Complex mixtures of organic and inorganic substances (such as sulfates, halogenated and nitrated organic compounds, polycyclic aromatic hydrocarbons (PAHs) and strong acids) are apparent in the urban atmosphere due to high levels of pollution, which can be potentially mutagenic and carcinogenic, bringing damage to the environment and human health (Luiz
Biomonitoring uses animal, plant or fungal species continuously exposed in environments to assess the effects that pollutants can have on living beings. The
The negative effects that pollutants can cause to plants depend on the concentration and time of exposure and cause alterations in the plants’ gas exchange process, leaf necrosis and chlorosis, hamper plant growth and in some cases the suppression of a species in the local flora (Rascio & Navari-Izzo, 2011). Gaseous fractions of air pollution can be absorbed by the stomata of the leaves, particulate matter can be retained on the surface of the plant and can be resuspended by wind or further deposit from the leaves to the ground. The fraction of healthy leaf area, concentration of pollutants and meteorological variables are some of the factors that affect the self-removal of pollutants by plants (Nowak & Dwyer, 2007). The current global trend in urbanization (UN, 2019) increases the pressure on natural and urbanized ecosystems globally.
Long exposure to particulate matter causes adverse effects on human health and can lead to the premature death of millions of people yearly (WHO, 2021). Some diseases caused by this exposure are: cardiovascular diseases, lung cancer, respiratory infections, cerebrovascular accident (CVA) and depression (Vodonos
Particulate Matter (PM) can be considered as a proxy for air pollution and genotoxic effects on plants and negative effects on human health. These particles are subdivided into PM2.5, particles with an aerodynamic diameter of ≤ 2.5 μm, and PM10, particles with an aerodynamic diameter of ≤ 10 μm. In the work of Carreras
This study aims to understand the air pollution and mutagenic dynamics of the bioindicator
The study was carried out in two municipalities – Curitiba and Araucária – which are located in the eastern region of the State of Paraná, in Brazil (Figure 1). The estimated population for Curitiba is 1,948,626 and for Araucária 146,214 inhabitants. (IBGE, 2020). The main economic activities of these two municipalities are commerce and industry, in addition to having together a fleet of approximately 1,539,491 vehicles, which contributes to the release of particulate matter and gases into the environment, thus interfering with air quality, public health and the local biome (IPARDES, 2019). According to the Köppen climate classification, the municipalities have a Cfb climate (temperate climate proper), with well-distributed rainfall, mild summer, occurrence of frost (autumn and winter), average temperature below 20°C (except summer) and no defined dry season (Nitsche
Location of cities and points studied.
The bioassay with the pots containing
The negative control of air pollution, located in the same location of sampling point 3, was installed in a place with controlled or low concentration of particulate matter (PM) in the air. At this point, the SDS011 sensor was placed inside an acrylic box closed with filter paper, so that the air was cleaner when entering the box, filtering the particulate matter. The
The SDS011 was installed outdoors, in a place protected from bad weather. The sensor was mounted in the horizontal position, as recommended by the manufacturer. A script was developed in
The SDS011 sensor is a low-cost device developed in China that reads PM2.5 and PM10 concentrations. It has a measurement range between 0.0 and 999.9
Plants of
The Trad-SHM assay procedures adopted in this research followed an adaptation of the procedures of Ma
To count the cells, all the flowers collected from each point were used, six stamens were removed with the help of tweezers and arranged on glass slides. A 1:1 solution of 70% alcohol and glycerin was used to fix the biological material. With the help of tweezers and a needle, the hairs were aligned on the slide and observed under a magnifying glass. Then, a coverslip was superimposed and the material was analyzed with the aid of an optical microscope. All the pink cells present in the stamen hair and also the total number of stamen hairs contained in the stamen were counted. If there was no pink cell in the observed stamen hair, only the total number of filaments (stamen hair) was counted. The mutation frequency per flower was then expressed as the number of events/1000 stamen hair.
To analyze the relation between the number of mutations in stamen hair and the concentration of particulate matter, we developed a methodology to estimate the time of exposure of the plant to atmospheric pollution that may result in a certain number of cell mutations. We were interested in discovering how many days of exposure to air pollution the plant needs to experience before its mutagenetic response appears. The Backward Sliding Window (BSW) method was developed for this task, and it is explained in the next subsection. The particulate matter data was resampled to daily average values for applying the BSW method.
The Backward Sliding Window (BSW) Method was developed by the authors and applied for detecting two parameters related to the chronic exposure of
Illustration of the Backward Sliding Window (BSW) Method for detecting possible exposure window size and lag time before inflorescence sampling.
After choosing a reasonable window size and the lag time, a dataset was constructed relating the number of mutations in stamen cells to the mean PM10 and PM2.5 concentration in the backward exposure window. Only sites with more than 16 inflorescence sampling days were chosen for this analysis, as it was considered that few collection days (≤ 16) would not be statistically significant. The Pearson correlation coefficient between PM and
Daily profiles with hourly average data of particulate matter were calculated to compare the dynamics throughout the day and night between the sampling points. It is known that in the Araucária sampling point are several industries that contribute to the emission of particles and in Curitiba the main pollution comes from motor vehicles.
The collection of flowers of
Stamen hair mutations by the Trad-SHM bioassay, indicated by arrows.
Figure 5 shows the boxplot of PM10, PM2.5 daily concentrations and mutation frequencies in stamen hair of the sample points, including the control point.
