Assessment of Air Pollution and Lagged Meteorological Effects in an Urban Residential Area of Kenitra City, Morocco

Abstract

Complex mixtures of air pollutants, including ozone (O₃), carbon monoxide (CO), sulfur dioxide (SO₂), nitrogen dioxide (NO₂), black carbon (BC), and fine particulate matter (PM_2.5), present significant health risks. To understand the factors influencing air pollution levels and their temporal variations, comprehensive high-resolution long-term air pollution data are essential. This study analyzed the characteristics, lagged meteorological effects, and temporal patterns of six air pollutant concentrations over a one-year period at an urban residential site in Kenitra, Morocco. The results reveal pronounced seasonal and diurnal variations in pollutant levels, shaped by meteorological factors, emission sources, and local geographic conditions. PM_2.5, SO₂, and CO concentrations peaked during winter, while NO₂ and CO exhibited consistent diurnal peaks during morning and evening rush hours across all seasons, driven by traffic emissions and nocturnal pollutant accumulation. In contrast, O₃ concentrations were highest during summer afternoons due to photochemical reactions fueled by strong UV radiation, while winter levels were the lowest due to reduced sunlight. Lagged meteorological effects further highlighted the complexity of air pollutant dynamics. Meteorological factors, including temperature, wind speed, humidity, and pressure, significantly influenced pollutant levels, with both immediate and lagged effects observed. Lag analyses revealed that PM_2.5 and BC levels responded to wind speed, temperature, and humidity over time, highlighting the temporal dynamics of dispersion and accumulation. CO is sensitive to temperature and pressure changes, with delayed impacts, while O₃ formation was primarily influenced by temperature and wind speed, reflecting complex photochemical processes. SO₂ concentrations were shaped by both immediate and lagged meteorological effects, with wind direction playing a key role in pollutant transport. These findings emphasize the importance of considering both immediate and lagged meteorological effects, as well as seasonal and diurnal variations, in developing air quality management strategies.

1. Introduction

Air pollution has emerged as a pressing global health crisis, significantly impacting public health, environmental quality, and economic stability [1,2,3]. Among the various air pollutants, particulate matter (PM), particularly PM_2.5 (particles < 2.5 µm in diameter), along with ozone (O₃), carbon monoxide (CO), sulfur dioxide (SO₂), nitrogen oxides (NO_x), and black carbon (BC), are identified as some of the most dangerous to human health [4]. Therefore, the increasing rate of pollutants in the atmosphere has altered its composition, posing harm to humans and other living organisms [5]. The SoGA 2024 Report indicates that 8.1 million total deaths were due to air pollution in 2021: 30% of deaths were from lower respiratory infections, 28% were from ischemic heart disease, and 48% were from chronic obstructive pulmonary disease [6].

The public health implications of air pollution are particularly pronounced in low- and middle-income countries (LMICs), where rapid urbanization, industrial growth, and insufficient regulatory frameworks exacerbate exposure to harmful air quality [7,8,9]. The WHO reports that approximately 99% of the global population is exposed to air quality levels below recommended standards, with LMICs bearing the brunt of this crisis [10]. The toll of air-pollution-related deaths exceeds 6.5 million globally, with about 90% of these fatalities occurring in low-income settings [11]. Furthermore, the economic ramifications are equally alarming; in 2019, the costs associated with air pollution were estimated at USD 8.11 trillion, representing approximately 6% of the world’s Gross Domestic Product [12,13].

The interactions between different air pollutants and meteorological conditions play a crucial role in shaping air quality. The need for comprehensive high-resolution data over extended periods is vital for understanding the meteorological factors that influence air pollution levels and their temporal variations [14,15,16]. Seasonal and diurnal variations in pollutant concentrations have been widely documented; for instance, it is often observed that winter months experience higher levels of pollutants due to increased emissions from heating systems, stagnant atmospheric conditions, and meteorological phenomena that inhibit the dispersion of pollutants [17,18,19].

Despite the growing recognition of the critical role that meteorological factors play in modulating air pollution, a significant gap exists in the literature concerning the delayed and nonlinear impacts of these factors on pollutant concentrations. Most existing studies primarily focus on the immediate effects of meteorological variables on air quality, often neglecting the potential lag effects that could provide a more nuanced understanding of pollution dynamics. The lagged effects of meteorological parameters on air pollutants refer to the delayed impacts of weather conditions on pollutant concentrations [20]; for example, an increase in temperature can lead to a subsequent rise in ozone (O₃) levels with a delay of several hours to a few days, as it accelerates the chemical reactions involved in ozone formation [21]. Similarly, variations in wind speed can significantly influence the dispersion of fine particles (PM_2.5); however, low wind speeds can lead to the gradual accumulation of pollutants, with the impact potentially observable up to 24 h after the change [22]. Yang et al. [23] emphasized that the delayed effects of meteorological factors play a crucial role in the dispersion of PM_2.5 and O₃. Barmpadimos et al. [24] highlighted that accounting for lagged meteorology can significantly improve the understanding of relationships between PM₁₀ and meteorological factors by quantifying these relationships and addressing nonlinearity. Although Guo et al. [25] explored the lagged effects of precipitation on PM pollution in two Chinese cities, the delayed impacts of other meteorological factors, such as wind, relative humidity, and temperature, on PM_2.5 remain less understood. Regarding BC, Bounakhla et al. [26] reported a negative correlation with temperature on the sampling day and with relative humidity two days earlier, whereas air pressure three days earlier showed a positive correlation. For O₃, Yang et al. [23] argued that temperature, relative humidity, and sunshine duration play critical roles in influencing its concentrations. They concluded that the effects of relative humidity and sunshine duration were primarily observed within a five-day lag, while rainfall and temperature exerted significant impacts on the current day. Oversight of these lagged effects can lead to ineffective air quality management strategies and hinder accurate forecasting models. For example, studies have shown that changes in wind patterns may have delayed effects on pollution levels, as pollutants can travel significant distances before being deposited or dispersed [23,27,28].

In Morocco, the impact of meteorological conditions on air pollution is still not well-researched, even though there is growing evidence that weather factors significantly influence the dispersion and concentration of pollutants [26,29]. This gap underscores the need for localized studies to understand the specific air quality challenges faced by cities like Kenitra. In light of these challenges, this study aims to analyze the characteristics, lagged meteorological effects, and temporal patterns of six key air pollutants, including O₃, CO, SO₂, NO₂, PM_2.5, and BC at an urban residential site in Kenitra over one year (May 2023–April 2024). The primary aims of this research are to perform the following: (1) assess temporal trends in the concentrations of selected air pollutants at daily, monthly, and seasonal scales; (2) investigate the immediate and lagged impacts of meteorological parameters (wind speed, temperature, relative humidity, and atmospheric pressure) on pollutant concentrations; (3) explore inter-pollutant relationships to identify dependencies and shared emission sources; and (4) provide high-resolution, long-term air quality datasets to inform effective air quality management strategies for Kenitra city and similar urban areas.

