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The aim of this work was to study the impact of temporal lag between the exposition to air pollutants and the children admitted to the emergency room of Hospital Nª Srª Rosário pediatric service, in Barreiro, Portugal, with symptoms of respiratory problems. The two variables were recorded by the medical staff and by an air quality monitoring station, in the same periods. From the results, a moderate correlation between different symptoms of respiratory diseases (sdr, cough, and asthma) and pollutants was found, reaching maximum values after temporal lags of 2 to 6 days. The strongest correlation for lag 0 (consequences on the same day) rises for the symptomatology of asthma, reaching the highest values for COmax (ρ = 0.26) and COpeak (ρ = 0.25). Also, an important correlation was found for NOx, NOx med and peak NOx (ρ = 0, 21). The correlation with PM10 shows an unrepresentative value (ρ = 0.09), being negative for O3max (ρ = −0.23) and O3peak (ρ = − 0.22), as well as for SO2med (ρ = −0.12). Considering temporal lags of 1 to 8 and 15 days, overall, the maximum correlations between symptoms and NO, NO2, NOx, CO, and PM10 occur after temporal lags of 2 to 6 days, being constant or negative to SO2 and O3.
Lisbon Superior Institute of Engineering, Lisbon, Portugal
CINEA-IPS, Energy and Environment Research Centre, IPS Campus, Setúbal, Portugal
Rita Cerdeira
Independent Researcher, Setúbal, Portugal
Luís Coelho
CINEA-IPS, Energy and Environment Research Centre, IPS Campus, Setúbal, Portugal
Mechanical Engineering Department, Polytechnic Institute of Setúbal, Higher School of Technology, Setúbal, Portugal
*Address all correspondence to: joao.garcia@isel.pt
1. Introduction
There are several air pollutants that can have a significant impact on human health [1, 2], including particulate matter (PM) which can be inhaled into the lungs and cause respiratory and cardiovascular problems, as well as increase the risk of lung cancer [3, 4, 5]. Nitrogen oxides (NOx) can irritate the lungs and aggravate asthma and other respiratory problems. It can also contribute to the formation of ground-level ozone, which can cause respiratory and cardiovascular problems [6]. Sulfur dioxide (SO2) can irritate the respiratory system and aggravate asthma and other respiratory problems [7]. Carbon monoxide (CO) can be deadly in high concentrations, but even at low concentrations, it can cause headaches, dizziness, and nausea. Also, Ozone (O3) can cause respiratory and cardiovascular problems, especially in people with preexisting conditions such as asthma [8, 9]. These health effects of these pollutants can vary depending on factors such as the concentration and duration of exposure, age, and preexisting health conditions [10]. It is known that children, the elderly, and the people with chronically ill, particularly respiratory patients, constitute populations that are very sensitive to atmospheric pollution and are therefore usually chosen as a sample for studies in this area [11, 12]. It is known that children, a sensitive population, are more vulnerable to the effects of atmospheric pollution than adults, for several reasons, from the time they spend outdoors to the anatomy and physiology of the respiratory system, which is still under development. In addition, children have higher ventilation rates than adults and the short stature of children further increases their exposure to traffic emissions. All such factors contribute to the triggering of episodes of respiratory distress more frequently, even in the present of lesser pollutant concentrations [13].
The objective of this work was to study the relation between the number of children admitted to the emergency room (ER) of the pediatric service of Hospital Nª Srª do Rosário in Barreiro, with symptoms of respiratory problems, and the levels of atmospheric pollution, recorded by the air quality monitoring network, in the city of Barreiro.
A team of pediatricians of Hospital Nossa Sra. do Rosário, in Barreiro, recorded daily, over a period of 20 months, the number and type of symptoms of children under the age of 15 years who were admitted to the hospital’s pediatric emergency service. The children observed who had respiratory complaints of noninfectious etiology were classified according to their age, gender, area of residence, and type of symptom. As for symptoms, only three types of symptoms were considered: cough; respiratory distress (SDR), and asthma. Initially, the data collection by physicians was carried out with a frequency of two or three times a week. In a later phase of the project, collection became daily. As mentioned earlier, the tanking of symptoms by the medical team considered three groups only: cough, asthma, and breathing difficulty, although several international studies rank symptoms according to the List of International Statistical Classification of Diseases (ICD-11) [14], limiting the subjectivity inherent to the classification in these groups. In this case, a decision was made to simplify the classification due to the lack of a supporting computer system.
