5	7.3	8.4	7.2	6.9	5.3	5.7	6.2
DM	5.5	5.5	4.8	3.3
DN	4.3	3.7
EN	12.9	10.8	11.8	10.9	14.9	14.4		13.5	11.7	10.0	8.6	6.8
ER	16.4	14.8	10.6	5.5	7.3	6.2			9.8	8.6	8.7	9.3
ES	16.6	17.2	14.0	12.5	14.7				9.0	8.8	11.9	10.0
FA	3.5		2.8	2.9		3.2			3.4		2.6
FP	7.9			6.4	11.8		13.5			7.2	5.4	6.2
FU	6.5	5.5	5.2	4.3	5.6		5.4		4.2	4.2	8.6	5.7
JM				11.4	11.9		9.8				3.6	3.9
LA	12.0	9.4	10.9	8.6	13.6	12.7	14.8	13.5	10.0	9.2	8.2	7.3
LO	5.8					6.7	8.5	7.2	7.2	6.7	7.5	5.8
MM	8.8	7.2	7.5		12.2	7.5	9.6	8.4	8.1	8.9	8.7	8.9
MC				10.9					6.5	5.9	6.7	6.7
MV	10.6	8.9	12.8
OL	14.1	12.9	11.7	11.2	11.3	9.8	11.6	10.1	9.2	9.6	8.1	7.9
PF		5.4			8.2
PP										9.5	9.3	9.0
SA						2.9	6.4	3.4	3.2	3.1	3.7	3.6
SC						6.4						5.2
SG						0.5	1.5	2.9	4.7	3.8	3.9	4.5
SJ						8.8	8.0	6.6	5.8	6.0	6.2	7.2
SB	6.8	6.2	4.8
SN					4.7	4.2	4.2	3.8	2.7	4.7		5.9
TE	9.3	8.2		10.3	12.3	13.0	13.3		4.1	3.5

shows the annual average PM_2.5 concentrations calculated for each monitoring site. Concerning the EU Directive limit of 25 µg m⁻³, no exceedances were recorded. The stricter WHO PM_2.5 guideline (5 µg m⁻³) was often exceeded, mainly at urban and suburban sites. This limit was obeyed during the studied period only at the rural sites FA and SG—located at the Azores and Madeira islands, respectively—even though a declining trend (up to 0.6 µg m⁻³ yr⁻¹) was observed at almost all monitoring sites from 2011 to 2022. This air quality improvement may be related to adopting cleaner technologies, stricter emission regulations (e.g., vehicles), and improved industrial practices. Rodrigues et al. [21] analyzed the air quality trends in six European regions (2008–2017). Bristol and Amsterdam achieved significant reductions in air pollution. However, concerning PM₁₀, Ljubljana, Sosnowiec, and Aveiro presented several exceedances of the legal limits. Zhao et al. [22] evaluated the temporal and spatial variation of PM_2.5 concentrations in five urban regions in China (2000–2021). PM_2.5 concentrations followed a U-shaped trend, rising before 2013, and declining after the government implemented clean air policies. In 2021, the compliance rates to the legislated limit ranged from 44 to 100%.

The PM₁₀ concentrations showed similar trends, with exceedances of the EU Directive daily average limit (50 µg m⁻³) often recorded at sites DM and ES (see

Table 3. Number of exceedances of the limit established by the EU Directive for the daily average PM₁₀ concentrations for human health protection (50 µg m⁻³).

