1. Introduction
In recent decades, urban air quality has become a central focus of environmental research, policy making, and public health advocacy. Among the myriad of pollutants affecting the urban atmospheres, O
3 stands out due to its adverse effects on respiratory health, agricultural productivity, and ecosystem integrity [
1,
2]. The formation of the tropospheric O
3 involves complex processes: the stratosphere–troposphere air masses exchange [
3] and photochemical reactions. These reactions involve the oxidation of carbon monoxide (CO), methane (CH
4), and volatile organic compounds (VOCs), catalyzed by nitrogen oxides (NO
x), which is the sum of nitric oxide (NO) and nitrogen dioxide (NO
2), as well as meteorological parameters such as temperature [
4]. In urban areas, daily variations in O
3 concentration and its precursors are correlated to rush hours (which cause an increase in road traffic emissions) and to the diurnal patterns of solar radiation and other weather factors affecting the efficiency of photochemical reactions. Besides this, O
3 and NO
x concentrations are also influenced by the planetary boundary layer variations and advective transport processes, which is affected by the dominant wind direction and regional orography [
5]. The O
3 precursors, such as organo nitrates, can be transported over long distances due to their longer lifetime, becoming possible sources of O
3 in a region far from their production sites [
6,
7]. It is useful to monitor the air quality and meteorological features with more detail in urban area and background countrysides to evaluate the influence of urban emission sources on the surrounding rural environment, where the local emissions of O
3 precursors are negligible [
8]. In North-Eastern India, the yearly O
3 and NO
x concentrations have been monitored in three urban sites (Gauhati, Tezpur, and Aizwal), not affected by industrial emissions. It has been found that surface O
3 is not entirely due to local production and that during pre-monsoon and winter seasons, the transport of air masses from industrial areas is the cause of the higher pollutions level registered in the three monitoring sites [
9]. Studies conducted in the Los Angeles Basin, a region notorious for its smog and air quality challenges, have provided critical insights into these dynamics. Notably, research by [
10,
11,
12] has shed light on the intricate balance of chemical precursors in O
3 formation and the pivotal role of temperature in modulating this balance. The Los Angeles Basin serves as an emblematic case study for understanding O
3 trends and the efficacy of air quality management strategies. Despite significant reductions in NO
x and VOC emissions due to stringent regulatory measures, O
3 levels in the basin remain stubbornly high, often exceeding national ambient air quality standards. The decline in NO
x observed over the last decade in Los Angeles, which has not been followed by a reduction in O
3, has also been observed in several other urban areas worldwide, including the urban area of Pescara, Central Italy—the object of the present study [
13]. The persistent O
3 exceedances highlight the non-linear relationship between O
3 formation and its precursors, underscoring the importance of a nuanced approach to emission control. Furthermore, studies underscore the impact of temperature, with warmer conditions exacerbating O
3 formation through enhanced photochemical reactions and increased emissions of temperature-dependent VOCs. In this work, we used a combination of the observations of O
3 and NO
x, and meteorological parameters in two sites—one in the urban area and the other inland and downwind of the coastal town—and a neural network technique to assess the origin of elevated O
3 concentration in the rural area compared with that in the urban site. The complex orography of the area and the emission mix due to domestic and industrial activities and those due to the presence of an international airport in the urban area, make this study interesting for strategies to mitigate O
3 pollution and protect public health in a different geographical context.
3. Results and Discussion
The local O
3 concentration was due to two main mechanisms that determined it: The local photochemistry and the regional transport. We investigated the O
3 production mechanism in a rural background site, considering both the local photochemistry and the regional transport from a metropolitan area, where the urban station is installed (
Table 1 and
Figure 1). In order to evaluate the relationship between O
3 and NO
2 and NO, we compared the diurnal trends of these compounds and NO
x in both stations (
Figure 4C).
Even if the mutual interactions in the troposphere between O
3, NO
2, and NO are complex and other chemical and deposition processes should be considered, from
Figure 4A, we can identify that, remarkably, in the rural background station, the concentration of O
3 is, on average, higher than the O
3 levels detected in the urban station. This can be explained as follows: Considering that the regional transport could move air masses, richer in O
3 and its precursors (i.e., NO
x), from the densely populated coastal area (US) to the inland hilly areas (RS). The relatively high NO concentration, measured even during the night at the US station (
Figure 4B), explains the lower O
3 measured in this site (
Figure 4A) that, by reacting with NO, produces nocturnal NO
2 (
Figure 4D). The higher NO concentration in the US can be explained by considering the position of this station, which is installed in a heavy traffic and industrial area of the town. On the other hand, at the background RS station, the NO (
Figure 4B) shows a low concentration with a small peak at around 08:00 a.m., corresponding to the morning rush hour. The NO
x is about five times lower than those measured in the US station, with a typical diurnal trend with the daytime and late afternoon peaks related to vehicle traffic emissions (
Figure 4C). Because of the low NO concentration in RS, the O
3 titration between midnight and 7 AM is significantly less efficient in RS than in US, and the O
3 concentration in this interval is about double than those sampled in US. At the same time, the local NO
2 and NO at RS is too low to explain the local O
3 level, suggesting that its origin should be due to its precursors transport from the US area to the background site (RS). These findings are confirmed by analysing the wind speed measured in the US and RS stations and the relationship between the O
3 measured at the rural downwind site and the wind direction (
Figure 5).