2. Materials and Methods

2.1. Sampling Site Description

Kenitra is a coastal city in northern Morocco, located about 50 km from Rabat, the country’s capital, and bordered by the Atlantic Ocean (Figure 1). It is the fourth largest industrial city in Morocco [30] and is situated on the southern bank of the Sebou River, approximately 12 km from its mouth at Mehdia on the Atlantic coast. Kenitra experiences high temperatures during summer (from mid-August to September) and low temperatures in winter (from January to mid-February). The city is characterized by frequent winds and high relative humidity (>75%). Wind speeds are typically higher in summer and spring, averaging 3.4 m/s and 3.5 m/s, respectively, compared to 2.6 m/s in autumn and winter. Westerly and southwesterly winds were dominant during the sampling period, with a frequency exceeding 15%, while other wind directions were less significant (Figure 1d). Bounakhla et al. [26] highlighted seasonal variations in the directions of air masses affecting Kenitra. This study found that, during winter, most air masses came from the Atlantic Ocean, whereas in spring and summer, air masses from the Iberian Peninsula and northern Morocco significantly influenced air quality in the city.

The location of the sampling site is depicted in Figure 1. The urban residential site was chosen within the Ismailia district of Kenitra. The area is surrounded by a diverse range of establishments, including shops, snack vendors, traditional public bathhouses (hammams), modern and artisanal bakeries, and various services such as banks, reflecting the district’s dynamic socio-economic activity. The area features a modern urban grid layout with wide streets lined by multifunctional buildings that combine commercial spaces on the ground floor with residential units above. While the neighborhood maintains a lively atmosphere, the street layout generally ensures traffic flow, albeit with potential congestion during peak hours. A few modest green spaces and public squares provide areas for relaxation and social interaction within the bustling environment. Sampling and measurement instruments were installed on the exterior grounds of a private school (34.27° N, −6.61° W) located in downtown Kenitra. All instruments were placed approximately 40 m from the main road and 2 m above ground level.

2.2. Instrumentation and Sampling

A comprehensive measurement campaign was conducted from May 2023 to April 2024. Four criteria pollutants (CO, O₃, SO₂, and NO₂) were measured at a 30 min time resolution, while PM_2.5 concentrations were recorded every 24 h. Black carbon (BC) was measured using the Multi-Wavelength Absorption Black Carbon Instrument (MABI). All equipment used for the measurements of gaseous pollutants, particulate matter (PM), and black carbon belongs to the National Centre for Nuclear Energy, Science, and Technology (CNESTEN). Nitrogen dioxide (NO₂) was analyzed using a Chemiluminescence technique (Horiba, Kyoto, Japan, APNA-370) with a flow rate of 0.8 L/min and a minimum sensitivity detection of 0.5 ppb for concentrations lower than 0.2 ppm. Ozone (O₃) was analyzed using the Ultraviolet Photometry technique using an automatic analyzer type O341M (Environnement S.A., Poissy, France) with a flow rate of 1.5 L/min and a minimum sensitivity detection of 0.4 ppb. Carbon monoxide (CO) was analyzed using non-dispersive infrared analysis using an automatic analyzer type APMA-370 (HORIBA, Kyoto, Japan) with a flow rate of 1.5 L/min and a minimum sensitivity detection of 0.05 ppb for concentrations lower than 10.0 ppm. Sulfur dioxide (SO₂) was measured using Ultraviolet fluorescence. The performance features of the gaseous pollutant measurements were evaluated according to the following standards: NM EN 14211:2012 for NO, NO₂, NO_x [31], NM EN 14626:2012 for CO [32], and NM EN 14625:2012 for O₃ [33]. A detailed description of the gaseous pollutant measurement techniques and the specific analyzers employed for the comprehensive characterization of these pollutants can be found in Oujidi et al. [29].

A low-volume Echo PM sampler (Tecora Echo PM-TCR, Tecora, Italy) was used to collect fine particles (PM_2.5) on 47 mm diameter quartz filters (Whatman, Inc., Middlesex, UK, 2 µm pore size) at a flow rate of 1 m³/h (16.7 L/min). Gravimetric measurement determined PM_2.5 mass concentrations. Filters were conditioned in an oven at 100 °C for 24 h and stored in a desiccator for 48 h to minimize the influence of atmospheric moisture, and then were weighed before and after sampling using a Sartorius microbalance (ME5, Göttingen, Germany) with a sensitivity of 1 µg. PM_2.5 mass concentrations were calculated by dividing the net weight of collected particles by the sampled air volume.

Otherwise, a Multi-Wavelength Absorption Black Carbon Instrument (MABI) was utilized to measure light-absorbing carbon (LAC), also referred to as black carbon, at seven different wavelengths: 405 nm, 465 nm, 525 nm, 639 nm, 870 nm, 940 nm, and 1050 nm [34]. This method enables the differentiation between light-absorbing carbon originating from high-temperature fossil fuel combustion and biomass burning [35]. The MABI measures the light transmission through a PM_2.5 filter before and after sampling. PM_2.5 samples were collected using a separate Echo PM sampler. Before and after sampling, MABI scans were performed on each filter. Scans were performed on blank filters (unexposed filters) to determine the baseline absorption at each wavelength from the filter substrate, referred to as $I_0$. Scans were then repeated on the same filters after sampling (exposed filters) to determine the absorption at each wavelength from both the filter substrate and the collected particles. These data are referred to as $I$. Using these values ($I_0$ and $I$), the exposed filter area ($A$ in cm²), and the sampled air volume ($V$ in m³), the BC mass concentration (ng/m³) was calculated using Equation (1). This calculation relies on a mass absorption coefficient ($\epsilon$) specific to each wavelength and is expressed in Equation (2). Following the MABI manual and the study of Cohen et al. [12], the mass absorption coefficients are dependent on the types of filters used in PM sampling, and the PM size fraction and vary as a function of the wavelength $\lambda$ (nm).

