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The Impact of Climate Change on the Spread of Airborne Pollen in Northern Italy - The Results Of 27 Years of Monitoring in Parma

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21 January 2025

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22 January 2025

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Abstract

Pollen grains play an important role in the etiopathogenesis of seasonal respiratory allergies, which are increasing in prevalence and severity worldwide. Climate change is one of the possible explanations for the increase of pollen allergies enabling plants to produce more allergenic pollen, in larger quantities and longer periods. Pollen count data can be considered the proxy for aeroallergen exposure and long pollen data sets allow to investigate the trends in seasonal characteristics over time. This study examines temporal variations in seasonality and load of airborne pollen and meteorological data recorded over 27 years in Parma (Italy). The study was performed collecting pollen by a Hirst spore trap considering the following taxa: Betula, Corylus, Cupressaceae-Taxaceae, Platanus, Ambrosia, Poaceae, Total pollen and Alternaria fungal spores. Start and end date, duration, date of peak, peak value, and Seasonal Pollen Integral (SPIn) were examined. Temporal variations in pollen seasons were displayed as the number of days from January 1 (DOY, day of the year). Daily averages temperature, relative humidity and total rainfall were considered. Linear regression analysis was carried out to investigate trends in data over time. The start date turned precocious for Corylus and Poaceae, but late for Betula. The end date was postponed for Poaceae and Total pollen, as well the duration of pollen seasons was longer for Poaceae and Total pollen, the duration became shorter for Betula. The peak date was anticipated for Poaceae, and the peak values were reduced for Poaceae and Total pollen. A weak positive trend was observed for SPIn of Corylus. Regarding Ambrosia, the duration was shorter, and the peak date was postponed. No significant differences were observed for Platanus and Alternaria spores. A significant decrease in the relative humidity and a significant increase of annual average temperature were observed. The results of our study represent a contribution to better understanding the impact on human health of environmentally changing conditions. Moreover, it should be considered that not only the seasonal respiratory allergies may be related to the variation of climate and its impact on pollen load and pollen season, but also it could be related to chronic respiratory disease and cardiovascular diseases. This highlights gaps in current knowledge and the need to quantify the impact of climate change in a One Health perspective to provide useful information to determine exposure of the allergic population to pollen and to plan public health preventive measures.

Keywords: 
Climate change
;  
Pollen
;  
Fungal spores
;  
Season parameters
;  
Seasonal respiratory allergies
Subject: 
Public Health and Healthcare  -   Public, Environmental and Occupational Health

