Eclet. Quim. 49 | e-1505, 2024 https://doi.org/10.26850/1678-4618.eq.v49.2024.e1505 ISSN 1678-4618 page 1/11 1Federal Institute of Ceará, Caucaia, Brazil. 2Federal University of Ceará, Department of Analytical Chemistry and Physical Chemistry, Fortaleza, Brazil. 3Federal University of Ceará, Department of Transportation Engineering, Fortaleza, Brazil. +Corresponding author: Francisco Wagner de Sousa, Phone: +5585999025637, Email address: fr.wagner@ifce.edu.br Original Article Estimation of nitrogen dioxide levels on streets from Fortaleza Brazil using passive sampling and multivariate analysis Francisco Wagner de Sousa1+ , Maxwell Lima Maia2 , Wendy Fernandes Lavigne Quintanilha3 , Demostenis Ramos Cassiano3 , Bruno Vieira Bertoncini3 , Ronaldo Ferreira Nascimento2 , Cleidiane Silva Oliveira1 , Jefferson Pereira Ribeiro2 Abstract The increase in the fleet of motor vehicles circulating in urban centers is one of the main generators of gaseous pollutants harmful to human health and the environment. Pollutants can be economically and effectively monitored through passive sampling. This study aims to estimate NO2 levels on roads of Fortaleza city /CE using the passive sampling method. 12 campaigns covered the rainy (March-June) and the dry (July-November) seasons in 2019. The seasonal averages of NO2 in the rainy season were higher than the dry one, and Almirante Rubim Street showed the greatest difference in the averages: 26.6 µg m-3 in the rainy and 19.3 µg m-3 in the dry season. The principal component analysis applied to the averages of NO2 concentration in the rainy and dry seasons, vehicle traffic and Height of the road/width ratio indicated that components 1, 2 and 3 explain 94.4% of the studied cases. Passive sampling proved to be efficient, contributing to the production of unpublished data about NO2 levels in streets of Fortaleza/Ceará/Brazil from mobile sources. The PCA was used to group the sampling points that present similar behavior about the average NO2 variables in the rainy and dry seasons, H/W ratio and vehicle traffic, through a two-dimensional graph. Article History Received August 02, 2023 Accepted January 15, 2024 Published May 19, 2024 Keywords 1. passive sampling; 2. nitrogen dioxide; 3. seasonality; 4. principal component analysis. Section Editor Natanael de Carvalho Costa Paulo Clairmont Feitosa Lima Gomes Highlights Passive sampling was efficient in indicating NO2 levels from mobile sources. Meteorological data and the H/W ratio of roads influence the NO2 levels. The PCA is a powerful and suitable statistical tool for interpreting NO2 levels. https://doi.org/10.26850/1678-4618.eq.v49.2024.e1505 https://ror.org/02225fd27 https://ror.org/03srtnf24 https://ror.org/03srtnf24 https://ror.org/03srtnf24 mailto:fr.wagner@ifce.edu.br https://orcid.org/0000-0002-5588-2501 https://orcid.org/0000-0001-6998-6013 https://orcid.org/0000-0001-8770-5334 https://orcid.org/0000-0002-4354-3836 https://orcid.org/0000-0002-1434-1960 https://orcid.org/0000-0002-6393-6944 https://orcid.org/0009-0001-5053-4903 https://orcid.org/0000-0002-0225-3226 Original Article https://doi.org/10.26850/1678-4618.eq.v49.2024.e1505 Eclet. Quim. 49 | e-1505, 2024 ISSN 1678-4618 page 2/11 1. Introduction Economic development in a region favors, among other factors, population growth, urbanization of green areas and the installation of diverse industries. This situation also contributes to the considerable increase in the emission of harmful compounds that in significant quantities reduce the air quality of the region and thus significantly affect the dynamics of the ecosystem and the health of the population (Baird and Cann, 2011; Ding et al., 2022; Guo et al., 2019; Lenzi and Favero, 2014; Zanetti and Melli, 1992). Nitrogen dioxide (NO2) is one of these compounds and its main sources in urban areas are largely from industry and vehicle emissions from the burning of fossil fuels (Aránguez et al., 1999; Biswal et al., 2020; Januševičius and Grubliauskas, 2019; Lenzi and Favero, 2014; Richmond-Bryant et al., 2017). At low altitude atmospheres, NO2 is part of the photochemical smog that causes oxidation of any material it meets and at high altitudes, it causes the depletion of the ozone layer (Lenzi and Favero, 2014). Plants, by absorbing NO2 through their leaf stomata, can oxidize this pollutant to nitrate through photochemical reactions, favoring the internal spaces of the leaf to become acidic, thus damaging them (Freedman, 1995; Rao et al., 2014). In the human population, NO2 