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Review

Long-Term Variations of Air Quality Influenced by Surface Ozone in a Coastal Site in India: Association with Synoptic Meteorological Conditions with Model Simulations

by
Resmi C T
1,
Nishanth T
2,
Satheesh Kumar M K
3,
Balachandramohan M
1 and
Valsaraj K T
4,*
1
Department of Physics, Erode Arts and Science College, Erode, Tamil Nadu-638009, India
2
Department of Physics, Sree Krishna College Guruvayur, Kerala 680102, India
3
Department of Atomic and Molecular Physics, MAHE, Manipal, Karnataka 576104, India
4
Cain Department of Chemical Engineering, Louisiana State University, Baton Rouge, LA 70803, USA
*
Author to whom correspondence should be addressed.
Atmosphere 2020, 11(2), 193; https://0-doi-org.brum.beds.ac.uk/10.3390/atmos11020193
Submission received: 25 December 2019 / Revised: 7 February 2020 / Accepted: 7 February 2020 / Published: 12 February 2020
(This article belongs to the Special Issue 10th Anniversary of Atmosphere: Air Quality)
Browse Figures
Versions Notes

Abstract

:
Atmospheric ozone (O3) in the surface level plays a central role in determining air quality and atmospheric oxidizing capacity. In this paper, we review our comprehensive results of simultaneous measurements of surface ozone (O3) and its precursor gas (NOx) and weather parameters that were carried out continuously for a span of six years (January 2013–December 2018) at a typical rural coastal site, Kannur (11.9° N, 75.4° E) in South India. Surface O3 concentration reached its maximum during daytime hours and minimum during the night time. The influence of solar radiation and water content on variations of O3 are discussed. A Multi-Layer Perceptron (MLP) artificial neural network technique has been used to understand the effect of atmospheric temperature on the increase in O3 over the past six years. This has been found that temperature has been a major contributor to the increase in O3 levels over the years. The National Centre for Atmospheric Research- Master Mechanism (NCAR-MM) Photochemical box model study was conducted to validate the variations of O3 in different seasons and years, and the results were shown to be in good agreement with observed trends.

1. Introduction

Pollution refers to the changes in atmospheric air quality caused by natural or human intervention [1]. A report by the World Health Organization (WHO) shows that air pollution is on the rise in developing countries [2]. According to the study report by the India State-Level Disease Burden Initiative Air Pollution Collaborators [3], air pollution is the fifth most common cause of death in India, and every year, over 26 million people in India receive treatment for air pollution-related diseases. Pollutants due to trace gases, particulate matter, and volatile organic compounds harm human health as well as the ecosystems [4,5].
Being an important pollutant in the lower atmosphere, inhaling ozone (O3) leads to serious health problems, including reduced lung function and respiratory problems [6,7]. The two main causes of O3 showing up on the Earth’s surface are the photochemical reactions of precursor gases (Carbon Monoxide, Methane, Non-Methane Hydro Carbons and Volatile Organic Compounds) in the presence of Nitrogen Oxide (NOx) and the inflow from the stratosphere. The amount of precursor gases emitted by human activities from rural and urban areas have a significant effect on O3 variations [8,9,10]. Moreover, O3 also acts as an active greenhouse gas by which it can modulate climate change through a positive radiative forcing [11]. O3 in the troposphere has increased approximately two-fold compared to pre-industrial years [12,13]. In addition to higher emissions of its precursors due to anthropogenic activities, atmospheric dynamics has a central role in controlling the concentrations of O3 in the troposphere [14,15,16,17]. Likewise, water vapor in the lower atmosphere, surface air temperature, and climatological factors also play a critical role in the spatial and temporal variations of surface O3 [18,19,20].
NOx (= NO + NO2) is a prominent species found in the atmosphere, and it plays an important role in atmospheric O3 chemistry. In the stratosphere, it acts as a catalyst for O3 depletion and on the terrestrial level as important precursor of O3 formation [21,22,23,24]. In a rich NO2 environment, O3 is produced photochemically due to the acceleration of peroxy radicals formed by the oxidation of trace compounds, whereas in a high NO environment, the concentrations O3 is destroyed [25,26,27,28]. Changes in local weather conditions can greatly affect the surface O3 concentrations [29,30,31,32]. Therefore, analyzing the long-term variations in weather change that occur at a particular location is very useful for understanding the concentration of surface O3 concentrations [33,34,35,36,37,38]. Many studies [39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55] on the variations of O3 and its precursor gases revealed that higher surface O3 concentrations were observed in summer/autumn seasons and lower concentrations were observed during monsoon season.
Photochemical models are essential for assessing and studying the dynamics of an atmospheric trace gases and dust particles at a particular location [56,57,58]. Many groups [59,60,61,62,63,64] have used the NCAR-MM model to study in detail the photochemical reaction of trace gas in the terrestrial atmosphere for better understanding, and the model simulations were consistent with their observations.
Kannur, a heritage land in Kerala state that is reminiscent of the waves is growing rapidly. Unlike yesterday, each footprint takes villages and towns to new times. A part of it is a long stretch of beach and coconut plantations with greenery on more than half of the land; on the other side is the Western Ghats, which has grown into a magnificent place. There are many who set sail for the coast in the cool breeze of the Arabian Sea. The sky of the Arabian Sea, with its everlasting sunsets, attracts a large number of people to this gaze. The place is very popular during the holiday season, as it is easily accessible from the city. Even the smallest pollutants in the atmosphere of the Kannur can greatly affect the air quality of the place. Over the west coast of India, only few surface measurements of O3 are available. In view of this, an Atmospheric Chemistry-Transport and Modelling (AT-CTM) project was started under the Indian Space Research Organisation’s Geosphere Biosphere Program (ISRO-GBP) for the continuous observations of surface O3 and its precursor gases from 2010 in Kannur, a coastal site in India.
Here, we describe a detailed review report on the changes observed in surface O3, NOx concentrations over the past six years at Kannur. We first investigated the influence of solar radiation and water content on the distribution of seasonal and diurnal variation of O3 and NOx. The relationship observed between weather parameters and O3 over the past six years is also described here. Further, Multi-Layer Perceptron (MLP) artificial neural network method is employed to investigate the effect of air temperature on the O3 concentration over the observational site. In addition to these, the measured variation of O3 is analyzed by using the NCAR-Master Mechanism Photochemical box model.