	2011	2012	2013	2014	2015	2016	2017	2018	2019	2020	2021	2022
CA												8
CE				3	10	3	3				0
CH	3	1	2	3	2	7	9	2	0	0	3	10
DM	59	55	18	10
DN	0	0
EN	31	17	3	7	15	9		3	10	1	6	7
ER	19	15	2	3	16	2			9	0	6	4
ES	62	49	27	31	23				11	10	14	9
FA	0		0	0		0			0		0
FP	6			0	5		15			1	2	7
FU	0	6	1	2	2		11		0	4	6	9
JM				4	9		1				4	13
LA	29	7	5	6	12	8	12	8	14	3	7	12
LO	1					6	2	2	2	0	7	4
MM	6	0	1		6	4	5	2	2	2	6	6
MC				36					3	1	7	8
MV	3	2	0
OL	29	7	4	2	7	8	9	2	5	1	4	7
PF		10			5
PP										8	12	20
SA						0	8	3	8	3	9	15
SC						15						10
SG						0	16	7	9	7	7	27
SJ						5	27	7	7	10	12	28
SB	49	37	21
SN					0	1	1	2	1	2		5
TE	24	10		2	5	10	16		0	1

; the exceedances of air quality guideline of WHO [9] are presented in Table S4). Urban sites with high traffic density, such as EN and OL, also presented several exceedances of the legislated limit, emphasizing the contributions of vehicular emissions in the air pollution of this region. Improvements were evident at some locations. For example, at the EN site, the exceedances were reduced from 51 in 2011 to 12 in 2022, reflecting the impact of the local air quality measures, as mentioned above. Concerning the annual average PM₁₀ concentrations, there was a clear contrast between compliance with the EU Directive limit and the stricter WHO PM₁₀ guideline (see

Table 4. Annual average PM₁₀ concentrations.

	2011	2012	2013	2014	2015	2016	2017	2018	2019	2020	2021	2022
CA												16.9
CE				13.7	17.5	15.7	17.8				10.3
CH	17.3	15.4	15.9	15.0	16.4	14.5	16.8	15.5	13.5	11.6	13.6	16.3
DM	31.7	28.5	20.9	15.2
DN	12.4	12.5
EN	29.7	25.2	22.3	22.4	24.3	22.7		23.0	20.9	18.2	17.8	20.1
ER	25.6	21.8	19.8	16.7	22.0	17.7			19.2	16.8	17.4	18.2
ES	33.8	29.9	26.1	23.9	25.5				23.5	21.5	19.8	20.0
FA	6.3		5.9	4.8		5.8			8.2		7.8
FP	20.4			15.2	19.2		24.6			15.1	14.4	16.8
FU	10.9	12.7	11.4	10.2	13.8		15.9		11.6	15.2	14.6	16.2
JM				19.5	21.6		19.7				18.3	21.6
LA	26.5	21.7	22.9	20.0	22.0	19.6	22.2	22.4	21.2	19.5	19.1	22.3
LO	16.7					15.1	16.8	15.3	16.0	14.2	14.8	15.7
MM	22.3	17.5	19.3		20.5	16.6	20.6	18.2	18.1	17.5	18.6	19.8
MC				26.3					18.0	16.7	17.9	19.6
MV	22.0	20.0	21.5
OL	29.9	24.2	23.4	20.4	20.2	18.0	20.8	18.4	17.1	17.9	17.7	19.5
PF		22.5			21.7
PP										20.1	21.1	22.5
SA						14.7	18.9	14.2	12.3	11.4	12.0	14.1
SC						23.0						17.4
SG						10.2	15.4	15.7	15.3	13.9	13.1	19.7
SJ						16.7	26.2	21.4	18.7	17.7	18.1	22.7
SB	31.3	26.8	22.4
SN					13.4	12.3	11.0	15.0	14.3	13.4		18.5
TE	22.0	20.2		17.5	19.7	20.6	23.8		12.1	11.7

). None of the monitoring sites exceeded the EU annual limit of 40 µg m⁻³ during this period, reflecting significant progress in reducing particulate pollution in line with the EU regulatory standards. However, the stricter WHO guideline of 15 µg m⁻³ was frequently exceeded, particularly in the urban and industrial areas. Also, a declining trend in the annual average PM₁₀ concentrations was observed at most monitoring sites (up to 1.0 µg m⁻³ yr⁻¹). This improvement aligns with the adoption of cleaner technologies, stricter emission regulations, and the promotion of renewable energy sources. Urban traffic sites, such as the EN and OL sites, showed significant reductions over time. For instance, at the EN site, the air quality consistently improved, reflecting the impact of the traffic management measures mentioned above. Similarly, suburban and industrial areas, such as the ES sites, recorded gradual decreases in annual PM₁₀ levels; however, additional mitigation measures should be implemented to meet the WHO PM₁₀ guideline.