can cause eye irritation and when inhaled it favors inflammation of lung tissue, emphysema, and installation of respiratory infections, caused by changes in local immunity (Ghozilaki et al., 2016; Negrisoli and Nascimento, 2013; Russo et al., 2014), in addition to increase the susceptibility to bronchoconstrictor agents and respiratory infections caused by bacteria, especially in children (Shiraiwa et al., 2012; Ugucione et al., 2002) and positively increase the number of hospitalizations and mortality (Arbex et al., 2012; Duan et al., 2019; Hatzopoulou et al., 2013; Martins et al., 2002; Nascimento et al., 2006; Pestana et al., 2017). Between 2009 and 2018, about 115 thousand hospital admissions of people residing in Fortaleza were carried out because of asthma, bronchitis, influenza, and pneumonia. Of this total, more than 50% of cases are due to pneumonia. The number of deaths due to these diseases in the period from 2009 to 2017 was 29,292, with 89.5% being people aged 50 years or older (Datasus, 2019). Emissions of atmospheric pollutants produced by vehicular sources are difficult to control, thus leading much research to studies that verify the reduction of car emissions using catalytic converters (Halim et al., 2018; Mehta and Dey, 2020). In Fortaleza, until December 2018, about 1.1 million vehicles were registered, with more than 50% of this fleet being private cars and more than 13% being diesel vehicles (IBGE, 2019). To evaluate the number of atmospheric pollutants, as well as NO2, several types of sampling were developed that can be classified according to the analysis methodology, among which passive sampling equipment stands out (Harner et al., 2013; Miranda et al., 2017) due to its low cost, easy handling and understanding, without any automatic systems, it does not require electricity, has easy labor logistics and can absorb pollutants of a gaseous nature or polluted vapors from the atmosphere through the process of diffusion and permeation by concentration difference (Cruz and Campos, 2008; Hauser et al., 2015; Lacava et al., 2002; Lisboa and Kawano, 2010; Masey et al., 2017; Piceli and Lisboa, 2018; Souza et al., 2017). Considering these facts, in the city of Fortaleza there is no annual monitoring of the levels of all legislated inorganic compounds (SO2, Total Suspended Particulates, CO, O3, PM 2.5, PM 10 and even NO2) on high-traffic roads. The absence of monitoring work on NO2 and other parameters is due to the lack of financial incentives, the lack of qualified and trained human resources and the high costs of materials and equipment needed. This lack of information emerges as a very serious problem, which makes it difficult for health inspection and surveillance agencies to establish a mechanism for preventing and controlling atmospheric pollution. Faced with this problem, the present study aims to estimate NO2 levels using the passive sampling method in commercial streets in Fortaleza/CE. 2. Experimental 2.1. Study area The city of Fortaleza has 2,473,614 inhabitants and a total area of 313.8 km2 (Fig. 1). The city has an Aw’ climate on the Koppen-Geiger scale, with an average annual temperature of 26.5 °C, an average wind speed of 12.7 km h–1 and an average annual rainfall of 1600 mm, with the highest occurrences between February and May featuring the city’s rainy season (IBGE, 2019; M. Moura et al., 2008; M. Moura, 2015). Figure 1. Map of the city of Fortaleza with the location of Montese and Centro. Almirante Rubim (Street R) and Antônio Fiúza (Street F) streets, both in the Montese neighborhood and General Sampaio (Street S) and 24 de Maio (street M) Streets, both in the Centro neighborhood, were selected because they have two lanes and one- way traffic. Each road was divided into sectors and each sector has two sampling points, one on each side of the road. The chosen nomenclature and the georeferenced location of the sampling points were given as shown in Table 1. Points F3 and F4 are close to the corner of Street Antônio Fiúza and Street 15 de Novembro, the access road to Fortaleza airport. Points M7, and M8, from 24 de Maio and points S7 and S8, from General Sampaio, then fixed on the corner with Street Castro e Silva. In this block is located the bus terminal of Praça da Estação. On all 4 lanes, the odd-numbered points are located on the posts on the right side of the street and the even points are on the posts on the left side, as illustrated in Fig. 2. https://doi.org/10.26850/1678-4618.eq.v49.2024.e1505 