In parallel and during the same period, the values of pollutant concentrations from the Barreiro air quality network were used. The values of carbon monoxide (CO), nitrogen dioxide (NO2), sulfur dioxide (SO2), particles (PM10), and ozone (O3) were considered, as well as the meteorological variables of temperature and relative humidity. Meteorological variables, namely temperature, relative humidity, wind speed and direction, were provided by the Portuguese Institute of the Sea and the Atmosphere and/or by the CPPE of Barreiro. Thus, and during this period, the following data were recorded regarding the variables described in Table 1, for all observation stations.
Unit
Variable description
Date
Observation date
Total
#
Total number of children observed
0–2
#
Number of children observed between 0 and 2 years old
3–5
#
Number of children observed between 3 and 5 years old
6–10
#
Number of children observed between 6 and 10 years old
11–15
#
Number of children observed between 11 and 15 years old
Cough
#
Number of children observed with cough symptoms
sdr
#
Number of children observed with of respiratory distress syndrome symptoms
Asthma
#
Number of children observed with asthma symptoms
Intern
#
Number of children admitted to hospital after observation
F
#
Number of female children observed
M
#
Number of male children observed
VEL (IM)
(km/h)
Average daily wind speed
DIREC
Prevailing daily wind direction
TEMP
(°C)
Average daily temperature
TEMPMAX
(°C)
Maximum daily temperature
TEMPMIN
(°C)
Minimum daily temperature
HUM
(%)
Humidity at 9 am
HUMmax
(%)
Humidity at 6 am
Rad
(watt/m2)
Total daily radiation
SO2 med
(μg/m3)
Average daily value of the average of all stations
NO med
(μg/m3)
Average daily value of the average of all stations
NO2 med
(μg/m3)
Average daily value of the average of all stations
NOx med
(μg/m3)
Average daily value of the average of all stations
PM10 med
(ug/m3)
Average daily value of the average of all stations
CO (max) med
(μg/m3)
Maximum daily value of eight-hour average by eight-hour averages.
O3 (max) med
(μg/m3)
Maximum daily value of the eight-hour average
CO (peak) med
(μg/m3)
Maximum daily value, without considering eight-hour averages
O3 (peak) med
(μg/m3)
Maximum daily value, without considering eight-hour averages
NOx (peak) med
(μg/m3)
Maximum daily value
Table 1.
General description of the variables under study.
A summary of the descriptive statistics on the study variables is shown in Table 2.
FS
No. obs.
Ave.
Stand. devi.
Min. value
Max. value
Int.
Var. coef.
Median
Total
399
2.78
2.05
0
12
12
0.73
2
0–2
280
1.85
1.29
1
9
8
0.70
1
3–5
184
1.45
0.68
1
5
4
0.47
1
6–10
152
1.39
0.73
1
4
3
0.52
1
11–15
89
1.27
0.73
1
5
4
0.58
1
Cough
197
1.64
0.95
1
5
4
0.58
1
sdr
245
1.75
1.14
1
9
8
0.65
1
Asthma
197
1.63
0.91
1
5
4
0.56
1
Intern
38
1.11
0.31
1
2
1
0.28
1
F
247
1.71
0.99
1
6
5
0.58
1
M
301
2.14
1.34
0
9
9
0.63
2
VEL (IM)
730
4.87
6.58
0
40.5
40.5
1.35
3.1
DIREC
717
4.81
2.74
1
8
7
0.57
6
TEMP
721
17.72
4.48
7.7
31.0
23.3
0.25
17.4
TEMPMAX
721
22.15
5.78
9.1
40.0
30.9
0.26
21.6
TEMPMIN
721
14.16
4.06
0.0
26.0
26.0
0.29
14.1
HUM
723
71.93
18.26
6.8
99.1
92.3
0.25
68.0
HUM max
711
84.71
12.58
39.3
99.1
59.8
0.15
87.6
Rad
153
5092.2
1778.02
697.0
7098.0
6401.0
0.35
5802.0
SO2 med
641
12.61
12.26
1.0
88.6
87.6
0.97
8.4
NO med
641
13.98
15.39
1.1
136.1
135.1
1.10
9.1
NO2 med
641
29.98
11.94
5.8
72.5
66.7
0.40
28.5
NOx med
641
48.94
27.92
9.1
188.9
179.8
0.57
42.8
PM10 med
617
38.04
21.88
3.5
152.5
148.9
0.58
31.7
CO (max) med
641
491.52
367.15
167.6
2684.1
2516.5
0.75
362.8
O3 (max) med
641
68.73
25.63
4.2
169.7
165.5
0.37
68.3
CO (peak) ad
641
668.09
505.69
195.0
3740.5
3545.5
0.76
503.5
O3 (peak) med
641
81.79
29.88
6.7
241.3
234.7
0.37
79.0
NOx (peak) med
641
88.78
54.91
9.0
452.8
443.8
0.62
79.4
Table 2.