Spatial variability across Portugal reveals distinct differences between urban, suburban, and rural areas. Urban traffic and industrial hotspots, such as the EN and ES sites, consistently recorded higher concentrations, whereas rural background sites like DN and FU reported lower levels. The island regions of the Azores and Madeira, exemplified by FA and SG, consistently showed the lowest PM levels, benefiting from maritime airflows and minimal local pollution sources.

Overall, the temporal improvements in air quality across Portugal highlight the effectiveness of regulatory measures and technological advancements. However, the persistence of high exceedances in specific locations, driven by industrial emissions and traffic, requires additional targeted mitigation strategies.

3.2. PM Concentration Profiles

Figure 1 shows the distribution of PM_2.5 and PM₁₀ concentrations during the studied period. The CH and EN sites were selected as examples of rural and urban sites due to their high monitoring efficiency during the studied period. At the CH site, PM concentrations generally remained low, reflecting the cleaner air typical of rural environments. The annual distributions of PM_2.5 and PM₁₀ concentrations were similar until 2019, presenting a notable reduction during the COVID-19 lockdown, highlighting the significant contribution of anthropogenic activities to urban air pollution [23,24]. Gama et al. [25] evaluated the indirect effect of COVID-19 on air pollution in Portugal. Data from 20 monitoring sites were used, and a mean reduction of 18% was achieved for PM₁₀ concentrations. Slezakova and Pereira [26] evaluated the impact of COVID-19 on air pollution in Portugal, considering several monitoring sites and environments (urban, suburban, and rural). PM was reduced by 30% in Lisbon and up to 49% in the northern region. In that period, traffic-related PM dropped by 10–70%. Skiriene and Stasiškien [27] evaluated the impact of the COVID-19 lockdown on air quality of five European Countries (United Kingdom, Spain, France, Italy, and Sweden). The associated reduction in industrial and mobility activities in this period reduced the air pollution by 20–40%.

In contrast, the EN site showed higher PM_2.5 and PM₁₀ concentrations, mainly due to urban traffic emissions. Higher levels were observed in 2011 when the LEZ was implemented in Lisbon [20]. Afterwards, in 2019, a decrease in PM concentrations was also observed due to the COVID-19 period. The analyzed period of 2015 was the year with the highest concentrations of PM_2.5, and the data from this period were selected for further analysis. Figure 2 shows the seasonal average profile of PM_2.5 and PM₁₀ concentrations at the CH and EN sites in 2015. Seasonal trends further differentiated the two sites. At the CH site, higher PM concentrations during the summer months can be linked to wildfires and Saharan dust events, with both being more prevalent in the warmer periods [28,29]. On the other hand, at the EN site, high PM levels were recorded during the winter, likely due to increased residential heating and the trapping of pollutants under stable atmospheric conditions. Also, in the summer period, the curves of PM_2.5 and PM₁₀ concentrations were closer (similar to the profiles observed at the CH site), corresponding to a higher fraction of PM_2.5 compared to coarse particles (PM_2.5-PM₁₀). Wildfires, more frequent during the summer due to hot and dry conditions, release large amounts of fine particulate matter (PM_2.5) into the atmosphere. Moreover, summer months are characterized by higher photochemical activity, driven by increased solar radiation. This leads to the formation of secondary fine particles, such as sulfates, nitrates, and organic aerosols, which elevate PM_2.5 levels.