https://doi.org/10.26850/1678-4618.eq.v49.2024.e1505 https://doi.org/10.26850/1678-4618.eq.v49.2024.e1505 Original Article https://doi.org/10.26850/1678-4618.eq.v49.2024.e1505 Eclet. Quim. 49 | e-1505, 2024 ISSN 1678-4618 page 3/11 Table 1. Nomenclature and georeferenced location of sampling points. Street Point Latitude Longitude Street Point Latitude Longitude Street F F1 3°46’07.7”S 38°33’06.3”W Street M M3 3°43’44.1”S 38°31’57.7”W F2 3°46’07.7”S 38°33’06.0”W M4 3°43’44.1”S 38°31’57.4”W F3 3°46’12.3”S 38°33’08.2”W M5 3°43’26.8”S 38°31’52.2”W F4 3°46’12.7”S 38°33’07.8”W M6 3°43’26.3”S 38°31’51.6”W F5 3°46’17.8”S 38°33’10.0”W M7 3°43’22.3”S 38°31’50.6”W F6 3°46’17.8”S 38°33’09.8”W M8 3°43’22.3”S 38°31’50.3”W Street R R1 3°46’22.5”S 38°33’26.3”W Street S S1 3°43’57.5”S 38°31’56.7”W R2 3°46’22.5”S 38°33’26.8”W S2 3°43’57.2”S 38°31’57.1”W R3 3°46’19.1”S 38°33’25.1”W S3 3°43’47.1”S 38°31’53.6”W R4 3°46’18.4”S 38°33’25.4”W S4 3°43’46.3”S 38°31’53.9”W R5 3°46’13.0”S 38°33’22.9”W S5 3°43’29.4”S 38°31’48.6”W R6 3°46’12.7”S 38°33’23.3”W S6 3°43’29.4”S 38°31’48.8”W Street M M1 3°43’56.4”S 38°32’01.7”W S7 3°43’23.6”S 38°31’46.6”W M2 3°43’56.4”S 38°32’01.4”W S8 3°43’23.4”S 38°31’47.1”W Figure 2. Schematic of the location of sampling points – (from left to right) – Str. F, Str. R, Str. M and Str. S. 2.2. Sampling period The sampling period was carried out from March 10, 2019, to December 10, 2019, with a total of 12 campaigns (Table 2) comprising the rainy season, from March to May and the dry season, from June to November. Table 2. Rainy and dry period of passive sampling of NO2 filters. Campaign Period Season 1º 10/03 – 31/03 Rainy 2º 31/03 – 21/04 Rainy 3° 21/04 – 05/05 Rainy 4° 05/05 – 26/05 Rainy 5° 26/05 – 16/06 Rainy 6° 16/06 – 07/07 Dry 7° 07/07 – 28/07 Dry 8° 28/07 – 18/08 Dry 9° 18/08 – 08/09 Dry 10° 08/09 – 29/09 Dry 11° 29/09 – 20/10 Dry 12º 20/10 – 10/11 Dry Each campaign has a period of 21 days, except for the 3rd campaign which took place in 14 days due to strategic reasons. The change of samplers from one campaign to another took place on Sundays, due to the low flow of vehicles on the roads and consequently little emission of pollutants and for the safety of the sampling team. Overall, 360 filters were produced, with 30 filters per campaign (28 for sampling points and 2 for blanks). 2.3. Nitrogen dioxide methodology Among the various methodologies for sampling NO2, the methodology used in this work was based on the works of Saltzman (1954) where an absorber solution (0.5 M KI (Campinas, Brazil, Dinâmica) + 0.2 M KOH (Campinas, Brazil, Dinâmica) in Methanol (Suzano, Brazil, Neon)) was prepared, used to be added directly (impregnated) in cellulose filters to capture and adsorb NO2 molecules present in the environment. Then, a reagent solution (0.5% w/v Sulfanilamide (Suzano, Brazil, Neon), 0.005% w/v N–1-Naphthyl-ethylenediamine (St. Louis, USA, Sigma- Aldrich) and 1% v/v Phosphoric Acid (St. Louis, USA, Sigma- Aldrich)) was used on the filters after the sampling period to the extraction of NO2 present in the filters and converting it into nitrite ion [NO2]–. A stock solution (0.0203 g of Sodium Nitrite P.A in 1 L of water) was prepared to construct the UV-VIS (Barueri, Brazil, Shimadzu) calibration curve, where the levels of NO2 present in the filters were determined (Dita and Dias, 2016; Lodge, 1988; Saltzman, 1954; Shaw, 1967). 2.4. Street morphology and traffic Urban morphology can be considered one of the factors that contribute to the dispersion of a pollutant, along with the wind direction and speed on a road. The calculation of the morphology of a road is obtained through the ratio between the height of the road (H) and width (W) - distance between buildings (Aguiar et al., 2017; Bender and Dziedzic, 2014; Muniz-Gäal et al., 2018; https://doi.org/10.26850/1678-4618.eq.v49.2024.e1505 https://doi.org/10.26850/1678-4618.eq.v49.2024.e1505 https://doi.org/10.26850/1678-4618.eq.v49.2024.e1505 Original Article https://doi.org/10.26850/1678-4618.eq.v49.2024.e1505 Eclet. Quim. 49 | e-1505, 2024 ISSN 1678-4618 page 4/11 Nakata-Osaki et al., 2016). The values of H and W were manually measured from the distance between the buildings and for the height of the road, consider that each building floor is 3 m high. To quantify the number of vehicles that travel on the Streets where the sampling took place, a count of vehicles that pass- through a given reference point in the first 15 min of each hour was performed. 