(1)$\mathrm{B}\mathrm{C}\left(\mathrm{n}\mathrm{g}/{\mathrm{m}}^3\right)={\displaystyle \frac{{10}^5· A}{\epsilon · V}}·ln\left[{\displaystyle \frac{I_0}{I}}\right]$

(2)$\epsilon \left(\lambda \right)={a·\lambda }^{-b}$

where $a$ and $b$ are constants that are determined experimentally. In our analysis of the sampled PM_2.5 size fraction using quartz filters, the coefficients $a$ and $b$ were found to be 1203.679 and 0.79686, respectively.

2.3. Meteorological Data

The meteorological data for this study were obtained from the NOAA Integrated Surface Database (ISD), which is freely accessible via the National Climatic Data Center’s (NCDC) Climate Data Online (CDO) platform. The ISD provides historical weather and climate data along with station history information [36]. For this study, local meteorological data were recorded every six hours at the Kenitra Airport weather station (34.27, −6.57), located approximately 1 km from the study site. The dataset includes key variables such as wind speed (WS), wind direction (WD), relative humidity (RH), air pressure (P), and air temperature (T). The R package “worldmet” was used to access the ISD (https://www.ncei.noaa.gov/products/land-based-station/integrated-surface-database; accessed on 20 April 2024) [37].

2.4. Statistical Analysis

Descriptive statistics, including minimum, maximum, and mean ± standard deviations, were calculated for gaseous pollutants, particulate matter, black carbon, and meteorological variables using Microsoft Excel. To ensure data quality and reliability, only data points within the 1st and 99th percentiles of the gaseous pollutant concentrations were considered for analysis. This approach effectively eliminated outliers and anomalous values, such as negative readings or isolated extreme spikes. As a result, a small portion of the data (less than 1%) for Kenitra were excluded from the study. The remaining valid data were used to calculate hourly average values, which served as the foundation for subsequent analyses. To visualize temporal trends in gaseous pollutant concentrations, time series plots were generated using the “Openair” package in R statistical software (version 4.3.3) [38].

To capture the lagged effects of meteorological parameters, we employed a Distributed Lag Nonlinear Model (DLNM) within a Generalized Linear Modeling (GLM) framework. The DLNM is a widely used method in environmental studies to uncover the nonlinear relationships between air pollutants, meteorological factors, and their delayed effects [23]. This study considered four main meteorological factors: relative humidity, temperature, wind speed, and air pressure. The GLM framework was chosen because it allows for flexibility in modeling the response variable, such as pollutant concentrations, which may follow non-normal distributions. Within this framework, the DLNM provides a way to model lagged effects across multiple time lags, captures the nonlinear relationships of the effects of meteorological variables on pollutant concentrations, and uses a cross-basis function to combine both the nonlinear effects of the meteorological variables and their lagged effects simultaneously [39]. DLNM was implemented using the R package “dlnm” (version 4.1.3, https://cran.r-project.org/package=dlnm; accessed on 20 April 2024) [40] in conjunction with the “mgcv” package [41] to construct flexible models based on five air pollutants and the four meteorological variables.

3. Results and Discussion

3.1. Overview of Criteria Air Pollutant Levels and Meteorological Status

Table 1 shows the descriptive statistics of the concentrations of the main pollutants and meteorological variables in Kenitra city between May 2023 and April 2024 for 351 sampling days. The meteorological data show that Kenitra is characterized by high temperatures in summer, with a daily mean of 23.6 °C ranging from 18.4 to 35.9 °C, followed by a warm autumn and a rather cold winter with respective temperatures of 19.4 °C and 13.7 °C. In spring, the average temperature is around 17.9 °C. The city is also known for its high relative humidity, ranging from 74% in summer to 83.4% in winter. Moreover, the wind speed is higher in spring, 3.8 m/s, against 2.6 m/s in both autumn and winter. The highest temperatures were experienced in August.

The study period revealed an average PM_2.5 concentration of 22.9 ± 11.6 µg/m³, with monthly extremes ranging from a high of 79.8 µg/m³ in December to a low of 6.3 µg/m³ in June. Black carbon (BC) concentrations averaged 7.3 ± 5.5 µg/m³, peaking at 11.4 ± 6.0 µg/m³ in winter and reaching a low of 3.0 ± 1.4 µg/m³ in summer. For gaseous pollutants, mean mass concentrations were recorded as follows: carbon monoxide (CO) at 2886 ± 161 µg/m³, sulfur dioxide (SO₂) at 6.6 ± 3.6 µg/m³, nitrogen dioxide (NO₂) at 20.0 ± 4.1 µg/m³, and ozone (O₃) at 9.8 ± 2.6 µg/m³. Further details are presented in Table 1.

At the national level, the average PM_2.5 concentration reported in this study (22.9 ± 11.6 µg/m³) is comparable to that found in Nador (22.2 ± 11.6 µg/m³) [29]. However, in comparison to other Moroccan cities, PM_2.5 levels in Tetouan and Salé are notably lower at 17.96 µg/m³ [42] and 17.30 µg/m³ [43], respectively. Furthermore, comparative studies with international cities reveal varying PM_2.5 levels: lower in Barcelona, Spain, and Marseille, France [44], and higher in Venice, Italy [45], Kigali, Rwanda [46], Cairo, Egypt [47], and Thessaloniki, Greece [45]. Regarding BC levels, a recent study by Bounakhla et al. [26] highlighted significant seasonal variations in BC concentrations in urban Kenitra, Morocco, between July 2020 and April 2021, with peak levels reaching 6.3 µg/m³ during summer. In comparison, the average BC concentrations recorded in the closed city of Salé (30 km from Kenitra) were lower, at 1.9 ± 2.2 µg/m³, as reported by Otmani et al. [48,49]. In a Mediterranean context, Benchrif et al. [50] found an average BC concentration of 3.14 µg/m³ in Tetouan from May 2011 to April 2012. Furthermore, Oujidi et al. [29] reported average daily BC concentrations of around 7 µg/m³ in the Nador region, Morocco, between 2016 and 2018, alongside levels of CO (500 µg/m³), NO₂ (55 µg/m³), and O₃ (16 µg/m³). Additionally, a prior study conducted by Ait Bouh et al. [51] in urban Meknes, Morocco, highlighted significant concentrations of SO₂ in the ambient air, ranging from 10 to 50 µg/m³.