1. Introduction

Pollen grains play an important role in the etiopathogenesis of seasonal respiratory allergies releasing the allergens they contain. The seasonal allergies are increasing in prevalence and severity worldwide [1]. Pollen allergy adversely affects the quality of life generating a substantial burden for social life, work, and school performance of several million people globally [2,3,4,5,6,7]. Nonetheless, pollen allergy symptoms are often trivialized and consequently sufferers frequently refrain from seeking proper treatment [8]. Climate change, altering the pollen concentrations and seasonality, influences the development of rhinitis, asthma symptoms in adults and children [9,10,11]. Climate change increases the concern about the relationship with pollen production. Warmer springs and autumns can cause some plants to produce pollen earlier or to extend the pollen season. An earlier start and peak of the pollen season is more pronounced in species that start flowering early in the year. Pollen-related allergies are expected to increase due to the lengthening and intensification of the pollen seasons associated with climate change [12,13,14,15,16]. Plants flower earlier in urban areas than in the corresponding rural areas approximately 2 to 4 days [17]. Atmospheric conditions can affect the release of anemophilous pollen, and the timing and magnitude will be altered by climate change. Present-day knowledge on the relationship between allergic respiratory diseases, asthma and environmental factors, such as meteorological variables, airborne allergens and air pollution, is taken from epidemiological studies. A 23-year observation study on Pinaceae pollen distribution showed a correlation of pollen with high temperatures during summer before the onset of the local Pinaceae flowering season [18]. As simulated with a pollen emission model and future climate data, warmer end-of-century temperatures shift the start of spring emissions 10–40 days earlier and summer/fall weeds and grasses 5–15 days later and lengthen the season duration. Phenological shifts depend on the temperature response of individual taxa, with convergence in some regions and divergence in others. Temperature and precipitation alter daily pollen emission maxima by −35 to 40% and increase the annual total pollen emission by 16–40% due to changes in phenology and temperature-driven pollen production [19]. The increased CO2 concentrations also enable plants to produce more allergenic pollen, in larger quantities [20,21,22,23]. Production of allergenic pollen by ragweed (Ambrosia artemisiifolia L.) has increased in CO2-enriched atmospheres [24]. Seven years study of increase CO2 levels from 560 ppm to 720 ppm enhanced the production of oak pollen to 353% and 1,299%, respectively compared with ambient level [25]. As a result, it can be estimated that the increase in atmospheric CO2 could strongly increase pollen production in conjunction with climate-altering emissions by the end of the century. Land cover change modifies the distribution of pollen emitters, yet the effects are relatively small (<10%) compared to climate or CO2. These simulations indicate that increasing pollen and longer seasons could increase the likelihood of seasonal allergies [19]. Moreover, various studies have shown that aeroallergens can become chemically modified by pollutants and enhance allergic symptoms [20,26,27]. Climate change together with exposure to chemical air pollutants has been shown to have alarming consequences for human health and increase asthma exacerbations. Increasing atmospheric ozone is associated with an acute decrease in lung function, an increase in airway responsiveness, inflammation and systemic oxidative stress [24,28]; the number of birch catkins is influenced positively by the increase of preceding summer temperatures and negatively by the increase of O3 [29]. Moreover, the meteorological conditions can cause the transport of pollen over long distances, depending on the pollen type and the area. Long distance transport of Ambrosia pollen is well documented [30,31]. It was reported that wind direction and speed, temperature, relative humidity, precipitation, air pressure and solar radiation as well as micro and macro-topography within the area can influence Betula pollen dispersion [32,33]. Asthma related to thunderstorms is one of the phenomena related to climate change. Thunderstorms that occur during the pollen season can induce severe asthma attacks and deaths in pollen allergic patients [10,34,35]. Asthma exacerbations and asthma epidemics related to thunderstorms have been described in several cities, mainly in Europe (Birmingham and London in the UK and Naples in Italy) and Australia (Melbourne) [36].
Some studies about airborne pollen season were carried out in Italy analysing long-term variations in airborne pollen seasons [37,38]. Other studies focused on a limit number of pollen families [39,40,41,42], while other studies examining pollen data in the Po Valley geo-climatic area in Northern Italy were over limited time periods [43,44,45,46,47,48]. This study aims at evaluating the impact that changes in some climatic parameters can have on quantity of pollen and fungal spores released into the atmosphere and their seasonality and, consequently, on human health regarding seasonal respiratory allergies examining a data series over 27 years (from 1997 until 2023) in the city of Parma, Northern Italy.

2. Materials and Methods

2.1. Location of the Study

This study was performed in the city of Parma (180,000 inhabitants) which lies in the Po Valley, close to the Southern bank of the Po River (Figure 1), 100 km from the Tyrrhenian coast and 200 km from the Adriatic coast, a humid subtropical climate area [49].

2.2. Aerobiological Data

The pollen grains were collected by a Hirst spore trap [50], placed at 18.2 m above ground level (latitude 44°48′N, longitude 10°16′E), 52 m a.s.l.
We used the rules of Italian Association of Aerobiology that in 2004 became UNI 11108:2004 [51] and afterwards EN 16868:2019 [52]. Pollen data collected over a 27-year period, from 1997 until 2023 were analysed. The following taxa were taken into consideration: Betula, Corylus, Cupressaceae-Taxaceae (hereinafter Cupressaceae), Platanus, Ambrosia, Poaceae, Total pollen and the fungal spores of Alternaria sp. The following characteristics of the pollen season were examined start date, end date, duration, date of peak, peak value and Seasonal Pollen Integral (SPIn) [53,54,55].

2.3. Meteorological Data

Daily averages of mean, minimum and maximum temperature (°C), relative humidity (%) and total rainfall (mm) transformed into annual or seasonal averages were taken into consideration. The meteorological data were obtained from the Meteorological Station (Physics Department) of the University of Parma located at the same site that the pollen data were recorded. The missing data were obtained from the OGIMET database.