2.5. Principal component analysis (PCA) The PCA was used to group the sampling points that present similar behavior about the average NO2 variables in the rainy and dry seasons, H/W ratio and vehicle traffic, through a two-dimensional graph (Azevedo and Anzanello, 2015; Ghosh and Dubey, 2013; Honda et al., 2010; Moori et al., 2002; Stricker et al., 2013; Xu et al., 2015). To perform the calculation of these algorithms, PCA, the free software PAST (Hammer et al., 2001), version 3.26b for Windows was used, in which it is possible to analyze scientific data, with functions for data manipulation, plotting, univariate and multivariate statistics. Before applying the data obtained in the software, they were pre-treated by self-scaling to minimize the influence of the dominant variable (Ferreira, 2015; Hongyu et al., 2016; Karamizadeh et al., 2013; Lyra et al., 2010; Maia et al., 2019). 3. Results and discussion The results of the 360 NO2 samples analyzed in this study over 12 collection campaigns on the studied streets are shown in Table 3. The street with the lowest overall mean concentration was on 24 de Maio (Street M) with 19.2 µg m–3 with a variation between 1.6 and 33.6 µg m–3. Antônio Fiúza (Street F) presented the highest average concentration with 25.3 µg m–3 with a variation between 1.5 and 55.7 µg m–3. The other streets showed average concentrations of 22.4 µg m–3 in Almirante Rubim (Street R), 24.3 µg m–3 and General Sampaio (Street S). Table 3. Average concentration of NO2 during the 12 campaigns. Campaign Street Street F Street R 𝒙 Conc. Min-Max sd 𝒙 Conc. Min-Max sd 1º 23.8 6.5–38.9 13.0 23.5 15.4–31.5 5.9 2º 32.7 21.3–44.3 9.2 24.7 18.8–31.5 5.2 3° 22.9 13.4–29.4 7.0 34.0 20.3–45.2 11.1 4° 26.5 17.0–35.7 8.5 27.9 23.1–34.3 4.3 5° 26.5 17.6–35.1 6.7 22.5 15.6–30.2 5.5 6° 25.0 19.2–32.0 5.3 23.4 17.4–32.7 6.1 7° 29.5 16.2–47.7 13.4 20.1 15.6–24.4 3.6 8° 24.4 18.6–27.5 4.3 23.2 17.8–30.7 5.3 9° 23.3 20.1–26.6 2.8 20.2 13.8–25.9 5.8 10° 30.0 19.4–55.7 17.2 16.1 10.7–21.9 4.8 11° 24.6 21.4–28.6 3.3 16.6 10.9–22.7 4.6 12º 21.3 6.5–38.9 13.0 16.1 13.5–20.6 2.8 Note: 𝒙 is average; sd is standard derivation; conc. min-max is the minimum and maximum concentration. The low average concentrations of NO2 presented by M Street, as well as the higher average concentrations of NO2 presented by F Street, may be related to the flow of vehicles, since M Street presented an average daily traffic of 5,402 vehicles, the lowest among the routes, while Street F had 12,406 daily vehicles, indicating that the levels of NO2 present in the streets may come from vehicular sources and secondary chemical reactions from the reaction of NO with O3 (Carslaw et al., 2016; Lenzi and Favero, 2014). None of the analyzed samples showed values above the current standards established by National Council for the Environment No. 491/2018 (CONAMA, 2018) which is 260 µg m–3 h–1 and by international bodies such as the World Health Organization (WHO, 2005) and the Environmental European Agency (EEA, 2018) that set a maximum limit of 200 µg m–3 per hour and the Environmental Protection Agency (EPA, 2018) that established a maximum limit of 188 µg m–3 per hour. The concentration of NO2 levels using passive sampling varies from one street to another according to the studied sites’ local characteristics. Hien et al., 2014, found a maximum concentration of 84 µg m–3 in the city of Hanoi, Vietnam, using passive sampling with an estimated annual variation between 45.2 and 79.7 µg m–3 for high-flow lanes and between 18, 7 to 41.2 µg m–3 for streets with access to industries. Grundström and Pleijel, 2014, analyzed NO2 with passive samplers arranged inside and outside treetops located in regions close to the highway in the city of Gothenburg, Sweden, obtaining results ranging from 12.9 to 47.1 µg m–3. Bari et al., 2015, monitored NO2 in Alberta province of Canada, between 2006 and 2010, obtaining data ranging from 0.4 to 34.0 µg m–3, with a total average of 3.9 µg m–3. Bozkurt et al., 2018, obtained mean concentrations of NO2 between 18.3 and 33.8 µg m–3, in the city of Düzce, Turkey. In Brazil, Campos et al. (2010) found mean values ranging from 3.6 to 12 µg m–3 in Salvador city, BA and from 6.7 to 11.0 µg m–3 in residential areas of the city of Curitiba/PR. 3.1. Analysis by sampling point Figure 3a shows NO2 concentrations in µg m–3 at all sampling points located on Antônio Fiúza (Street F) during the 12 campaigns. In it, we observed that F5 and F6 were the points that presented the lowest concentrations both in the rainy and dry seasons and this may be related to