3.2. Temporal Variability in Air Quality Pollutants

3.2.1. PM_2.5 and BC

The monthly variation in PM_2.5 concentrations in Kenitra (Figure 2) demonstrates significant seasonal variations (one-way ANOVA test, p-value < 0.001). Monthly PM_2.5 concentrations show relatively seasonal dependent levels throughout the year, ranging from a minimum of 12.6 ± 4.2 µg/m³ in June to a maximum of 33.8 ± 14.7 µg/m³ in January. The overall mean concentration of PM_2.5 was 22.9 ± 11.6 µg/m³. A similar trend was observed for BC levels, with a maximum concentration of 14.6 ± 6.4 µg/m³ in January and a minimum of 2.6 ± 0.9 µg/m³ in July, reflecting the seasonal variability of these pollutants. Several factors contribute to the elevated levels of PM_2.5 and BC in Kenitra, particularly during the winter months, including increased traffic-related activities. Previous studies have found that local sources, such as vehicle emissions, socio-economic activities, and industry-related combustion strongly influence BC levels and their variability in Kenitra [52]. These are also closely linked to meteorological conditions, such as boundary layer height. For PM_2.5, variations are driven by a combination of local atmospheric conditions and the contribution of regional and long-range emission sources [26]. Winter months in coastal cities such as Kenitra are characterized by lower temperatures and diminished wind activity, which contribute to the stagnation of pollutants near the surface (Table 1) [53]. This trend in BC pollution during winter is consistent with findings from studies conducted in other urban areas, where increased vehicular emissions are linked to elevated levels of BC levels [26,54,55,56].

Additionally, the observed positive correlation between relative humidity and PM_2.5 concentrations suggests that RH may play a role in influencing PM_2.5 levels. As RH increases, existing studies indicate that processes such as aqueous-phase reactions and gas-particle partitioning associated with water uptake could potentially enhance the formation of secondary inorganic species and secondary organic aerosols, contributing to elevated PM_2.5 concentrations [57]. This phenomenon has been reported to be particularly pronounced during winter months, when higher RH conditions may promote the hygroscopic growth of particulate matter, including substances like sodium chloride, which are known to absorb moisture and expand at relative humidity levels as low as 70% [58]. Furthermore, increased RH has been suggested to facilitate photochemical reactions of atmospheric pollutants, potentially leading to the formation of new particulate matter and exacerbating air pollution [57].

3.2.2. Carbon Monoxide

Figure 3a illustrates the hourly distribution of carbon monoxide (CO) concentrations, revealing distinct patterns. Hourly average CO values range from 2778 ± 111 µg/m³ at 5 p.m. (local time) to 3016 ± 365 µg/m³ at noon. OC concentrations revealed a clear bimodal diurnal pattern, corresponding to early morning (6 a.m.) and evening (6 p.m.) traffic rush hours [12,59]. Conversely, minimum concentrations of around 2800 ± 170 µg/m³ are observed in the late night and early afternoon, potentially reflecting improved atmospheric dispersion conditions associated with higher daytime temperatures and wind speeds [60,61]. The primary sources of CO in urban environments are motor vehicle emissions and industrial activities, both of which intensify in winter due to increased fuel combustion [62,63]. A more detailed examination of hourly trends in Figure 3b reveals a W-shaped pattern, with two distinct peaks throughout the day. This pattern aligns with daily human activities, primarily related to vehicle use [64,65]. However, notable differences emerge between weekdays and weekends, as depicted in Figure 3d. Weekday CO levels exhibit regular morning and evening peaks, corresponding to rush hour traffic. In contrast, weekend CO levels decrease, likely due to reduced traffic and industrial activity, such as brickworks or cardboard factories, a common observation in urban pollution studies [66,67]. The diurnal cycle of CO concentrations, characterized by morning and evening peaks, is directly linked to road traffic pollution [68]. Additionally, industries and combustion processes contribute significantly to CO emissions [69].

Figure 3c presents the monthly variation in average CO concentrations, showcasing a U-shaped pattern. The lowest concentrations, around 2750 ± 67 µg/m³, occur between April and August. From September onwards, CO levels gradually increase, peaking at approximately 3050 ± 227 µg/m³ in December. This seasonal trend is influenced by winter weather conditions, which often lead to stagnant atmospheric conditions, low wind speeds, and reduced temperature inversions. These meteorological factors hinder the dispersion of pollutants, resulting in elevated CO concentrations and poor air quality events [70]. The correlation between CO levels and colder months suggests that atmospheric conditions, such as temperature inversions, play a critical role in trapping pollutants close to the ground, exacerbating air quality issues [71].

Figure 3e shows the hourly variations in average CO concentrations for each season. In spring, CO concentrations range between 2800 and 2900 µg/m³, with two peaks: a prominent one in the morning (around 8 a.m.) and a less pronounced one in the evening. These peaks are most likely linked to road traffic and the biomass combustion processes. In summer, concentrations are the lowest of the year (from 2850 to 2950 µg/m³) and remain relatively stable throughout the day, due to increased photo-degradation and better pollutant dispersion [69,72]. In autumn, CO levels rise to their highest (from 2900 to 3100 µg/m³), with significant morning and evening peaks, reflecting intensified human activities. In addition, in winter, CO concentrations, ranging from 2800 to 3050 µg/m³, also exhibit pronounced hourly peaks. These are likely driven by increased combustion activities, and fires at a nearby public landfill could also be a source. Winter thermal inversions further exacerbate these levels by limiting pollutant dispersion [73,74].

3.2.3. Sulfur Dioxide

The monthly variation in mean sulfur dioxide (SO₂) concentrations in Kenitra, Morocco, peaked in December (10.7 ± 0.8 µg/m³) and January (10.2 ± 0.6 µg/m³). In contrast, the lowest concentration was observed in May (0.3 ± 0.02 µg/m³) (Figure 4c). While SO₂ can generally act as a signature pollutant of industrial emissions in urban areas [75,76], its seasonal trend is likely driven by variations in meteorological and diffusion conditions in Kenitra City.

An hourly analysis of SO₂ concentrations revealed relatively stable levels throughout the day, with slight fluctuations (Figure 4b). Similarly, the day-of-the-week distribution showed minimal variation, ranging from 6.7 ± 3.5 µg/m³ on Friday to 6.9 ± 3.7 µg/m³ on Monday (Figure 4d). Hourly comparisons across the week further emphasized this consistency, with concentrations averaging around 7 ± 4 µg/m³ (Figure 4a).