2.4. Statistical Analysis

Kolmogorov–Smirnov and Shapiro-Wilk tests were used to assess the type of distribution of pollen and fungal spore’s parameters. Following the methodology used in some prominent studies looking at long-term trends in pollen seasons [56,57,58], simple linear regression analysis was carried out in order to investigate trends of selected meteorological data: a) average of relative humidity (%), b) annual total rainfall (mm), c) and annual and seasonal temperatures. The following statistics were taken into consideration: the mean, standard deviation (SD), R2 value, slope of the regression over time, standard error of the regression slope (SE), probability level (p) and number of years in the analysis (N). Spearman’s ‘‘q’’ correlation test was used to establish any significant relationship existed between the different characteristics of the pollen and fungal spores seasons examined [59]: (a) start date/end date; (b) start date/duration; (c) start date/peak value; (d) start date/SPIn; (e) start date/date of peak; (f) end date/duration; (g) end date/peak value; (h) end date/SPIn; (i) end date/date of peak; (j) duration/SPIn; (k) duration/peak value; (l) duration/date of peak; (m) SPIn/date of peak; (n) SPIn/peak value; and (o) peak value/date of peak. Results were P-value <0.05 was considered statistically significant. Statistical analysis was performed using Microsoft Excel and IBM SPSS 28 software.

3. Results

3.1. Analysis of the Meteorologic Parameters over 27 Years

Means of annual meteorological data recorded during the study period were summarized as follows (minimum and maximum values shown in parenthesis): temperature 14.9 °C (13.8–16.6 °C), relative humidity 66.0 % (59–70 %) and rainfall 776.1 mm (535.7–1113 mm), (Figure 2 a–c). A significant increase in the mean temperature (p = 0.001, r = 0.6778) and a significant decrease of the RH (p = 0.005, r = 0.5250) were observed (Figure 2a and 2b, respectively).
Seasonal mean temperature and precipitation were analysed (Figure 4 and Figure 5).
Precipitation data were obtained from 2003 to 2022 due to lack of data.
It was observed a significant increase of mean temperature for Summer (p = 0.002, r = 0.5631) and Autumn (p = 0.001, r = 0.647) during the period 1997-2023, (Figure 6a,b).

3.2. Characteristics and Trends of Pollen Seasons over 27 Years

The analysis of data from 27 years of observations shows that, on average, the earliest pollen grains that appear in the air of Parma were arboreal taxa as Corylus (DOY 28), Cupressaceae (DOY 42), Betula (DOY 82) and Platanus (DOY 91). On the contrary the pollen grains that appeared later were herbaceous as Poaceae (DOY 109) and Ambrosia (DOY 219), Table 2a. Similarly, the pollen seasons that ended the earliest were Corylus (DOY 79), Platanus (DOY 111), Betula (DOY 122) and Cupressaceae (DOY167). The pollen seasons that ended later were those of Poaceae (DOY 204) and Ambrosia (DOY 266). Regarding season duration, the shortest pollen seasons were those of Platanus (20 days), Betula (41 days), Corylus (50 days),. The longest pollen seasons were those of Poaceae (95 days) and Cupressaceae (125 days), Table 2b.
Regarding trend of the pollen season, the start date for Corylus (p = 0.03) and Poaceae (p = 0.02) was earlier, while start date for Betula (p = 0.002) was postponed, Table 2a. The end date was delayed for Poaceae (p = 0.04) and Total pollen (p = 0.03), Table 2c. As consequence the duration was longer for Poaceae (p = 0.011) and Total pollen (p = 0.05) and shorter for Betula (p = 0.04), Table 2b.
The peak date was postponed for Ambrosia (p = 0.01), and precocious for Poaceae (p = 0.001) and Total pollen (p = 0.004), Table 2d. The peak value of Poaceae decreased significantly (p = 0.02), as well the SPIn of Corylus increased significantly (p = 0.02), Table 3a and 3b.
The mean peak values were Ambrosia (44 grains/m3), Betula (105 grains/m3), Corylus (111 grains/m3), Cupressaceae (496 grains/m3), Poaceae (651 grains/m3); Platanus (1083 grains/m3). The pollen types with the lowest and highest average peak date were Corylus (48 DOY) and Cupressaceae (67 DOY); Poaceae (120 DOY) and Ambrosia (243 DOY), respectively. The taxa with the lowest (308 grains) and highest (8803 grains) average SPIn were Ambrosia and Poaceae, respectively (Table 3a and 3b).
By applying the regression formulas to the parameters which were shown to be significantly modified over time, it was possible to calculate the extent of this change over time, Table 4.