Figure 3 shows the average daily profile of PM_2.5 and PM₁₀ concentrations at the CH and EN sites in 2015. At the CH site, a rural monitoring site, the average daily profile of PM concentrations reflects the limited influence of anthropogenic activities and the dominance of natural processes. Unlike urban areas (EN site), characterized by distinct peaks during morning and evening rush hours due to traffic emissions, the CH site exhibits a relatively flat profile. This lack of variability can be attributed to the absence of significant pollution sources, such as heavy traffic or industrial activities, that typically drive fluctuations in PM levels.

During the day, solar radiation promotes atmospheric mixing, dispersing particulates and maintaining stable PM concentrations [30]. At night, lower temperatures and more stable atmospheric conditions may cause slight increases in PM levels as particles settle closer to the surface [31]. These natural atmospheric processes are the primary drivers of the daily PM profile at the CH site.

3.3. PM_2.5/PM₁₀ Ratio

The analysis of the PM_2.5/PM₁₀ ratios at the CH (rural) and EN (urban) monitoring sites reveals different patterns that reflect the influences of natural and anthropogenic sources in these environments (see Figure 4). The PM_2.5/PM₁₀ ratio serves as an indicator of the relative contribution of fine particles (PM_2.5) compared to the total particulate matter (PM₁₀), offering valuable insights into source apportionment and atmospheric processes.

At the CH site, a rural monitoring location, the PM_2.5/PM₁₀ ratios typically range between 0.40 and 0.70, indicating that fine particles constitute a significant portion of the total particulate matter. The coarse fraction is primarily associated with natural sources such as soil dust, biological debris, and other non-combustion-related particles. Episodic events like Saharan dust transport and wildfires can markedly influence these ratios. For instance, Saharan dust events often reduce the PM_2.5/PM₁₀ ratio by increasing the concentration of coarse particles, while wildfires elevate the fine particle fraction due to the emission of PM_2.5 from biomass combustion [32,33]. Despite these episodic variations, the background conditions at the CH site remain relatively stable, consistent with rural areas characterized by limited anthropogenic influences.

On the other hand, the EN site, located in an urban area, displays lower PM_2.5/PM₁₀ ratios, typically ranging from 0.30 to 0.60, indicating a higher prevalence of coarse particles than fine particles. This result reflects the contribution of coarse particles from some urban activities (e.g., construction activities). While fine particles from sources like vehicular emissions, industrial processes, and residential heating are present, the coarse fraction predominates due to local and transient pollution sources. Moreover, urban areas experience higher levels of resuspension of dust and other coarse particles, which leads to a lower PM_2.5/PM₁₀ ratio. This variation highlights the distinct characteristics of urban pollution, where coarse particles are more prominent in the particulate matter profile than in rural environments. The observed patterns at the CH and EN sites are consistent with the trends reported in the literature. European studies have documented typical PM_2.5/PM₁₀ ratios of 0.40–0.50 in urban background sites, where natural sources dominate, and 0.60–0.80 in regional background sites, where anthropogenic sources and secondary particle formation are more prevalent [34,35].

Seasonal variations also play a role in shaping PM_2.5/PM₁₀ ratios [36,37,38]. Urban sites, like EN, often experience higher ratios during the winter due to increased emissions from residential heating and the accumulation of fine particles under stable atmospheric conditions. On the other hand, in the winter, frequent precipitation may reduce PM_2.5 concentrations, decreasing the PM_2.5/PM₁₀ ratio. Rural sites show more consistent ratios throughout the year (see Figure 2), with occasional deviations driven by episodic events like wildfires and dust transport.

3.4. PCA

The PCA of PM_2.5 and PM₁₀ concentrations reveal important patterns in the grouping of air quality monitoring sites, providing insights into the underlying factors driving particulate matter variability. Tables S5 and S6 show (as examples) the RFL of PCA applied to PM_2.5 and PM₁₀ concentrations, respectively, measured in 2022. Table S7 shows the relative frequency of each pair of monitoring sites with important contributions in the same PC obtained for the PM_2.5 and PM₁₀ data.