3.2.4. Nitrogen Dioxide

Figure 5 illustrates the variation in nitrogen dioxide (NO₂) concentrations in Kenitra, Morocco, across different timescales. The monthly average concentration of NO₂ peaked in May at 22.7 ± 3.7 µg/m³ and in August, with a concentration of 21.9 ± 8.2 µg/m³, while it reached the lowest level in July at 14.7 ± 2.7 µg/m³. These peaks are probably influenced by traffic fluctuations, particularly during summer vacation periods, when emissions increase at the beginning and end of vacations [77]. The hourly distribution of NO₂ shows two pronounced peaks, one at 7 a.m. and the other at 11 p.m., suggesting that emissions are closely linked to road traffic [78,79]. Located near an industrial zone that is known for its continuous activities, the city of Kenitra is significantly impacted by the 3 × 8 work system, where three shifts alternate every 8 h to cover a full day. However, this system causes considerable traffic congestion, as shift change times, typically set at 7 a.m., 3 p.m., and 11 p.m., often coincide with peak hours, leading to increased congestion and overcrowding on the main roads during these periods [80]. As depicted in Figure 5, daily mean NO₂ concentrations exhibited a consistent pattern throughout the week, with relatively stable levels from Monday to Friday, except for a peak on Thursday. A statistically significant reduction in mean concentrations was observed during the weekend. This decrease can be primarily attributed to reduced vehicular traffic compared to weekdays. These findings align with a recent study conducted in Morocco by Oujidi et al. [29], which highlighted a pronounced variation in NO₂ concentrations between weekdays and weekends. Significantly higher NO₂ levels were reported on weekdays, underscoring the substantial impact of road traffic-related activities on air quality. Additionally, special peaks in NO₂ concentrations were recorded on Thursday, reaching 30 ± 18.4 h µg/m³ between 5 a.m. and 10 a.m., although other days displayed two peaks: morning and late night. This particularity could be attributed to increased road traffic on that day, linked to the weekly local market, a dynamic place where locals gather to trade a variety of products. These observations highlight the significant impact of local activities, particularly emissions related to traffic, on the variations in NO₂ levels throughout the week [81].

On the other hand, the hourly variation in NO₂ concentrations in both spring and summer is shown in Figure 5e. In summer, NO₂ concentrations significantly drop between 11 a.m. and 4 p.m. due to intense photolysis facilitated by strong sunlight, with the most significant decrease occurring around noon (12 p.m.–1 p.m.). However, marked peaks are observed during morning rush hours (from 6 a.m. to 9 a.m.) and evening rush hours (from 7 p.m. to 10 p.m.), driven by road traffic and nocturnal pollutant accumulation [82]. Moderate winds and atmospheric instability enhance pollutant dispersion during the day. However, in spring, NO₂ levels are slightly higher than in summer, with moderate photolysis reducing concentrations between 10 a.m. and 4 p.m. compared to the peaks during morning (from 6 a.m. to 9 a.m.) and evening (from 6 p.m. to 11 p.m.). Overall, NO₂ peaks in colder months due to limited photolysis and temperature inversions, while summer shows reduced daytime concentrations due to strong photolytic activity. Morning and evening traffic-related peaks are consistent across both seasons.

3.2.5. Ozone

Regarding O₃, the monthly distribution of averages showed a minimum concentration in June at approximately 6.2 ± 2.3 µg/m³, followed by a gradual increase, peaking in December at 11.6 ± 2.1 µg/m³ (Figure 6). O₃ concentrations were significantly low during the colder months (from January to March), likely due to reduced solar intensity and temperatures, which are key factors in the photochemical production of ozone through reactions between nitrogen oxides (NO_x) and volatile organic compounds (VOCs) [83,84]. A pronounced peak was observed in June, reaching around 8 µg/m³, typical of urban and suburban areas where ozone precursors accumulate under favorable meteorological conditions, such as prolonged sunlight and stagnant air. After this peak, ozone concentrations decline as solar intensity decreases, returning to lower levels in late autumn and winter [85,86,87]. However, an exceptionally high temperature recorded in Kenitra between August and December 2023, reaching 38 °C, may be attributed to the elevated O₃ levels during the winter season [88]. This seasonal cycle is well-documented in the scientific literature, with several studies showing that ozone concentrations are directly correlated with temperature and solar radiation [89]. NO₂ is a precursor to ground-level O₃ formation and contributes significantly to the formation of secondary particulate matter [90].

The hourly variation in O₃ concentrations closely follows the day–night cycle, with maximum values observed between 9 a.m. and 5 p.m., followed by a significant drop during the night. This pattern is consistent with the photochemical nature of ozone formation, which intensifies during periods of high sunlight and elevated temperatures. Specifically, when solar radiation increases significantly, the photolysis of nitrogen dioxide initiates, leading to a decrease in its concentration (as evident in Figure 5) and subsequently facilitating the formation of O₃ [91]. Daily mean O₃ concentrations remained relatively stable from Monday to Friday around a relatively stable average of 8.1 ± 4.8 µg/m³, followed by a statistically significant decrease on weekends. In Kenitra, this weekend effect is attributed to slight reductions in NO₂ emissions from vehicular and anthropogenic activities, which act as ozone precursors in urban areas. Sicard et al. [92] reported that a substantial decline in nitrogen oxide emissions can lead to an increase in the VOC-to-NO_x ratio, favoring O₃ formation. However, they found an inverse relationship: a significant decrease in the mean daily O₃ concentration was associated with a significant decrease in the VOC-to-NO_x ratio. This suggests a complex interplay of factors influencing O₃ formation and destruction in our specific urban environment. This stability in ozone concentrations contrasts with the more variable pattern of NO₂ levels, highlighting ozone’s dependence on weather conditions rather than direct emissions [93]. In summary, O₃ levels follow a typical diurnal pattern dictated by sunlight and temperature, with maximum concentrations in the afternoon and minimal variations across weekdays. These results underscore the complex interaction between pollutants and weather conditions, with NO₂ serving both as a directly emitted pollutant and as a precursor to ozone formation, a relationship confirmed by the observed inverse correlation between the two pollutants [94].

The seasonal dynamics of O₃ are shown in Figure 6e. The hourly variation exhibits that, during spring, O₃ concentrations slightly increase throughout the day, reaching a moderate peak of about 8 µg/m³ between 12 p.m. and 6 p.m. This is due to the moderate photolysis of NO₂, facilitated by average sunlight during this season [95]. However, in summer, influenced by stronger UV radiation, ozone concentrations peak at around 10 µg/m³ at 3 p.m. This likely enhances the photolysis of NO₂ and stimulates the production of tropospheric ozone. This increase is also supported by higher emissions of precursors such as NO_x and VOCs from road traffic [72,96]. In autumn, the concentrations show more marked variations, with a notable peak of about 18 µg/m³ recorded in the afternoon (from 12 p.m. to 6 p.m.). This increase is attributed to the exceptionally high temperatures in 2023, reaching up to 38 °C, which intensified NO₂ photolysis and favored increased ozone production. These abnormally high temperatures, combined with periods of atmospheric stagnation, limited pollutant dispersion and promoted their accumulation. Additionally, the reduced autumn precipitation in 2023 in Kenitra contributed to limiting the wet deposition of ozone, thus maintaining high concentrations [97]. In winter, concentrations are relatively low, ranging from 5 to 10 µg/m³, with a modest peak around noon. This decline is explained by reduced solar radiation, decreasing NO₂ photolysis, and the high frequency of wintertime temperature inversions, which limit vertical exchanges and hinder pollutant dispersion [93].