Descriptive statistics of the variables collected.
To analyze and process the data, the statistical treatment programs R, SPSS, and Excel were used. The data analysis shows that during the period studied, 1101 children were assisted at the HNSR emergency service, with a daily average of 2.78 children, ranging from 0 to 12 children per day. One of the main challenges in understanding the effect on health of each of the pollutants and meteorological variables is related to the interdependence between these variables. Therefore, in a preliminary phase of this study, and in an attempt to understand the main relation between the most relevant variables, graphs representing two variables were generated, which allow verifying the joint evolution of these very variables.
In this way, Figure 1 represents the average daily temperature evolution as a function of daily solar radiation. One can see that globally there is an increase of temperature with the increase of radiation. A Pearson correlation with the value ρ = 0.46 (95% CI: 0.33 to 0.58) is observed for these two variables.
Figure 1.
Average temperature vs. radiation.
Figure 2 represents the evolution of the daily maximum temperature as a function of daily solar radiation. One can see that globally there is an increase of the maximum temperature with the increase of radiation. A Pearson correlation is observed for these two variables with the value ρ = 0.55 (CI at 95%: 0.42 to 0.64).
Figure 2.
Maximum temperature vs. radiation.
Figure 3 plots the evolution of the average daily value of PM10 concentration (average of all seasons) as a function of average daily temperature. One can see that, globally, there is an increase in the concentration of particles with the increase in temperature. However, looking at the evolution of the curve that best fits, one observes that for temperatures below 13°C there is a slight increase in particle concentration. This can be explained by the conditions of atmospheric stability that hinder the dispersion of particles. There is a Pearson correlation for these two variables with the value ρ = 0.22 (95% CI: 0.15 to 0.30), even though the values of average, maximum, and minimum daily temperatures show correlations above ρ = 0.9 between them. The other graphs with these temperatures show similar evolutions, as displayed in Figure 4, which presents the evolution of the average particle concentration with the daily maximum temperature. In this case, a Pearson correlation with the value ρ = 0.30 (95%CI 0.23 to 0.37) is seen for these two variables.
Figure 3.
Average concentration of particles vs. average temperature.
Figure 4.
Average concentration of particles vs. maximum temperature.
The detailed analysis of all data shows that the QA station that has the highest correlation between the average daily temperature and the PM10 concentration values (daily averages) is the QA station of Lavradio, so only the graph corresponding to this station is displayed (Figure 5).
Figure 5.
Particle concentration (Lavradio station) vs. temperature.
For this specific station, the Pearson correlation coefficient between these variables has the value ρ = 0.44 (95% CI: 0.36 to 0.52).
Figure 6 shows the evolution of the maximum ozone concentration (average of stations) as a function of average daily temperature. It appears that there is a direct relation between the concentration of ozone in the air and the average daily temperature, with a visible increase in concentration values with increasing temperature. This relation is indicated by the value of Pearson’s correlation coefficient with the value ρ = 0.53 (95% CI: 0.47 to 0.58). This relation is even stronger for the QA station at the Hospital, where the correlation between ozone concentration and mean temperature has a Pearson correlation coefficient of ρ = 0.57 (95% CI: 0.52 to 0,63) as can be seen in Figure 7.
Figure 6.
Maximum ozone concentration (average of stations) vs. average air temperature.
Figure 7.
Maximum ozone concentration (hospital QA station) vs. average air temperature.
Figure 8 shows the values of the Pearson correlations (ρ) between the different variables collected at the hospital regarding the medical component. The analysis of Figure 8 highlights important correlations between some variables analyzed. It appears that, for example, in the case of asthma, the correlation values increase with the increasing age of age groups. The correlation starts with a value of ρ = 0.13 for the 0–2 years age group, rising to ρ = 0.26 (3–5 years) and continuing to rise to ρ = 0.39 (6–10 years) reaching ρ = 0.47 (11–15 years), thus trending toward an increase in asthma symptoms, in visits to the hospital emergency room, with the increasing age of children.