the low circulation of vehicles, especially heavy vehicles such as buses and trucks, in addition to factors that may have corroborated a deviation that occurred during a city hall work on this road on the sampling days (Nakata-Osaki et al., 2013)..The averages for F5 and F6 in the rainy season were, respectively, 18.4 and 17.9 µg m–3 and in the dry season, the averages were 20.0 and 19.6 µg m–3, respectively, according to presented in Table 1. https://doi.org/10.26850/1678-4618.eq.v49.2024.e1505 https://doi.org/10.26850/1678-4618.eq.v49.2024.e1505 https://doi.org/10.26850/1678-4618.eq.v49.2024.e1505 Original Article https://doi.org/10.26850/1678-4618.eq.v49.2024.e1505 Eclet. Quim. 49 | e-1505, 2024 ISSN 1678-4618 page 5/11 Figure 3. (a) NO2 levels obtained at the points located at Antônio Fiúza; (b) at Almirante Rubim; (c) at 24 de Maio; (d) at General Sampaio. Figure 3b shows the NO2 concentrations in µg m–3 at all sampling points at Almirante Rubim (Street R) during the 12 campaigns. We can observe that R1 and R5 are the points that present the highest concentrations of this road, in most of the samples, except in campaign 3. This fact can be explained by the location of these points being close to the crossing of streets. Although R1 and R5 presented values above the others, they did not register levels above 40 µg m–3. On the other hand, R3 and R6, in the 3rd campaign, when they reached the highest levels recorded in this street, being 45.2 and 44.8 µg m–3, respectively. During this campaign, the sampling lasted 14 days, different from the rest of the campaigns, and presented an average temperature of 22.9 °C, the lowest recorded during the study, and the highest average daily amount of rain recorded, reaching 17.9 mm. Such factors may have contributed to the reduction in the flow of vehicles and, consequently, there was a higher concentration of NO2 at these points of the road. It also shows the absence of samples R3 in the 8th and 10th campaigns; R4 in the 4th campaign; and R5 in the 10th campaign. Such samples were lost during the sampling period. Figure 3c shows the NO2 concentrations in µg m–3 at all sampling points on the 24th of May (Via M) during the 12 campaigns. In this one, we observed that all points had concentrations below 28.0 µg m–3, except M7 in campaigns 4, 8 and 12. We also observed that all, except the 4th campaign of M4, had concentrations above 7.0 µg m–3. Point M7 stands out among the other points for presenting the highest concentration in most campaigns and this can be explained by the fact that this point is located close to the bus terminal and a crossroads, contributing to the higher concentration of NO2 in this region of the street (Carslaw et al., 2016). Figure 3d shows NO2 concentrations in µg m–3, at all sampling points, in General Sampaio Street (Street S) during the 12 campaigns. In it, we observed that the points, except for S1, S5 and S8 in the rainy season, do not reach concentrations above 30 µg m–3, with S1 and S5 being located near bus stops and S8 being located at road crossings and close to a bus terminal. From the 7th campaign onwards, there was a drop in NO2 levels in S8, as works began at that moment in the station square, reducing vehicle traffic on the road. 3.2. Influence of meteorological data and H/W ratio of roads According to the data obtained from Instituto Nacional de Meteorologia (INMET, 2019) meteorological stations, the average temperature of the 12 campaigns was 26.72 °C, except in excess, a single campaign had an average of 22.9°C with winds blowing mostly from the southeast and east directions. Such meteorological parameters did not suffer sudden variations during the sampling campaigns (22.9–27.8 °C). However, humidity (%), wind speed (km h–1) and precipitation (mm) in each campaign had notable variations and these values are shown in Table 4, together with the general average of NO2 in µg m–3, per campaign, in each studied route. As can be seen, the average humidity and precipitation gradually decreased over the sampling period, while the average wind speed increased, characterizing the rainy and dry periods in Fortaleza city. It is important to emphasize that the individual assessment of these parameters should not be related to the concentration since it can lead to misinterpretations. https://doi.org/10.26850/1678-4618.eq.v49.2024.e1505 https://doi.org/10.26850/1678-4618.eq.v49.2024.e1505 https://doi.org/10.26850/1678-4618.eq.v49.2024.e1505 Original Article https://doi.org/10.26850/1678-4618.eq.v49.2024.e1505 