3.3. Bivariate Plots: Potential Local Sources

Bivariate polar graphs, plotted as a function of wind speed and direction, were employed to assess the influence of various meteorological conditions and emission sources on pollutant concentrations. The dominant meteorological regime in Kenitra is characterized by prevailing winds from southwestern directions, typically with speeds between 6 and 7 m/s (Figure 7). Figure 7 the average PM_2.5 and BC concentrations associated with different wind directions and speeds. The color scale represents the concentration levels, with warmer colors indicating higher concentrations. The plot shows that the highest PM_2.5 concentrations are observed with winds from the west and northeast directions, particularly at high wind speeds. Conversely, winds from the east are associated with lower PM_2.5 concentrations, indicating that these directions might be linked to cleaner air masses. Furthermore, Spearman’s rank correlation coefficient of −0.3 (p-value < 0.1) was observed between PM_2.5 concentrations and wind speed, indicating a weak but statistically significant negative association. However, this correlation alone cannot definitively establish a causal relationship, particularly that stronger winds directly lead to PM_2.5 dispersion and reduced concentrations. Further analysis, as discussed below, reveals both the positive and negative lagged effects of wind speed on PM_2.5 concentrations, suggesting a more complex interaction than a simple inverse relationship.

On the other hand, the plot (right panel) reveals a clear pattern where higher BC concentrations are associated with winds from the east and southeast directions, particularly at lower wind speeds. This suggests that emissions sources located in these directions might be contributing to elevated BC levels in the study area. Conversely, winds from the west and southwest are associated with lower BC concentrations, indicating that these directions might be linked to cleaner air masses. The reduction in BC levels associated with the west winds can be attributed to the influence of marine air, which disperses particles and dilutes pollution. However, in PM_2.5, the sea salt component can contribute significantly to increased mass concentrations. Recently, Benchrif et al. found that sea salt accounts for approximately 6% of the PM_2.5 mass in Tetouan, Morocco, highlighting its notable role as a contributor to particulate matter in coastal regions [50].

Figure 8 provides valuable insights into the relationship between wind direction, wind speed, and the concentrations of various pollutants, including O₃, NO₂, SO₂, and CO. Across all pollutants, a striking trend emerges: higher concentrations consistently align with winds originating from the east, particularly at lower wind speeds. This suggests the presence of significant emission sources, such as heavy traffic corridors, industrial zones, or other anthropogenic activities concentrated in these directions. For instance, the elevated NO₂ concentrations (panel (a)) carried by easterly winds point to urban and industrial emissions, likely from traffic and fossil fuel combustion [98,99]. On the other hand, winds from the west, bringing cleaner air masses from the Atlantic Ocean, correspond to lower NO₂ levels. Remarkably, as wind speed increases, NO₂ concentrations decline, illustrating the cleansing effect of stronger winds in dispersing pollutants [100,101]. A similar pattern emerges for CO (panel (b)), where higher concentrations are linked to easterly winds. This further supports the presence of significant emission sources in these directions, likely related to traffic or industrial activities [102,103]. The west and northwest winds, however, deliver noticeably cleaner air, suggesting minimal pollutant contributions from these directions. Furthermore, for SO₂ (panel (c)), the plot reveals higher concentrations in almost all sectors, indicating the presence of SO₂ emission sources in these areas [104], and the spatial pattern is less pronounced compared to NO₂ and CO. Nevertheless, O₃ concentrations (panel (d)) reveal an interesting pattern. Elevated levels align with winds from the northeast, where ozone precursors such as NO_x and VOCs—emitted from urban and industrial sources—undergo photochemical reactions. Conversely, marine winds from the west and northwest dilute these precursors, resulting in lower ozone levels. Overall, coastal breezes from the west direction likely contribute to improved air quality [105,106,107]. The beneficial impact of marine air on air quality in coastal cities is well-established, as evidenced by studies such as Viana et al. [108], who highlight the potential for reduced airborne particle concentrations due to the proximity to the ocean.

3.4. Lagged Effects of Meteorology

To assess the lagged effects of meteorological parameters (temperature, wind speed, relative humidity, and atmospheric pressure) on various air pollutants (PM_2.5, BC, CO, O₃, and SO₂), a Distributed Lag Nonlinear Model (DLNM) within a Generalized Linear Modeling (GLM) framework was employed. To determine the optimal model complexity, a sensitivity analysis was conducted using GLM with varying degrees of freedom (3, 5, and 7). The model with a degree of freedom of seven exhibited the lowest residual deviance and Akaike Information Criterion (AIC), indicating the best fit to the data while balancing model complexity. This analysis allowed for the assessment of how meteorological parameters influence air pollutant concentrations, accounting for lagged effects extending up to seven days prior.

3.4.1. Particulate Matter

The analysis of meteorological parameters reveals significant lagged effects on PM_2.5 concentrations, indicating complex and time-dependent relationships. Temperature exhibits mixed influences, with some lagged coefficients being positive and others negative, although most are statistically insignificant (p-value > 0.05). However, a notable exception is a significant negative relationship at lag 5 (β = −110.3, p-value = 0.0025), suggesting that decreases in temperature five days prior may contribute to reduced PM_2.5 levels. Wind speed emerges as a crucial factor, demonstrating both positive and negative lagged effects [109]. Specifically, wind speeds from 5 to 7 days prior are associated with elevated PM_2.5 levels (lag 5: β = 67.3, p-value = 0.0137; lag 6: β = 42.8, p-value = 0.0227; lag 7: β = 55.4, p-value = 0.0143). Conversely, wind speed at lag 3 (β = −78.5, p-value = 0.011) is associated with a potential reduction in PM_2.5 due to enhanced dispersion. Relative humidity, atmospheric pressure, and wind direction exhibit weaker or inconsistent impacts on PM_2.5 concentrations. A potential borderline negative effect of relative humidity at lag 2 (β = −136.7, p-value = 0.071) needs further investigation. Recently, Bounakhla et al. [26] showed that the most significant meteorological effects on PM typically occurred on the first day and rapidly decreased throughout the lag period. However, they observed that the lagged effect of meteorology on PM pollution varies depending on the meteorological variable.