Figure 8.
Medical data correlations.
Also noteworthy is the strong correlation between sdr symptoms and children aged 0 to 2 years (ρ = 0.72) and between cough symptoms and children aged 0 to 2 years (ρ = 0.47). There is also a strong correlation between hospital admission and children aged 6 to 10 years, indicating that older children only resort to the hospital emergency service in situations where seriousness justifies hospital admission, while younger children resort to the hospital emergency for all types of cases, including those that do not require hospital admission.
Figure 9 shows the values of the Pearson correlations (ρ) between the different variables related to the concentration of pollutants considering the mean values of all stations (global). Analyzing the values for Pearson’s correlation coefficient (ρ), one can see that there are strong correlations between the average and peak values, for the same pollutant. The Pearson correlation coefficient values ρ = 0.91 (CO), ρ = 0.95 (O3), and ρ = 0.85 (NOx) (mean and peak) which expresses that, in fact, the values are very interdependent. This analysis allows concluding that the results obtained are not very different, using mean values of pollutant concentrations or peak values.
Figure 9.
Correlations between pollutants.
Carrying out a global assessment of the relation between pollutants, one can see that, in general, there are correlations between different pollutants, highlighting some important correlation values. For example, between the CO and NOx families (NO, NO2 and NOx), the values of ρ = 0.82 (COmax with NOmed), ρ = 0.80 (COmax with NOx med), ρ = 0.84 stand out (COpeak with NOmed), and ρ = 0.77 (COpeak with NOx peak), indicating a strong relation between CO concentrations and NOx families in an urban environment.
As for SO2, it does not show strong correlations with any other pollutant, except for the value of the correlation coefficient of ρ = 0.42 between SO2 and O3 peak concentrations and ρ = 0.36 between SO2 and O3 max concentrations.
Regarding PM10, the following important correlations are observed, ρ = 0.36 (PM10 with NOx med), ρ = 0.47 (PM10 with NO2 med), ρ = 0.46 (PM10 with NOx med), ρ =0.39 (PM10 with COmax), ρ = 0.38 (PM10 with COpeak), and ρ = 0.41 (PM10 with NOx peak), indicating a relation between PM10 concentrations and CO concentrations and families of NOx. As for the correlations with ozone, the values of ρ = 0.28 (PM10 with O3 peak) and ρ = 0.18 (PM10 with O3 max) suggest a lower correlation between these pollutants. Also, with regard to SO2, the value of ρ = 0.29 (PM10 with SO2 med) indicates a lower correlation between PM10 concentrations and SO2 concentrations.
One can thus see that PM10 concentrations are correlated with all pollutants, with the highest correlation for NO2 and the lowest correlation for O3. It should also be noted that ozone has negative correlations with NO med (−0.58) and NOx med (−0.46), which can be explained by the fact that NOx are precursors of Ozone, causing the formation of each molecule of O3 to lead to a consequent decrease in NOx.
Figure 10 shows the values of the Pearson correlations (ρ) between all variables analyzed in this study (medical and pollutant variables). Being an extensive figure, the strong correlations between the average concentration values for the different pollutants and the concentration values of said pollutants for the different air quality stations stand out.
Figure 10.
Correlations between all variables studied.
Figure 11 shows the Pearson correlation values (ρ) between the medical variables studied and the mean concentrations of pollutants measured at the stations. This figure is important because it allows analyzing the possible relation between the concentrations of pollutants and the symptoms studied. Analyzing the figure one can see, globally, that the more important values of correlation between the pollutants and the symptoms are obtained for the symptomatology of asthma, with the highest values for the correlation coefficient with COmax ρ = 0.26 and COpeak ρ = 0.25. There is also an important correlation between asthma and NOx, ρ = 0.21 (with NOx med), ρ = 0.21 (with NOx med) ρ = 0.21 (with NOx peak). The correlation with PM10, however, has an unrepresentative value of ρ = 0.09.
Figure 11.
Correlations of medical data with station averages.
Negative correlation values are also seen between asthma symptoms and O3 ρ = −0.23 (asthma and O3 max) and ρ = −0.22 (asthma and O3 peak), as well as a negative correlation between asthma and SO2 med (ρ = −,12).