Eclet. Quim. 49 | e-1505, 2024 ISSN 1678-4618 page 6/11 Table 4. Mean values of humidity in (%), wind speed in (km h–1), temperature (°C), precipitation (mm) and average at streets during the 12 campaigns. Variable Rainy Season Dry Season 1° 2° 3° 4° 5° 𝒙 6° 7° 8° 9° 10° 11° 12° 𝒙 Moisture (%) 90.0 86.9 85.8 82.3 76.3 84.3 74.1 71.8 68.2 66.0 66.7 66.6 65.8 68.5 Wind Speed (km h–1) 4.4 4.6 5.9 6.5 7.4 5.8 8.0 9.2 10.7 11.8 12.2 12.0 11.1 10.6 Temperature (°C) 26.4 26.8 22.9 27.1 26.9 26.0 26.6 26.6 26.7 27.1 27.3 27.4 27.8 27.1 Precipitation (mm) 13.8 10.7 17.9 10.1 9.6 12.4 6.2 2.7 0.9 0.5 0.5 0.4 0.0 1.9 𝑥 of NO2 St. F (µg m–3) 23.8 32.7 22.9 26.5 26.5 26.6 25.0 29.5 24.4 23.3 30.0 24.6 21.3 25.5 𝑥 of NO2 St. R (µg m–3) 23.5 24.7 34.0 27.9 22.5 26.5 23.4 20.1 23.2 20.2 16.1 16.6 16.1 19.3 𝑥 of NO2 St. M (µg m–3) 21.2 20,6 20.8 18.8 19.4 20.0 18.9 16.8 19.5 20.8 15.5 19.7 18.1 18.5 𝑥 of NO2 St. S (µg m–3) 25.2 24.7 25.3 30.9 21.5 25.3 26.2 24.0 27.5 23.7 18.9 24.2 20.2 23.6 Note: 𝒙 of NO2 St. F is NO2 average on Antônio Fiúza Street; 𝒙 of NO2 St. R is NO2 average on Almirante Rubim Street; 𝒙 of NO2 St. M is NO2 average on 24 de Maio Street; 𝒙 of NO2 St. S is NO2 average on General Sampaio Street. The results showed that, for the rainy season, the NO2 levels are slightly higher than in the dry season. However, the R Street showed a greater difference from 26.5 µg m–3 to 19.3 µg m–3. This small reduction of NO2 for the dry season to the levels obtained in the rainy season may be consistent with a reduction in the speed of cars, high traffic jams and a decrease in secondary reactions (Arbex et al., 2012; Moura et al., 2015). Other parameters that may have influenced this reduction were meteorological conditions. For this, a Pearson correlation matrix was elaborated to verify the influence of Relative Humidity (RH), Wind Speed (WS), Precipitation (PP) and Temperature (T) with the levels of NO2 obtained in each route (Table 5). In it, we found that the correlation of RH and PP with NO2 concentration was positive in all streets, especially the R Street. However, WS showed negative correlation with NO2 levels, especially for the R Street. Concerning T, the correlation was negative in the streets, except for the F Street. From what was shown by the matrix, except for the F Street, the increase in the values of UR and PP together with the reduction of WS and T, favors a slight increase in NO2 concentrations, especially in the R Street. Table 5. Pearson’s correlation matrix between Relative Humidity (RH), Wind Speed (WS), Precipitation (PP) and Temperature (T) with the average concentrations of NO2 of the studied streets. RH WS PP T NO2 Str F NO2 Str R NO2 Str M NO2 Str S RH 1 WS –0.976 1 PP 0.941 –0.917 1 T –0.519 0.461 –0.707 1 NO2 St. F 0.193 –0.206 0.030 0.169 1 NO2 St. R 0.753 –0.741 0.855 –0.807 –0.063 1 NO2 St. M 0.526 –0.504 0.533 –0.372 –0.370 0.522 1 NO2 St. S 0.437 –0.445 0.371 –0.228 –0.077 0.635 0.418 1 Note: NO2 St. F is average concentrations of NO2 on Antônio Fiúza Street; NO2 St. R is average concentrations of NO2 on Almirante Rubim Street; NO2 St. M is average concentrations of NO2 on 24 de Maio Street; 𝒙 of NO2 St. S is average concentrations of NO2 on General Sampaio Street. The literature reports that low relative humidity and wind speed favor the increase of NO2 levels while the occurrence of precipitation and the increase of wind speed can favor the reduction of NO2 levels (Drumm et al., 2013; Kamińska, 2019; Monte et al., 2016). The increase in temperature near the surface, especially during the summer, can increase the kinetics of gases and consequently improve vertical mixing, contributing to the reduction of NO2 concentrations in the lower atmosphere (Gasmi et al., 2017). In addition to meteorological conditions, the secondary chemical reactions of NO2 with photochemical oxidants that occur in the atmospheric lower layers and the morphology of the pathway normally influence NO2 concentrations (Gasmi et al., 2017; Han et al., 2011). Compared with the results obtained in the literature, a study performed in Dhahran City, Saudi Arabia showed that NO2 concentration is strongly affected by traffic emission and photochemistry. Ambient air temperature and wind speed had negative correlation coefficients with NO2 concentrations while relative air humidity had a positive correlation coefficient (Gasmi et al., 2017). In Mato Grosso do Sul, Brazil, a study found negative correlations between relative humidity and wind speed with