3.4.2. Black Carbon

The GLM analysis indicates the significant lagged effects of meteorological variables on BC concentrations. Specifically, temperature variations demonstrate both immediate and lagged effects on BC levels: a significant negative effect is observed at short lags (lag 1, β = −139.16, p-value < 0.001), while a positive effect emerges at longer lags (lag 7, β = 128.03, p-value < 0.001). Wind also plays a crucial role, exhibiting positive effects at short to medium lags (lag 1, β = 50.48, p-value < 0.001; lag 2, β = 33.39, p-value < 0.001) and a negative impact at longer lags (lag 3, β = −37.56, p-value = 0.02). Humidity displays a more complex relationship, initially reducing BC concentrations at short lags (lag 1, β = −222.98, p-value < 0.001), but leading to an increase at longer lags (lag 7, β = 123.56, p-value < 0.001). These findings suggest that meteorological factors significantly influence BC concentrations not only in real time, but also through lagged effects, likely driven by varying atmospheric conditions [110,111]. A previous study in Kenitra used univariate correlation analyses, assuming a linear relationship between meteorology and pollutants [26]. They found that BC concentration exhibited a negative correlation with temperature on the day of measurement and with relative humidity two days prior, but only during the summer. Conversely, air pressure three days prior showed a positive correlation with BC concentration during the autumn months.

3.4.3. Carbon Monoxide

The analysis of the relationship between CO levels and selected meteorological variables revealed significant associations between temperature, humidity, and atmospheric pressure at specific lags. Temperature showed a significant negative association with CO levels at lag 4 (β = −633.23, p-value = 0.018), suggesting that lower temperatures at this point are associated with reduced CO levels. Conversely, at lag 6, a significant positive relationship was observed (β = 2062.89, p-value = 0.019), indicating that warmer temperatures later correlate with higher CO levels. Humidity demonstrated significant positive associations with CO at lag 1 (p-value = 0.048), lag 2 (p-value = 0.043), lag 5 (p-value = 0.017), and lag 6 (p-value = 0.032). These findings imply that increased humidity at these time points is linked to elevated CO concentrations. Additionally, atmospheric pressure at lag 4 exhibited a significant positive impact on CO levels (β = 684.91, p-value = 0.037). In contrast, wind speed did not show consistent or statistically significant effects on CO levels, with lag 7 yielding a weak correlation (β = −292.09, p-value = 0.171).

3.4.4. Ozone

The generalized linear model with DLNM revealed the significant lagged effects of meteorological variables on ozone concentrations. Specifically, temperature exhibited a complex relationship, with positive effects at longer lags (e.g., lag 5: β = 27.7011, p-value < 0.0001), suggesting that warmer temperatures can lead to increased ozone production over time. However, negative effects were observed at shorter lags (e.g., lag 3: β = −9.1678, p-value = 0.0266), possibly due to initial dilution or other short-term meteorological factors. Wind speed generally had a negative impact on ozone, especially at earlier lags (e.g., lag 1: β = −6.1259, p-value = 0.0018), suggesting that stronger winds can disperse ozone. However, the relationship became less clear at later lags, likely influenced by changes in wind direction or other factors. Humidity consistently showed a negative association with ozone, especially at longer lags (e.g., lag 3: β = −78.4469, p-value < 0.0001), indicating that higher humidity can suppress ozone formation. Atmospheric pressure also had significant negative effects at certain lags (e.g., lag 4: β = −15.4907, p-value < 0.0001), suggesting that lower pressure might be associated with increased ozone formation under specific conditions. Similar significant influences of temperature, relative humidity, and wind speed on O₃ concentrations were observed by Yang et al. [23]. They highlighted the positive effects of temperature observed on the current day, while relative humidity showed negative effects five days earlier (lag 5).

3.4.5. Sulfur Dioxide

The GLM revealed significant lagged effects of meteorological variables on SO₂ concentrations. Regarding temperature variation, contrasting effects were exhibited. Higher temperatures at lag 7 (β = 3.645, p-value = 0.0007) were associated with increased SO₂ levels. In contrast, at lag 5, a significant negative association was observed with SO₂ concentrations (β = −6.118, p-value = 0.0085). Wind speed had both positive and negative impacts, with higher wind speeds at certain lags (e.g., lag 2: β = 2.944, p-value = 0.036) increasing SO₂, while higher wind speeds at other lags (e.g., lag 3: β = −6.580, p-value = 0.030) decreased SO₂. Relative humidity significantly influenced SO₂ levels, with higher humidity at lag 7 (β = 13.787, p-value = 0.0005) being strongly associated with increased SO₂ levels, while higher humidity at lag 6 (β = −9.327, p-value = 0.042) decreased SO₂. Lastly, higher atmospheric pressure at lag 1 was associated with lower SO₂ concentrations (β = −6.469, p-value = 0.0002).

4. Conclusions

Simultaneous samplings of fine particulate matter (PM_2.5), its associated black carbon (BC) component, and four gaseous pollutants (SO₂, NO₂, O₃, and CO) were conducted over a one-year period in the urban residential area of Kenitra, Morocco. The analysis revealed complex temporal patterns influenced by meteorological factors, human activities, and local geographic conditions. Nitrogen dioxide (NO₂) and ozone (O₃) displayed clear seasonal and diurnal variations. NO₂ concentrations peaked during rush hours, on Thursdays, and in colder months, driven by traffic emissions and stagnant weather conditions. In contrast, O₃ levels were higher in warmer months, attributed to photochemical reactions fueled by sunlight and elevated temperatures. Carbon monoxide (CO) exhibited diurnal and seasonal patterns, with higher concentrations during winter and traffic peak hours. PM_2.5 and BC concentrations also followed seasonal trends, with elevated levels in winter due to increased emissions and meteorological conditions that promote pollutant accumulation. Pollutant levels were influenced by wind patterns, with easterly and southeasterly winds associated with higher pollutant concentrations, while westerly and northwesterly winds brought relatively cleaner air.

The assessment of the lag effects of meteorological parameters on various air pollutants emphasizes the complex temporal dynamics of air quality. For instance, PM_2.5 concentrations exhibit delayed responses to wind speed and temperature, highlighting the temporal nature of atmospheric dispersion and pollutant accumulation processes. BC concentrations are influenced by both immediate and lagged meteorological effects, with wind speed and relative humidity playing crucial roles in shaping BC levels. Furthermore, CO concentrations are sensitive to temperature, relative humidity, and pressure, with significant impacts observed at specific lags, indicating that changes in these parameters can influence CO levels over time. Ozone concentrations are influenced by a combination of meteorological factors, including temperature, wind speed, and humidity, with effects manifesting at multiple lags, suggesting that O₃ formation and transport are complex processes affected by past meteorological conditions. Finally, SO₂ concentrations are impacted by a variety of meteorological factors, including wind speed, direction, temperature, and humidity, with both immediate and lagged effects.