3.3. The Meteorologic Parameters over the Three Periods 1997-2005; 2006-2014 and 2015-2023.

The data obtained by dividing the observation period into three periods of nine years each, 1997-2005; 2006-2014 and 2015-2023, mean, minimum and maximum seasonal temperature are shown in Table 5.
Regarding temperature, Figure 7 shows the mean temperatures of the period 1997-2005 (T=14.4°C), 2006 -2014 (T=14.8°C), 2015-2023 (T=15.4°C) and over the study period (T=14.9°C). The difference was statically significant (1997-2005 vs 2015-2023), p = 0.000. Moreover, the average temperature 2006-2014 vs 2015-2023 was significantly different, p = 0.001. The comparison between 1997-2005 with 2006-2014 does not show any differences. Interestingly, the difference of average between 1997-2005 vs 2015-2023 and 2006-2014 vs 2015-2023 were (1°C and 0.6°C, respectively).
Figure 8 shows the mean of the relative humidity during the study period (1997-2023) RH = 66%; (1997-2005) RH = 67%; (2006-2014) RH = 68%; (2015-2023) RH = 63%.
No significant variation in rainfall were observed (data not shown).

3.4. The Pollen Seasons Parameters over the Three Period 1997-2005; 2006-2014 and 2015-2023.

Regarding pollen seasons parameters, a significant difference in start date (1997-2005 vs 2015-2023) for Betula (p = 0.02) Corylus (p = 0.01), and Poaceae (p = 0.002) and the start date (1997-2005 vs 2006-2014) for Cupressaceae (p= 0.041) (Figure 9).9a, 9b, 9c, 9d)) was observed.
The end date for Total pollen and Alternaria showed a significant difference (1997-2005 vs 2015-2023) and (1997-2005 vs 2006-2014), p = 0.005, p = 0.02 respectively) respectively (Figure 10).
Moreover, a significant difference in duration for Betula (p = 0.05) was observed (1997-2005 vs 2015-2023); for Poaceae (p = 0.03; p = 0.02) (1997-2005 vs 2006-2014 and 1997-2005 vs 2015-2023, respectively). Total pollen showed a significant difference (p = 0.035; p = 0.038) in duration (1997-2005 vs 2015-2023 and 2006-2014 vs 2015-2023, respectively) (Figure 11).
The taxa with significant difference about peak date were Alternaria between period 2006-2014 vs 2015-2023 (p = 0.01); Ambrosia (p = 0.02) (2006-2014 vs 2015-2023; Poaceae (p = 0.01; p = 0.000) (1997-2005 vs 2006-2014; 1997-2005 vs 2015-2023, respectively. Total pollen showed significant difference (1997-2005 vs 2015-2023), p = 0.001, Figure 12.
A significant difference for SPIn for Ambrosia (p = 0.01) was observed (1997-2005 vs 2015-2023); for Platanus (p = 0.02) (2006-2014 vs 2015-2023); for Corylus (p = 0.05; p < 0.05) during two period 1997-2005 vs 2015-2023 and 2006-2014 vs 2015-2023, respectively. Betula showed a significant difference (1997-2005 vs 2006-2014), p < 0.05, Figure 13.
A significant difference for peak value for Platanus (p = 0.010) (2006-2014 vs 2015-2023); for Poaceae (p = 0.041; p = 0.013) (1997-2005 vs 2015-2023; 2006-2014 vs 2015-2023, respectively) and for Total pollen (p= 0.005; p = 0.006) in the same periods (Figure 14).

3.3. Trends and Correlations Between Pollen Season Parameters

Regarding pollen season parameters Table 6 shows the significant Spearman’s rank correlations between different pollen season characteristics, in particular the comparison between start date and duration (b) resulted be significant in all type of pollen analysed.