Urban monitoring sites, such as EN, OL, MM, and LA, are consistently grouped within the same PCs, demonstrating a strong correlation in PM concentrations. This grouping reflects the dominance of shared anthropogenic sources, such as vehicular emissions and residential heating. Considering their geographical proximity, these urban sites are influenced by similar emission patterns and meteorological conditions (e.g., wind and temperature), reinforcing their similar PM patterns. The high relative frequency with which these sites group together, particularly for PM_2.5, underscores the homogeneity of urban air pollution sources. In contrast, rural monitoring sites, including CH, DN, and FA, often show distinct groupings or weaker associations with urban sites. This divergence highlights the role of natural sources, such as soil resuspension, agricultural activities, and episodic events like Saharan dust intrusions, which have a significant impact on rural areas.

The differences between PM_2.5 and PM₁₀ groupings reveal the distinct nature of fine and coarse particulate sources [39]. PM_2.5 groupings tend to be more homogeneous, driven by combustion processes and secondary aerosol formation, which dominate fine particulate contributions. In contrast, PM₁₀ groupings show greater variability, reflecting the influence of localized and episodic events, such as dust resuspension and construction activities.

3.5. Study Contributions to National and EU Air Quality Policies

The results of this study align closely with the objectives of both national and EU air quality policies, particularly those aimed at reducing particulate matter (PM) concentrations and achieving compliance with air quality standards. The observed decline in PM levels over the 12-year period is associated with the positive impact of air quality policies such as the EU Ambient Air Quality Directives (2008/50/EC) and the adoption of cleaner vehicle technologies, industrial emission controls, and energy-efficiency measures. These results validate the effectiveness of the existing measures while highlighting areas requiring further attention.

The reductions in PM concentrations at urban traffic sites reflect the success of policies targeting vehicular emissions, such as the implementation of Euro standards for vehicle emissions, low-emission zones (e.g., Lisbon), and the transition to cleaner fuels. However, persistent exceedances of the WHO air quality guidelines, especially in urban areas, emphasize the need for additional mitigation measures to address emissions from road transport, residential heating, and industrial sources.

In the Portuguese context, specific urban areas (e.g., EN) constantly exhibited exceedances of PM₁₀ and PM_2.5 legal limits. Therefore, mitigation strategies are required at the national level to address the specific sources of pollution. For example, promoting the electrification of public transportation fleets and expanding renewable energy use for residential heating could significantly reduce emissions in these areas.

This study bridges a critical gap by providing long-term analyses of PM trends across diverse monitoring sites in Portugal. By identifying the key drivers of PM variability and highlighting persistent critical areas, this work can serve as a foundation for policy updates, supporting future mitigation strategies.

4. Conclusions

The analysis of PM_2.5 and PM₁₀ concentrations at urban and rural monitoring sites in Portugal over the 2011–2022 period has provided valuable insight into the temporal and spatial variability of these air pollutants. At urban sites, higher PM concentrations were consistently recorded, mainly due to traffic emissions, industrial activities, and residential heating. Frequent exceedances of the WHO air quality guidelines for both PM_2.5 and PM₁₀ highlighted the need for additional mitigation measures to improve urban air quality. In contrast, rural sites generally recorded lower PM concentrations.

Temporal trends showed a decline in PM concentrations at most sites, showing the effectiveness of air quality policies and technological improvements in different sectors to reduce emissions. Daily and seasonal variations further emphasized the differences between urban and rural environments. The urban sites displayed concentration peaks during morning and evening rush hours, linked to traffic emissions, and also higher wintertime concentrations due to residential heating. On the other hand, the rural sites demonstrated more stable daily profiles, with summertime peaks driven by natural events like wildfires and dust transport.

Applying PCA to air quality data, monitoring sites were grouped based on PM variability, revealing strong correlations among urban sites due to shared emission sources and distinct groupings among the rural sites influenced by localized natural phenomena. These findings provide a comprehensive understanding of the temporal variation of PM concentration in Portugal, emphasizing the need for targeted air quality management strategies.