These findings emphasize the importance of considering both immediate and lagged meteorological influences when assessing air quality, as the effects of environmental conditions on pollutant concentrations can persist over time.

Author Contributions

Conceptualization, methodology, formal analysis, investigation, data curation, writing—original draft preparation, visualization, M.Z., A.B., M.T., M.E, M.D. and A.A.; writing—review and editing, M.Z., A.B. and M.T.; resources, all authors; supervision, M.L.O., A.A., M.T. and A.Q.; project administration, M.T. and A.B. All authors have read and agreed to the published version of the manuscript.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The datasets generated and analyzed during the current study are not publicly available but are available from the corresponding author upon reasonable request.

Conflicts of Interest

The authors declare no conflicts of interest.

Footnotes

Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Figures and Table

Figure 1. Location of the air pollutant measurement site in Kenitra (red pin) in the Atlantic region (panel (a)) next to the main road linking the city center to Mehdia beach and not far from the air base (panel (b)), not far from Kenitra city center (panel (c)). Wind rose map based on hourly averages of wind speed and direction (panel (d)). Note the different map scales.

Figure 2. Average monthly concentration variation in PM2.5 (upper panel) and BC (lower panel) during the period from May 2023 to April 2024.

Figure 3. Average hourly (a,b), monthly (c), and daily (d) concentration variation (in µg/m3) of CO during the period from May 2023 to April 2024. (e) Hourly variation in average Carbon monoxide concentrations (in µg/m3) by season.

Figure 4. Average hourly (a,b), monthly (c), and daily (d) concentration variations (in µg/m3) in SO2 during the period from May 2023 to April 2024.

Figure 5. Average hourly (a,b), daily (d), and monthly (c) concentration variation (in µg/m3) of NO2 during the period from May 2023 to April 2024. (e) Hourly variation in average nitrogen dioxide concentrations (in µg/m3) by season.

Figure 5. Average hourly (a,b), daily (d), and monthly (c) concentration variation (in µg/m3) of NO2 during the period from May 2023 to April 2024. (e) Hourly variation in average nitrogen dioxide concentrations (in µg/m3) by season.

Figure 6. Average hourly (a,b), monthly (c), and daily (d) concentration variation (in µg/m3) of O3 during the period from May 2023 to April 2024. (e) Hourly variation in average ozone concentrations (in µg/m3) by season.

Figure 6. Average hourly (a,b), monthly (c), and daily (d) concentration variation (in µg/m3) of O3 during the period from May 2023 to April 2024. (e) Hourly variation in average ozone concentrations (in µg/m3) by season.

Figure 7. Polar plot of mean levels of PM2.5 (left panel) and BC (right panel) concentrations (in μg/m3) by wind direction and speed.

Figure 8. Polar plot of mean concentrations (in μg/m3) of nitrogen dioxide (a), carbon monoxide (b), sulfur dioxide (c), and ozone (d) by wind direction and speed.

Table 1

Descriptive statistics of the mean (±standard deviation), minimum (min), and maximum (max) concentrations of the measured pollutants and meteorological parameters (T (temperature), P (pressure), RH (relative humidity), WD (wind direction), and WS (wind speed)) during the sampling period in Kenitra city. NSL and n.a. stand for National Standards Limits and not available, respectively.

	Spring		Summer		Autumn		Winter		Entire Period		NSL
	Average ± SD	Min–Max	Average ± SD	Min–Max	Average ± SD	Min–Max	Average ± SD	Min–Max	Average ± SD	Min–Max
PM2.5 (µg/m3)	20.4 ± 8.6	7.9–39.9	15.9 ± 6.2	6.3–32.3	23.7 ± 11.5	8.4–64.5	28.9 ± 12.9	10–79.8	22.9 ± 11.6	6.3–79.8	35
BC (µg/m3)	3.9 ± 2	1.1–11	3± 1.4	1.1–9.1	8.2 ± 4.3	1.2–21.8	11.4 ± 6	1.1–28.5	7.3 ±5.5	1.1–28.5
O3 (µg/m3)	7 ± 1.6	3.9–11.3	6.7 ± 1.5	3.8–10	9.8 ± 2.6	4.2–17.4	8.3 ± 2.8	3–16.3	8.1 ± 2.6	3–17.4	110
SO2 (µg/m3)	5.2 ± 4.6	0.3–10.8	2.7 ± 1.6	0.3–5.6	7.3 ± 1.6	4.8–10.6	10.1 ± 0.8	8.4–12.5	6.6 ± 3.6	0.3–12.5	75
CO (µg/m3)	2779 ± 69.4	2652–3043	2826 ± 60.8	2727–3013	2891 ± 101.9	2726–3218	2984 ± 221.7	2671–3665	2886 ± 160.7	2652–3665	7000
NO2 (µg/m3)	20 ± 4.1	12.7–31.6	18 ± 6.7	9.6–41.6	n.a.	n.a.	n.a.	n.a.	18.6 ± 5.5	9.6–41.6	50
WS (m/s)	3.8 ± 1.5	1.9–8.1	3.3 ± 0.7	2.3–5.7	2.6 ± 1.2	0.8–7.8	2.6 ± 1.7	0.8–9.5	3.0 ± 1.4	0.8–9.5	n.a.
WD (degree)	227.1 ± 69.3	55–317.5	262.6 ± 35.2	114.6–337.9	211.8 ± 71.7	28.3–329.8	170 ± 74.3	50–322.5	214.2 ± 73.6	28.3–337.9	n.a.
T (°C)	17.9 ± 1.7	14.2–21	23.6 ± 2.4	18.4–35.9	19.4 ± 3.6	11.1–24.8	13.7 ± 2.4	8.5–19.4	18.4 ± 4.7	8.5–35.9	n.a.
P (hPa)	1015 ± 4.3	1005–1023	1015 ± 2.3	1009–1019	1017 ± 4.1	1003–1025	1022 ± 5	1006–1033	1018 ± 5.2	1003–1033	n.a.
RH (%)	79.2 ± 7.2	55.6–89.1	74 ± 6.4	36.2–84.7	76.7 ± 5.3	65–90.6	83.4 ± 6.6	57.7–95.5	78.5 ± 7.3	36.2–95.5	n.a.

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