4. Discussion

Allergies are a global public health concern, further in a context where climate change affects pollen loads and pollen seasonal parameters [60]. Allergies are expected to show a further 50% increase in prevalence every decade [61]. The severity of symptoms during pollen season poses a public health challenge considering the ongoing potency and duration of pollen season related to climate change [62]. Ragweed in the US is projected to see a 60–100% increase in pollen production by 2085 under current emissions trends [19]. Europe is experiencing similar trends, with increased allergenicity in grasses and changes in ragweed, birch and cypress trees. Greater pollen exposure is linked to poorer asthma and minimizing pollen exposure may improve asthma outcomes [8,52,53,54,55,56,57,58,59,60,61,62,63,64,65]. However, phenological changes depend on the response of individual taxa to temperature and other conditions, with convergence in some regions and divergence in others [18]; as a result, to evaluate the phenological behavior of the taxa at local level based on general considerations can lead to incorrect conclusions. In this context it is important to improve the understanding of the phenomena and if possible, the relationship with symptoms for which it is necessary to implement data and analytical tools even with artificial intelligence [15,66,67,68].
Our study combined a long data series of aerobiological monitoring performed in Parma from 1997 until 2023 (27 years) and allowing to show the trends of the seasonal pollen characteristic and some climatic parameters over time. This study has delved into other studies that examined trends in Parma considering more limited period of study [43,44,45,48].
The mean, minimum and maximum temperatures increased significantly during the period studied. Some seasonal parameters also changed significantly. For example, the significant positive trend in mean summer and autumn temperatures over the entire period 1997-2023 were observed. The relative humidity decreased significantly, while rainfall showed a decrease yet not significantly. Especially about precipitation, the patterns changed. A general trend towards decreasing precipitation could be observed, but the wide variability of the data does not yet allow for significant evidence. For example, even in a drought context, the cumulative value of 12/10/2024 was 930.9 mm, higher than the 1991-2020 median value of 642.4 mm [69]. All this represents significant data for the systemic effects it can cause.
The impact on the start and end of the pollination season, and consequently on its duration, shows a lengthening in most cases, only for Betula was a shortening evidenced. In both cases, however, these are marked changes and already of the magnitude of what some authors have hypothesised for the end of the century [19]. Equally evident are the changes in the peak values and dates, and for the SPIn of Corylus.
The division into three periods was done to assess the continuity of any trends within the longer observation period. In fact, in some cases, the trends were not homogeneous, and it could be observed that the trend over a longer period can hide opposite trends over shorter periods, as for relative humidity. Making a comparison with our previous study which analyzed the same parameters up to 2011 we can observe that the maximum average temperature observed during that study [48] (15.7°C) is very close to what was the average of the last 9 years (15.4°C).
As regards the seasonal pollen parameters, some of them are confirmed. SPIn is increasing for Corylus. The start date was anticipated for Corylus and Poaceae and postponed for Betula. The end date was postponed for Poaceae and Total pollen. The duration was shorter for Betula, longer for Poaceae and Total pollen. The peak date was precocious for Poaceae and Total pollen, and later for Ambrosia. The peak value was reduced for Poaceae. As for climatic parameters, the division in three periods allows us to observe some not homogeneous behavior of the trends such as SPIn and peak value for Platanus, peak date for Alternaria or start date for Cupressaceae.
Poaceae, Corylus and Total pollen appear affected by climate change, while Platanus and Alternaria spores look not to be influenced at this time by climate change. No difference between tree and grasses pollen beahviour was assessed.
Regarding the correlation between different pollen season parameters, it is worth emphasizing that all the taxa considered correlate for start date and duration, and for end date/duration, start date/date of peak, SPIn/peak value, 6 out of the 8 of the taxa considered correlate, in some cases positively and in other negatively.
It is still unknown the complex interactions of pollen, meteorological variables, and air pollutants in the changing environment.
Considering the effect of climate change on the long-term trends in pollen levels and emerging viral infection, it is crucial to forecast and eliminate the associated risk for human health in future and take appropriate measures to reduce it [57].

5. Conclusions

The ongoing climate change is impacting planetary health with dangerous consequences at different levels. The latest Report of the Intergovernmental Panel for Climate Change [70] states that bioaerosols, and among which, airborne pollen and fungal spores are among the threats to human health. The World Health Organization has pointed out specific environmental diseases, emphasizing the synergy of several factors towards an unhealthy environment and their impact on human health [71].
Without wishing to enter a discussion on who or what is most responsible for climate change, it can be stated without doubt by literature and the results of our study that climate change is a reality with which needs to be faced. The repercussions are evident in many areas, including human health, which also goes hand in hand with that of animals and plants. To stay with the latter, the effects observed on them can also pose a risk to human health. However, it is also important to state that generalizations must be avoided and that expressing oneself in terms of trends, as is often the case, exposes to errors of assessment in the short and medium term. The analysis on pollen long time series compared to those obtained in other geographical areas and climatic conditions, albeit over a limited territory, could provide a useful contribution to better characterize the impact of climate change on human health with a One Health perspective.
We believe that the results of our study represent a contribution to better understanding the impact of climate change with a potential impact on human health by environmentally changing conditions. Moreover, it should be considered that not only the seasonal respiratory allergies may be related to the variation of climate and its impact on pollen load and pollen season, but also it could be related to chronic respiratory disease and cardiovascular diseases [72,73]. Long-term data sets obtained from pollen monitoring using the method developed in the 1950s are still a benchmark about this topic but new real-time bioaerosol monitoring systems based on machine learning and artificial intelligence are rapidly evolving and will soon become a reality. This could probably change our knowledge, but that will be another story.