NO2 concentration and a positive correlation between temperature and NO2 concentration (Souza and Santos, 2018). Regarding the morphology of the street, the mean values of the H/W ratio for the F, R, M and S Streets were 0.45, 0.23, 0.50 and 0.55, respectively. According to the simplified classification of different urban forms, the H/W ratio with values between 0.2 and 0.6 and with an area occupied by buildings of 70 to 90% are highly developed streets, with low or medium urban density, with large low-rise buildings (Oke et al., 2004). This description confirms the studied streets, as there are shopping centers, restaurants, and parking lots. The results also show that even with the winds blowing perpendicularly to the direction of the street, there were no discrepancies in concentration on one side of the street that was contrary to what was cited in the literature. It is important to report that the influence of meteorological parameters and morphology on NO2 levels requires a more detailed study, using meteorological stations at the sampling site. 3.3. Principal component analysis The Principal Component Analysis of the 4-way sampling points related the average NO2 concentration with the average estimate of vehicle traffic and the H/W ratio according to Fig. 4. https://doi.org/10.26850/1678-4618.eq.v49.2024.e1505 https://doi.org/10.26850/1678-4618.eq.v49.2024.e1505 https://doi.org/10.26850/1678-4618.eq.v49.2024.e1505 Original Article https://doi.org/10.26850/1678-4618.eq.v49.2024.e1505 Eclet. Quim. 49 | e-1505, 2024 ISSN 1678-4618 page 7/11 The variance of the 4 principal components (CP) analyzed CP1, CP2, CP3 and CP4 are 52.1, 25.6, 16.7 and 5.6%, respectively. These results show that two main components (CP1 and CP2) explain 77.7% of the cases studied. The CP1, represented by the X axis, indicates that the points in the positive direction of this axis have great influence on the variables Average of NO2 in the rainy (1°P) and dry (2°P) periods, since both have Loading (weight) of 0.63 and 0.61, respectively, while the Loadings for altimetry and traffic are –0.04 and 0.48, respectively. CP2, represented by the Y axis, indicates that its positive axis has a great influence of the altimetry variable, with a Loading of 0.98. In Fig. 4, the blue points belong to M Street, the red points belong to S Street, the grey are points of F Street and the green are those of R Street. Note that all points of the M Street, except M7, were on the negative axis of the principal component 1, indicating that they presented values of NO2 below the general average in both periods, which were 24.3 µg m–3, 21.5 µg m–3, for 1°P and 2°P, respectively. It is also worth noting that all points on the R Street are on the negative axis of the principal component 2, indicating that the H/W ratio was below the general average that was 0.4. Most of the points on F Street, except for points F5 and F6, were on the positive axis of the main component 2, indicating that these points presented NO2 levels in both periods and vehicle traffic above the general average. The S Street points did not show clusters in a specific region of the graph. Applying PCA with PC1 and PC3, we obtain Fig. 5. In this graph, the percentage of the total variance (%) of the data of the two principal components (PC) is explained by 68.8% of the cases, PC1 being represented on the X axis and PC3 on the Y axis. Due to the high loading presented by the Traffic variable in PC3, which was 0.87, the points were grouped according to the average number of vehicles that travel on the street per day, with a general average of 8,535 vehicles. As a result, we can observe that, although the total variance of this graph is smaller than the previous one, all samples were separated by streets, with the F Street having the highest values and the M Street having the lowest traffic values. We also observed in this graph that the points F1, F2, F3, F4, R1, R5, S5, S8 and M7 are in the positive region of the average NO2 variables in the rainy and dry periods, that is, they presented the highest values for these variables. Applying PCA with PC2 and PC3, we obtain Fig. 6, in which the percentage of total variance of the data is explained by 42.3% of the cases, PC2 being represented by the Y axis and PC3 by the X axis. The highest Loading in CP2 was the H/W ratio and in CP3 was the vehicle traffic variable. From this graph, we can see that the points of lane F are all located on the positive axis of CP3, indicating that they presented a