Figure 12 shows the values of the Pearson correlations (ρ) between all variables referring to the atmospheric concentrations of the pollutants studied. An analysis of this figure reveals, as expected, the existence of important correlations between the average values of the concentrations of the different pollutants and the values of the concentrations of these same pollutants, in the different air quality stations.
Figure 12.
Correlations between seasons.
With the aim of studying the evolution of the number of children observed in hospital with the symptomatology of asthma, sdr, or cough, it was analyzed the number of days elapsed, considering temporal lags of 0, 1, 2, 3, 4, 5, 6, 7, and 15 days between the values of PM10 concentrations and the aforementioned medical variables.
Table 3 and Figure 13 show the evolution of the Pearson correlation coefficient (ρ) between the asthma variables and the average concentrations of pollutants (O3, SO2, NO, NO2, NOx PM10, and CO) for the temporal lags of 0, 1, 2, 3, 4, 5, 6, 7, and 15 days. It appears that, overall, the maximum correlations between asthma symptoms and the concentrations of pollutants NO, NO2, NOx, CO, and PM10 occur after lags of 2, 3, or 4 days (depending on the symptoms and the pollutant analyzed). The following Pearson’s correlation coefficient values are also observed, ρmax = 0.35 (asthma and CO, 2-day lag), ρmax = 0.32 (asthma and NO, 3-day lag), ρmax = 0.33 (asthma and NOx, lag 3 days), ρmax = 0.22 (asthma and NO2, lag 2 days), and ρmax = 0.18 (asthma and PM10, lag 4 days). There is, for these pollutants, an increase in Pearson’s correlation coefficient values (ρ), over the days until it reaches its maximum after 2, 3, or 4 days. For PM10 and NO2 there is a slight decrease in the correlation coefficient from lag 0 to lag 1, this coefficient increasing again from lag 2 onward.
lag 0
lag 1
lag 2
lag 3
lag 4
lag 5
lag 6
lag 7
lag 15
O3
−0.23
−0.19
−0.26
−0.26
−0.26
−0.27
−0.26
−0.25
−0.06
SO2
−0.12
−0.09
−0.10
−0.08
−0.09
−0.05
−0.08
−0.14
−0.06
NO
0.21
0.23
0.30
0.32
0.26
0.25
0.31
0.20
−0.02
NO2
0.14
0.09
0.22
0.21
0.22
0.18
0.15
0.12
0.01
NOx
0.21
0.20
0.30
0.33
0.28
0.25
0.30
0.21
0.00
PM10
0.09
0.00
0.06
0.12
0.18
0.11
0.09
0.06
−0.07
CO
0.26
0.28
0.35
0.33
0.27
0.34
0.29
0.26
0.12
Table 3.
Pearson’s correlation coefficient (ρ) between the variable mean concentrations of pollutants (O3, SO2, NO, NO2, NOx, PM10, and CO) and asthma for time lags of 0, 1, 2, 3, 4, 5, 6, 7, and 15 days.
Figure 13.
Evolution of the correlation coefficient between concentrations of air pollutants and asthma symptoms (lag 0 to 15 days).
For SO2 and O3 pollutants, it appears that Pearson’s correlation coefficient (ρ) remains relatively constant and negative throughout the period analyzed, (ρ approx −0.2; asthma and O3) and (ρ approx −0,1; asthma and SO2) for all lags analyzed, suggesting lack of short-term correlations between observations with asthma symptoms and atmospheric concentrations of these two pollutants.
Table 4 and Figure 14 show the evolution of the Pearson correlation coefficient (ρ) between the variable sdr and the mean concentrations of pollutants (O3, SO2, NO, NO2, NOx, PM10, and CO) for the temporal lags of 0, 1, 2, 3, 4, 5, 6, 7, and 15 days. One can see that, globally, the maximum correlations between the symptomatology of sdr and the concentrations of pollutants NO, NO2, NOx, CO, and PM10 occur after temporal lags of 4 days. Observing the following Pearson correlation coefficient values, ρmax = 0.20 (sdr and NOx, 4 day lag), ρmax = 0.17 (sdr and NO2, 4 day lag), ρmax = 0.15 (sdr and PM10, lag 4 days), ρmax = 0.14 (sdr and CO, lag 4 days), ρmax = 0.13 (sdr and NO, lag 4 days). For these pollutants (NO, NO2, NOx, PM10, and CO), there is a decrease in the correlation coefficient from lag 0 to lag 1, and this coefficient increases again from lag 2 and up to lag 4.