Author Contributions

For research articles with several authors, a short paragraph specifying their individual contributions must be provided. The following statements should be used “Conceptualization, R.A.; methodology, R.A., A.C., M.M.; validation, R.A., M.M., A.C and C.P.; investigation, R.A., A.C. and M.M.; data curation, , R.A., A.C., M.M.; writing—original draft preparation, , R.A., A.C., M.M.; writing—review and editing, R.A. and C.P.; visualization, R.A., M.M., A.C. M.E.C., L.V., and C.P.; supervision, R.A., M.M., A.C. M.E.C., L.V. P.A., R.Z. and C.P. All authors have read and agreed to the published version of the manuscript.”

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable for studies not involving humans or animals.

Informed Consent Statement

Not applicable for studies not involving humans. You might also choose to exclude this statement if the study did not involve humans.

Data Availability Statement

The data sets used in this article are not readily available because [including reason, e.g., the data are part of an ongoing study

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Map of Italy highlighting the city of Parma’s position.
Figure 1. Map of Italy highlighting the city of Parma’s position.
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Figure 2. Annual average of mean temperatures (a), relative humidity (b) and total rainfall (c) recorded in Parma from 1997 to 2023 (for rainfall until 2022). Also, minimum (Figure 3a) (p = 0.001, r = 0.7173) and maximum (Figure 3b) (p = 0.006, r = 0.5183) temperatures significantly increased over the study period.
Figure 2. Annual average of mean temperatures (a), relative humidity (b) and total rainfall (c) recorded in Parma from 1997 to 2023 (for rainfall until 2022). Also, minimum (Figure 3a) (p = 0.001, r = 0.7173) and maximum (Figure 3b) (p = 0.006, r = 0.5183) temperatures significantly increased over the study period.
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Figure 3. Minimum and maximum temperature recorded in Parma from 1997 to 2023.
Figure 3. Minimum and maximum temperature recorded in Parma from 1997 to 2023.
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Figure 4. The mean of the seasonal temperatures recorded in Parma during from 1997 to 2023.
Figure 4. The mean of the seasonal temperatures recorded in Parma during from 1997 to 2023.
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Figure 5. The mean of seasonal rainfall recorded in Parma from 2003 to 2022.
Figure 5. The mean of seasonal rainfall recorded in Parma from 2003 to 2022.
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Figure 6. The mean temperatures of summer (a) and of autumn (b) seasons during the period 1997-2023.
Figure 6. The mean temperatures of summer (a) and of autumn (b) seasons during the period 1997-2023.
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Figure 7. Mean temperature for each study period.
Figure 7. Mean temperature for each study period.
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Figure 8. Mean of the relative humidity for each study period.
Figure 8. Mean of the relative humidity for each study period.
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Figure 9. Start date for Corylus (a), Betula (b), Cupressaceae (c) and Poaceae (d) for each study period.
Figure 9. Start date for Corylus (a), Betula (b), Cupressaceae (c) and Poaceae (d) for each study period.
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Figure 10. End date for Alternaria spores (a) and Total pollen (b) for each study period.
Figure 10. End date for Alternaria spores (a) and Total pollen (b) for each study period.