greater flow of vehicles among the studied streets, confirming what was described in Fig. 6, about CP2, F3 and F4 are on the negative axis, indicating the low H/W ratio. However, the other points of this street are on the positive axis of CP2. The points of the R Street are grouped on the negative axis of CP2, indicating the low H/W ratio, as mentioned in Fig. 4. The points of the M Street are all located on the negative axis of CP3, indicating the lowest flow of vehicles among the lanes studied, as also shown by Fig. 6. Both in Fig. 5 and 6, the points belonging to the S Street are more dispersed among the streets, indicating that the points of this street do not present a similarity between the samples. Figure 4. PCA with principal components 1 and 2 of the sampling points using the variables’ general average of NO2 in the rainy and dry seasons, altimetry (H/W) and vehicle traffic. https://doi.org/10.26850/1678-4618.eq.v49.2024.e1505 https://doi.org/10.26850/1678-4618.eq.v49.2024.e1505 https://doi.org/10.26850/1678-4618.eq.v49.2024.e1505 Original Article https://doi.org/10.26850/1678-4618.eq.v49.2024.e1505 Eclet. Quim. 49 | e-1505, 2024 ISSN 1678-4618 page 8/11 Figure 5. PCA with principal components 1 and 3 of the sampling points using the variables’ general average of NO2 in the rainy and dry seasons, altimetry (H/W) and vehicle traffic. Figure 6. PCA with principal components 1 and 2 of the sampling points using the variables general average of NO2 in the rainy and dry seasons, altimetry (H/W) and vehicle traffic. 4. Conclusions Although difficult to compare, the results obtained showed that this pollutant did not exceed the limits established by current Brazilian legislation (CONAMA, 2018 – Resolution 491) and by international legislation (EEA, 2018; EPA, 2018; WHO, 2005) with levels like those previously mentioned in the literature using a methodology equivalent. This dataset also provided this work with a specific analysis of the pollutant behavior studied along a road using principal component analysis, in which it is possible to observe that NO2 levels are dependent on the street profile, such as terrain and vehicle traffic. In front of the PCA graph, it was easy to visualize points that, although located in different ways, presented similar behavior of the analyzed variables. It is important to emphasize that the results observed throughout this research should be seen as indicative and not conclusive since a broader follow-up is necessary for the care to be elaborated. Authors’ contributions Conceptualization: Sousa, F. W.; Bertoncini, B. V.; Nascimento, R. F.; Ribeiro, J. P.; Data curation: Maia, M. L.; Oliveira, C. S.; R. F.; Ribeiro, J. P.; Formal Analysis: Maia, M. L.; Oliveira, C. S.; Quintanilha, W. F. L.; Cassiano, D. R.; Funding acquisition: Not applicable; Investigation: Maia, M. L.; Sousa, F. W.; Quintanilha, W. F. L.; Cassiano, D. R.; Ribeiro, J. P.; Bertoncini, B. V.; Oliveira, C. S.; Methodology: Maia, M. L.; Sousa, F. W.; Quintanilha, W. F. L.; Nascimento, R. F.; Ribeiro, J. P.; Bertoncini, B. V.; Oliveira, C. S.; Cassiano, D. R.; Project administration: Sousa, F. W.; Resources: Sousa, F. W.; Maia, M. L.; Software: Quintanilha, W. F. L.; Cassiano, D. R.; Supervision: Sousa, F. W.; Bertoncini, B. V.; Validation: Maia, M. L.; Sousa, F. W.; Quintanilha, W. F. L.; Visualization: Sousa, F. W.; Maia, M. L.; Writing – original draft: Maia, M. L.; Sousa, F. W.; Bertoncini, B. V.; Nascimento, R. F.; Ribeiro, J. P.; Writing – review & editing: Maia, M. L.; Sousa, F. W.; Bertoncini, B. V.; Nascimento, R. F.; Ribeiro, J. P. https://doi.org/10.26850/1678-4618.eq.v49.2024.e1505 https://doi.org/10.26850/1678-4618.eq.v49.2024.e1505 https://doi.org/10.26850/1678-4618.eq.v49.2024.e1505 Original Article https://doi.org/10.26850/1678-4618.eq.v49.2024.e1505 Eclet. Quim. 49 | e-1505, 2024 ISSN 1678-4618 page 9/11 Data availability statement All data sets were generated or analyzed in the current study. Funding Not applicable. Acknowledgments Not applicable. References Aguiar, L. F. M. C.; Silva, M. V. C.; Gandu, A. W.; Rocha, C. A.; Cavalcante, R. M. Caracterização de Cânions Urbanos e seus Efeitos Climáticos em Área com Intenso Processo de Verticalização na Cidade de Fortaleza, Ceará. Revista Brasileira de Geografia Física. 2017, 10 (4), 1046– 1058. https://doi.org/10.26848/rbgf.v10.4.p1046-1058 Aránguez, E.; Ordóñes, J. M.; Serrano, J.; Aragonés, N.; Fernández- Partier, R.; Gandarillas, A.; Galán, I. 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