lag 0
lag 1
lag 2
lag 3
lag 4
lag 5
lag 6
lag 7
lag 15
O3
−0.08
−0.11
−0.11
−0.16
−0.15
−0.14
−0.14
−0.13
−0.17
SO2
−0.03
−0.04
−0.04
−0.11
0.01
−0.05
−0.17
−0.11
−0.09
NO
0.13
0.09
0.11
0.12
0.13
0.07
0.13
0.11
0.12
NO2
0.07
0.01
0.07
0.12
0.17
0.13
0.14
0.09
−0.02
NOx
0.15
0.09
0.13
0.16
0.20
0.14
0.19
0.15
0.10
PM10
0.13
0.00
0.07
0.14
0.15
0.12
0.05
0.08
−0.03
CO
0.16
0.08
0.14
0.11
0.14
0.09
0.12
0.10
0.15
Table 4.
Pearson’s correlation coefficient (ρ) between the variable mean concentrations of pollutants (O3, SO2, NO, NO2, NOx, PM10, and CO) and sdr for temporal lags of 0, 1, 2, 3, 4, 5, 6, 7, and 15 days.
Figure 14.
Evolution of the correlation coefficient between air pollutant concentrations and sdr symptomatology (lag 0 to 15 days).
For SO2 and O3 pollutants, one can see that Pearson’s correlation coefficient (ρ) remains relatively constant and negative throughout the period analyzed, for all lags considered, suggesting the inexistence of short-term correlations between observations with sdr symptoms and atmospheric concentrations of these two pollutants.
Table 5 and Figure 15 show the evolution of the Pearson correlation coefficient (ρ) between the cough variable and the mean concentrations of pollutants (O3, SO2, NO, NO2, NOx, PM10, and CO) for the temporal lags of 0, 1, 2, 3, 4, 5, 6, 7, and 15 days.
lag 0
lag 1
lag 2
lag 3
lag 4
lag 5
lag 6
lag 7
lag 15
O3
−0.03
−0.09
−0.11
−0.15
−0.16
−0.19
−0.17
−0.12
−0.14
SO2
−0.15
−0.11
−0.05
−0.02
−0.07
0.07
0.01
−0.02
−0.07
NO
−0.09
−0.02
0.03
0.03
0.06
0.15
0.18
0.06
0.10
NO2
−0.11
−0.01
0.02
0.02
0.05
0.02
0.09
−0.02
−0.05
NOx
−0.08
0.01
0.04
0.06
0.10
0.15
0.20
0.08
0.07
PM10
−0.11
−0.07
0.03
−0.03
−0.04
0.11
0.20
0.11
0.02
CO
−0.07
−0.03
−0.03
0.02
0.09
0.12
0.17
0.12
0.07
Table 5.
Pearson’s correlation coefficient (ρ) between the variable mean concentrations of pollutants (O3, SO2, NO, NO2, NOx, PM10, and CO) and cough for temporal lags of 0, 1, 2, 3, 4, 5, 6, 7, and 15 days.
Figure 15.
Evolution of the correlation coefficient between concentrations of air pollutants and cough symptomatology (lag 0 to 15 days).
One can see that, globally, the maximum correlations between the symptoms of cough and the concentrations of pollutants NO, NO2, NOx, CO, and PM10 occur after temporal lags of 6 days, except for SO2, which has the maximum correlation with a temporal lag of 5 days, albeit with a maximum correlation value of ρmax = 0.07 (cough and SO2, lag 5 days), which is not significant. In addition, the following Pearson correlation coefficient values stand out, ρmax = 0.20 (cough and PM10, 6-day lag), ρmax = 0.20 (cough and NOx, 6-day lag), ρmax = 0, 18 (cough and NO, lag 6 days), ρmax = 0.17 (cough and CO, lag 6 days), and ρmax = 0.09 (cough and NO2, lag 6 days), representing nonsignificant values.
For O3, it appears that Pearson’s correlation coefficient (ρ) remains relatively constant and negative throughout the period analyzed for all lags considered, indicating the inexistence of short-term correlations between observations with symptoms of cough and atmospheric concentrations of this pollutant.