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Figure 11. Duration for Betula (a), Poaceae (b) and Total pollen (c) for each study period.
Figure 11. Duration for Betula (a), Poaceae (b) and Total pollen (c) for each study period.
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Figure 12. Peak date for Alternaria spores (a), Ambrosia (b), Poaceae (c) and Total pollen (d) for each study period.
Figure 12. Peak date for Alternaria spores (a), Ambrosia (b), Poaceae (c) and Total pollen (d) for each study period.
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Figure 13. SPIn for Ambrosia (a), Betula (b), Corylus (c) and Platanus for each study period.
Figure 13. SPIn for Ambrosia (a), Betula (b), Corylus (c) and Platanus for each study period.
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Figure 14. Peak value for Platanus (a), Poaceae (b) and Total pollen (c) for each study period.
Figure 14. Peak value for Platanus (a), Poaceae (b) and Total pollen (c) for each study period.
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Table 2. Seasonal characteristics (start date, duration, end date and peak date) of selected taxa recorded in Parma from 1997 to 2023. The assessed parameters over time were Mean, Standard Deviation (SD), R2 value, Slope of the regression, Standard Error of the regression Slope (SE), p value (p) and Number of years of data (N). In bold were highlighted the significant values.
Table 2. Seasonal characteristics (start date, duration, end date and peak date) of selected taxa recorded in Parma from 1997 to 2023. The assessed parameters over time were Mean, Standard Deviation (SD), R2 value, Slope of the regression, Standard Error of the regression Slope (SE), p value (p) and Number of years of data (N). In bold were highlighted the significant values.
a Start date (DOY)
Grains Mean SD R2 slope SE p N
Ambrosia 219 19.0 0.04 0.52 3.71 0.28 26
Betula 82 19.5 0.32 1.4 3.75 0.00 27
Corylus 28 15.7 0.16 -0.8 3.02 0.03 27
Cupressaceae 42 19.9 0.04 -0.11 3.83 0.82 27
Platanus 91 9.10 0.00 0.03 1.75 0.88 27
Poaceae 109 6.59 0.19 -0.37 1.26 0.02 27
Total pollen 69 18.8 0.01 -0.31 3.85 0.52 24
Alternaria 168 12.6 0.00 -0.12 2.43 0.69 27
b
Duration (Days)
Grains Mean SD R2 slope SE p N
Ambrosia 47 19.2 0.12 -0.87 3.7 0.07 26
Betula 41 19.6 0.16 -0.99 3.7 0.04 27
Corylus 50 17.3 0.09 0.66 3.33 0.12 27
Cupressaceae 125 47.8 0.04 1.30 9.2 0.27 27
Platanus 20 6.68 0.00 0.00 1.28 0.96 27
Poaceae 95 20.6 0.23 1.25 3.9 0.01 27
Total pollen 165 24.9 0.16 1.23 5.09 0.05 24
Alternaria 115 20.6 0.00 0.17 3.97 0.74 27
c
End date (DOY)
Grains Mean SD R2 slope SE p N
Ambrosia 266 12.9 0.04 -0.36 2.54 0.28 26
Betula 122 10.3 0.01 0.38 1.99 0.13 27
Corylus 79 13.1 0.08 -0.18 2.52 0.57 27
Cupressaceae 167 41.3 0.05 1.19 7.95 0.25 27
Platanus 111 6.5 0.00 0.02 1.26 0.88 27
Poaceae 204 18 0.14 0.88 3.47 0.04 27
Total pollen 234 16.9 0.19 0.92 3.45 0.03 24
Alternaria 283 22.3 0.00 0.04 4.29 0.93 27
d
Peak date (DOY)
Grains Mean SD R2 slope SE p N
Ambrosia 243 8.18 0.24 0.50 1.60 0.01 26
Betula 102 13.00 0.07 0.43 2.50 0.18 27
Corylus 48 17.19 0.02 -0.03 3.30 0.42 27
Cupressaceae 67 12.89 0.03 -0.3 2.48 0.34 27
Platanus 99 7.51 0.01 -0.12 1.44 0.50 27
Poaceae 120 6.02 0.34 -0.44 1.15 0.00 27
Total pollen 109 9.92 0.31 -0.69 2.02 0.00 24
Alternaria 236 27.2 0.06 -0.84 5.24 0.21 27
Table 3. Seasonal characteristics (Peak value, SPIn,) of selected pollen taxa recorded in Parma from 1997 to 2023. The assessed parameters over time were Mean, Standard Deviation (SD), R2 value, Slope of the regression, Standard Error of the regression Slope (SE), p value (p) and Number of years (N). In bold are highlighted the significant values.