Because of the wind speed of the air masses reported at the US (
Figure 5A), it is possible to estimate that the NO
x produced in the US station by traffic and industrial emissions travelled for about 3 to 7 h to reach the rural station. Despite the chemistry of NO
x and O
3 being complex, the quick NO conversion to NO
2 and the resulting O
3 production is a well-known mechanism for explaining O
3 production during the transport process of its precursors, i.e., NO
2, which can cause a higher concentration of O
3 in the rural region, e.g., lacking local sources of O
3 precursors, downwind of an urban/industrial area. The time to transport air masses from the US to RS is in line with the NO
x lifetime expected in the troposphere.
Figure 5B) shows the relation between the O
3 measured at the RS station and the NO
2 to NO
x ratio measured at the urban area, where the main emissions of O
3 precursors could trigger the high O
3 concentration sampled in the rural site with very low local emission of NO. As expected, when the NO
2/NO
x ratio is low in the US, meaning that the NO emission is more significant (as also shown in
Figure 4B,D), the O
3 in the RS is higher, confirming the possible mechanism of O
3 production at RS from NO–NO
2 photochemistry during the air masses transport from US to RS.
Figure 5C shows the polar plot of the O
3 measured at the RS station. The highest values of O
3 occurs when the winds blow from North-East, i.e., from the urban site with higher NO
x. On the contrary, with winds coming from West (inland mountains), the O
3 concentration is much lower, confirming that in inland regions, the origin of O
3 is not related to local emissions, but due to transport from coastal areas with higher concentrations of NO
x and O
3.
Figure 6 shows an intercomparison between the O
3 measured at the RS during the period investigated in this case study and the O
3 measured at the same site during the 2018 selecting only air masses from the West, i.e., from the hill and mountainous areas of this region characterized by cleaner air. It is evident from
Figure 6 that the O
3 at the RS is strongly affected by the origin of the air masses and, in detail, by the NO and NO
2 emitted at the US, which is located east of the RS site.
To confirm these results, an FF neural network was employed to identify the mechanisms based on the high O
3 level measured at the rural downwind site. Different scenarios were simulated (
Table 2) in order to identify whether the O
3 concentration measured at the rural site was more important than the local chemistry, i.e., the local emission of NO and NO
2, or the O
3 production during the regional transport from the urban site where the O
3 precursor concentration, i.e., NO and NO
2, was significantly higher than the one measured at the RS. The O
3 at the rural site was modeled using different parameters as the FFNN model inputs, depending on the simulation scenarios (
Table 2).
The selection of specific parameters for each scenario in
Table 2 was guided by a scientific understanding of the mechanism behind the high O
3 concentration in the RS compared with the one measured at the US. In order to have a reference for the model performance, we included all of the parameters available to simulate the O
3 (Scenario 1). To exclude the local production of O
3 due to temperature and RH, strictly related to solar radiation, we ran the model including only the chemical compounds and the wind speed and direction (Scenario 2). To exclude the local production of O
3 due to the local NO and NO
2 emissions, we ran an FFNN including only these measurements (Scenario 3). Finally, to prove our hypothesis that the O
3 at the RS was the result of the transport of NO and NO
2 from the metropolitan area, we ran the FFNN model only considering the wind speed and direction as the inputs (Scenario 4). All of the statistical parameters for determining the goodness of the model results between Scenario 2 and Scenario 4 were similar, demonstrating that the O
3 at the RS can be explained mainly by its precursors transport from the US.
As expected, the best simulation is the one corresponding to the first scenario (
Figure 7), in which NO
x, NO, NO
2, WS, WD, T, and HR were used as the inputs for the FFNN model (R = 0.90).
Furthermore, to understand the causes behind the higher O
3 measured at the rural site, it is interesting to observe that, when only the NO, NO
2, and NO
x (Scenario 3) measured at Rthe S are used as the inputs, the FFNN model is not able to reproduce the O
3 both in term of absolute value and diurnal trend (
Figure 8), with a correlation coefficient of R = 0.17.
On the other hand, if these inputs also included the wind speed and direction (Scenario 2), the correlation coefficient significantly increased, reaching 0.81 (
Figure 9).
This result strongly suggests that wind speed and direction are the most relevant inputs to model the observed O
3. It should be observed that in this scenario, despite the temperature, a well-known proxy to model O
3, was not included as an input parameter, the correlation coefficient between the model and measured O
3 at the RS station was very high and comparable to the one obtained for the first scenario. This suggests that transport is a possible explanation for the higher concentration of O
3 inland compared with the one measured in the urban area. Finally, the FFNN was run using only the wind speed and direction as the input (Scenario 4,
Figure 10); the correlation coefficient between the measured and modelled O
3 was about 0.81, close to the one obtained when including NO, NO
2, and NO
x (Scenario 3).
This confirms that the high O3 level measured at the rural downwind site was not related to the local production of O3 precursor species (NO and NO2) and their local photochemistry, but it was mainly due to the photochemical processes that took place in the air masses emitted at the urban site during the downwind transport. It is interesting to observe that the model underestimated the measurements especially between 12:00 and 17:00 p.m., i.e., at the maximum O3 concentration. This could be related to some missing chemical compounds relevant to O3 formation, such as volatile organic compound (VOC) oxidation products or organo nitrates (i.e., peroxy acetyl nitrate (PAN)) that could be dissociated back into NO2, which were not included in the model as they had not been measured.