The results presented in this chapter allow concluding some important aspects for the study and understanding the relation between the concentrations of atmospheric pollutants, meteorological variables, such as air temperature and solar radiation, and medical variables related to the different symptomatologies studied, in admissions of children in the ER, at Hospital do Barreiro (sdr, cough, and asthma). From the development and analysis of the results of this study, the following aspects stand out:
There is an increase of the ambient air temperature with the increase of solar radiation. This fact is visible in the values of Pearson’s correlation coefficient ρ = 0.46 (mean daily air temperature with radiation) and ρ = 0.55 (maximum daily air temperature with radiation).
There is an increase in the concentration of PM10 with the increase of the ambient air temperature. This is more relevant for temperatures above 25°C - ρ = 0.22 (average concentration of PM10 with average daily air temperature) and ρ = 0.30 (average concentration of PM10 with maximum daily air temperature). One can also see that for air temperatures below 13°C there is a slight increase in the concentration of particles, probably due to atmospheric conditions that hinder the dispersion of particles.
There is a strong correlation between the maximum concentration of ozone in the air and the average daily air temperature ρ = 0.53, with an increase in the values of ozone concentration with the increase in average air temperature.
There is a trend toward an increase in asthma symptoms, in visits to the hospital’s emergency room, with increasing age of children (ρ = 0.13 0–2 years, ρ = 0.26 3–5 years; ρ = 0.39 6–10 years; ρ = 0.47 11–15 years).
There are important correlations between the values of the concentrations of the different atmospheric pollutants, correlated among themselves, there being a strong relation between the concentrations of CO and the NOx families (ρ = 0.82 COmax with NOmed, ρ = 0.80 COmax with NOx med, ρ = 0.84 COpeak with NOmed, ρ = 0.77 COpeak with NOx peak). On the contrary, SO2 does not show strong correlations with any other pollutant, except for an important correlation value between peak SO2 and O3 concentrations (ρ = 0.42) and between SO2 and O3 max concentrations (ρ = 0,36). PM10 concentrations are correlated with the concentrations of all pollutants, with the highest correlation for NO2 (ρ = 0.47 PM10 with NO2 med) and the lowest correlation for O3 (ρ = 0.28 PM10 with O3 peak; ρ = 0.18 PM10 with O3 max).
The strongest correlations between the variables of health symptoms (sdr, cough, and asthma) and the concentrations of air pollutants for situations of lag 0 (consequences on the same day) are observed for the symptoms of asthma, presenting the highest values for the correlation coefficient with COmax (ρ = 0.26 asthma COmax) and with COpeak (ρ = 0.25 asthma COpeak). There is also an important correlation between asthma and NOx for situations of lag 0, ρ = 0.21 (asthma with NOx med), ρ = 0.21 (asthma with NOx med), and ρ = 0.21 (asthma with peak NOx). The correlation with PM10, on the other hand, has an unrepresentative value ρ = 0.09 (asthma with PM10) for the situation of lag 0 (same day);
For the situation of temporal lag 0 analyzed (consequences on the same day), negative correlation values are observed between asthma symptoms and O3 ρ = −0.23 (asthma and O3 max) and ρ = −0.22 (asthma and peak O3), as well as a negative correlation between asthma and SO2 med (ρ = −0.12);
Staggering the analysis among health symptoms (sdr, cough, and asthma) and the concentrations of air pollutants (O3, SO2, NO, NO2, NOx, PM10, and CO) considering temporal lags of 1, 2, 3, 4, 5, 6, 7, 8, and 15 days, overall, the maximum correlations between these symptoms and the concentrations of pollutants NO, NO2, NOx, CO, and PM10 occur after time lags ranging from 2 to 6 days depending on the pollutant and the symptomatology considered;
For SO2 and O3 pollutants, analyzing the evolution of the Pearson correlation coefficient (ρ) between these pollutants and the symptoms analyzed, for temporal lags of 1, 2, 3, 4, 5, 6, 7, 8, and 15 days, this correlation coefficient remains relatively constant or negative throughout the period analyzed, indicating lack of short-term correlations between observations with asthma symptoms and concentrations of atmospheric emissions of these two pollutants.
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Written By
João Garcia, Rita Cerdeira and Luís Coelho
Submitted: 13 April 2023Reviewed: 02 May 2023Published: 30 May 2023
Open access peer-reviewed chapter