Table 3. Seasonal characteristics (Peak value, SPIn,) of selected pollen taxa recorded in Parma from 1997 to 2023. The assessed parameters over time were Mean, Standard Deviation (SD), R2 value, Slope of the regression, Standard Error of the regression Slope (SE), p value (p) and Number of years (N). In bold are highlighted the significant values.
a Peak value (grains/m3)
Pollen type Mean SD R2 slope SE p N
Ambrosia 44 29 0.02 0.62 5 0.41 26
Betula 105 85 0.00 -0.26 16 0.90 27
Corylus 111 67 0.02 1.45 13 0.39 27
Cupuressaceae 496 509 0.06 16.26 98 0.20 27
Platanus 1083 757 0.02 -15.14 146 0.42 27
Poaceae 651 277 0.21 -16.03 53 0.02 27
Total pollen 2883 1242 0.10 -50.57 253 0.11 24
Alternaria 956 488 0.00 -3.27 93 0.79 27
b SPIn (grains)
Pollen type Mean SD R2 slope SE p N
Ambrosia 308 192 0.07 6.49 38 0.18 26
Betula 747 523 0.00 -4.63 100 0.72 27
Corylus 902 571 0.20 31.07 110 0.02 27
Cupressaceae 3638 2913 0.09 113.02 561 0.11 27
Platanus 5707 3498 0.00 -37.6 673 0.67 27
Poaceae 8803 2936 0.00 14.84 565 0.84 27
Total pollen 51044 14035 0.00 -138.58 2865 0.71 24
Alternaria 24089 10472 0.00 341 2015 0.19 27
Table 4. Pollen season parameters changed significantly through study period 1997 - 2023.
Table 4. Pollen season parameters changed significantly through study period 1997 - 2023.
Pollen types SPIn
Pollen * day/m3 		Start date
DOY		 Duration
DAYS		 End date
DOY		 Peak date
DOY		 Peak value
Pollen/m3
Ambrosia 22.7 13.1
Betula 36.5 -25.0
Corylus 895.3 -12.7 17.2 4.8
Cupressaceae 36.5
Poaceae -9.6 32.6 22.9 11.6 -416.8
Total pollen 32.2 24.1 18.0
Analysing the number of days with daily pollen concentration of Poaceae > of 10, 30 and 50 pollen/m3 respectively we observed a significant increase over the whole period excluding data of 2023 (p= 0.050, p = 0.048, p = 0.050, respectively).
Table 5. The means, maximum and minimum seasonal temperatures recorded in Parma during 1997-2005, 2006-2014, 2015-2023, respectively.
Table 5. The means, maximum and minimum seasonal temperatures recorded in Parma during 1997-2005, 2006-2014, 2015-2023, respectively.
Temperature Winter Spring Summer Autumn
Mean Max Min Mean Max Min Mean Max Min Mean Max Min
1997-2005 4.3 8.3 1.5 14.4 20.0 9.6 24.6 30.6 19.4 14.2 18.3 11.1
2006-2014 4.1 7.4 1.7 14.8 20.1 10.2 24.9 30.9 19.7 15.0 19.2 11.7
2015-2023 5.3 9.1 2.7 14.6 19.9 9.8 26.0 31.8 20.5 15.5 20.0 12.0
Table 6. The results of Spearman’s rank correlation test etween different pollen season parameters.
Table 6. The results of Spearman’s rank correlation test etween different pollen season parameters.
Pollen type a b c d e f g h i j k l m n o
Ambrosia ns ** -0.54 ns ns ns ***0.61 *-0.44 *-0.44 ns *-0.42 *-0.39 ns ns *** 0.77 ns
Betula ns *** -0.79 ns ns *0.43 ns ns ns ns ns ns ns ns *** 0.93 ns
Corylus ns *** -0.66 ns ns ***0.67 **0.53 ns ns *0.40 ns ns ns ns ***0.90 ns
Cupressaceae ns *** -0.71 ns * -0.41 ns ***0.73 ns ns ns ns ns ns *-0.46 ***0.86 ns
Platanus ***0.69 ***-0.66 ns ns ***0.73 ns ns ns ***0.72 ns *-0.41 ns ns ***0.82 ns
Poaceae ns ***-0.60 ns ns ***0.73 ***0.91 ***-0.55 ns ns ns *-0.48 *-0.44 ns ns ns
Total pollen ns ***-0.68 *0.45 ns **0.52 ***0.67 ns ns ns ns *-0.51 **-0.52 ns ns *0.47
Alternaria ns **-0.53 ns ns ns **0.52 ns ns ns ns ns ns ns ***0.58 ns
(a) start date/end date; (b) start date/duration; (c) start date/peak value; (d) start date/SPIn; (e) start date/date of peak; (f) end date/duration; (g) end date/peak value; (h) end date/SPIn; (i) end date/date of peak; (j) duration/SPIn; (k) duration/peak value; (l) duration/date of peak; (m) SPIn/date of peak; (n) SPIn/peak value; (o) peak value/date of peak. * p < 0.05, ** p < 0.01, *** p < 0.001, ns: not significant.
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