Boxplots of (a) daily mean PM2.5 concentration, (b) daily mean PM10 concentration and (c) mutation frequencies in stamen hair for the sample points. The number of sampling days (
Figure 5(c) shows the frequency of mutations in stamen hair for all points studied, different behaviors were observed between them, which might be associated with the frequency and quantity of flowers collected not being similar between sampling points (the number of sampling days “
The particulate matter concentrations were collected by the SDS011 sensor from January 2020 to February 2021. Some stations had few inflorescence sampling days, which would affect the correlation calculation against particulate matter measurements. To circumvent this limitation, a criterion of at least 17 days of inflorescence sampling was adopted. The selected stations for analysis were: Jardim Botânico, Jardim das Américas, Mercês and Orleans. The data was processed, daily averages were calculated for all stations and the data overview given in Figure 6. During 2020 and beginning of 2021 our data shows an increase of mutations in the winter period, between May and November of 2020, coincidently the air quality was also worse between May and October of 2020. It is known that during the coldest months of the year normally there is less dispersion of pollutants in the lower atmosphere. This process is related to the planetary boundary layer height variation, changes in incident solar radiation and meteorological variables, such as rain and wind patterns.
Time series of PM10 and PM2.5 average daily concentration and
In 2021 the WHO presented guidelines for the guide values for particulate matter and other pollutants that are harmful to health. This update is due to growing evidence of the adverse health effects of air pollution, based on advances in the measurement of pollutants and assessment of human exposure to them (PAHO, 2021). The goal of the guideline updates is to provide quantitative recommendations and guidance for minimizing air pollutant levels in order to reduce the damage caused by exposure to pollutants (PAHO, 2021). In comparison with the last report from WHO, the value for PM2.5 has become more restrictive because it is a fine particulate that is easily inhaled and can penetrate deeply into the lower airways, reaching the lung alveoli and bronchioles (SISAM, 2019).
The red and purple dashed horizontal lines in Figure 6 refer to the limits imposed by the World Health Organization (WHO, 2021), where the daily average level of PM10 and PM2.5 must not exceed concentrations of 45 μg/m3 and 15 μg/m3, respectively. During the data collection period and considering all the sampling points, 25 days exceeded the limit for PM10 and 202 days exceeded the limit for PM2.5, not necessarily being the same days. The station that most often exceeded the WHO limits was Araucária (14 days for PM10 and 65 days for PM2.5), probably due to its location close to the city’s industries and also because there are roads with greater vehicle traffic in this region. This fact was observed by Castelhano (2019) who noticed a pattern of PM concentration being higher in Araucária compared to its surroundings. In Curitiba metropolitan area, the sampling points Jardim das Américas, Batel, Jardim Botânico, Mercês exceeded PM2.5 levels during 28, 27, 23, 20 days, respectively. The control sampling point did not exceed any of the limits.
Figures 7(a) and 7(b) show the daily dynamics of PM10 and PM2.5 concentration, respectively, for all sampling points, except the control. Both graphs represent the hourly mean and 5 times the standard error (SE) for the period from January 2020 to February 2021. Araucaria sampling point had the highest PM10 and PM2.5 concentration for most of the hours of the day, with higher concentrations from 15h to 22h. Jardim das Américas, Batel, Botânico and Guaíra stations had a similar behavior with higher concentrations near the busiest vehicle traffic times, from 6h to 9h and from 18h to 21h. Concentration by time of day can give insight into possible sources of pollution.
Daily variation of (a) PM10 and (b) PM2.5 in sampling points, except the control point. Hourly means are depicted with 5 times the standard error (SE) colored shaded area.
Figure 8 represents the application of the BSW Method with the correlation results between mutations and mean PM concentration for the selected sampling points. We found that a 6-day exposure window beginning 2 days before inflorescence sampling resulted in the highest correlation between PM10 and PM2.5 mean concentration and cell mutations, considering the 4 selected stations (Jardim Botânico, Jardim das Américas, Mercês and Orleans).
BSW Method correlation analysis between PM and mutations for detecting lag time and window size of exposition (a) PM2.5 and (b) PM10.
Figure 9 shows the window size (6 days) and lag time (2 days) framework considered for
Results of the BSW Method, showing the size of the exposure window and the lag time adopted for PM2.5 and PM10.
In order to verify whether air pollution, represented by the particulate matter indicator, is capable of causing changes in the genetic material in plants, specimens of
As already mentioned before, a lag time of 2 days before inflorescence sampling was used to calculate the mean value of particulate matter in the 6-day exposure window, so the statistical correlation between PM and mutations was calculated for all the selected stations. The results of the Pearson correlation (
Pearson’s correlation between PM10 and PM2.5 × mutations per 1000 stamen hairs of
Figure 10 shows the
The present work shows the procedure of data collection and evaluation of particulate matter concentration in Curitiba and Araucária, distributed in eight points plus the control point, as well as the mutation frequency in stamen hair by the Trad-SHM bioassay in
Daily mean concentrations of PM at the monitoring points exceeded the limits specified by WHO (2021). PM2.5 is more critical than PM10 as there were 16 days during the entire studied period when the mean PM2.5 daily average was bigger than 15μg/m3, considering all the sampling points. This happened mostly in seasons when atmospheric dispersion is less favorable, thus causing a higher concentration of pollutants in the atmosphere. Therefore, there is an urge to increase monitoring and to detect the most critical points so that political actions can be taken.
There was an occurrence of mutation frequency in stamen hair at all points studied, being more frequent in the periods between autumn and spring. For the same period of the particulate matter, a tendency can be noticed between the results. Comparisons between PM concentration and mutations in stamen hair of
The correlations between particulate matter and mutation frequency showed positive values, ranging from weak to strong correlation. The mean correlation between particulate matter and mutation frequency was