2. Experimental Method

2.1. Observational Site and Measurement Techniques

The observational site at Kannur is shown in Figure 1. Measurements of the concentrations of O3 and NOx originally started in December 2009 at Kannur University Campus (KUC); however, complete sets of data was obtained during the years January 2013 through December 2018 and are presented in this review. The measurement site is rural with limited industrial activities and is an ideal place to look at a growing semi-urban environment in India. The measurements of surface O3 (5 min interval) were made using a continuous O3 analyzer (Model O342M) with a detection limit of 0.4 ppbv. Its measurement principle is based on O3 detection by direct absorption in UV light. The O3 absorption spectrum is intense in the 250 and 270 nm wavelength range. Thus, it corresponds to the maximum range of O3 absorption at 255 nm. NO and NO2 were monitored with the aid of a nitrogen monoxide and nitrogen dioxide analyzer (Model AC32M) specific to low concentrations in the ambient air with a detection limit of 0.4 ppbv. Its measurement principle is based on the NO chemiluminescence in the presence of highly oxidizing O3 molecules. The NO in the ambient air is oxidized by O3 to form excited NO2 molecules. The concentrations of NO and NO2 were measured based on the spectrum of the radiation emitted by NO2 molecules at the excited level. More details about the observational site and the ground-based gas analyzers (Environment SA, France) used for this study were already reported by us earlier [59,65]. The weather parameters at the observational site was retrieved from the local automatic weather station established by the ISRO.

2.2. Artificial Neural Network

To study the impact of air temperature on the variation of surface O3 over the observational site, an artificial neural network (ANN) method was used, and it is designed using air temperature as the sole varying input parameter. ANN are considered capable of modeling complex nonlinear processes [66,67,68]. A simple Multi-Layer Perceptron (MLP) model that is extensively used for regression analysis and time series autoregressive analysis comes under the category of feed-forward neural network (FFNN), which was employed here for the model simulations. The structure of the network consist of three layers—namely the input, hidden, and output layer—and the two nodes of each layer are interconnected, and the number of nodes in each hidden layer depends on the input parameters. More details of the network model are described elsewhere [68,69].

3. Results and Discussions

3.1. Influence of Solar Radiation and Water Vapor on O3 and NOx

The daytime increase in O3 concentration at the observational site is due to the active photochemistry of O3 precursors, Boundary Layer Height (BLH) variations, regional emissions, and changes in weather patterns. This type of O3 variation at urban and rural sites is attributed to the daytime in situ photochemical production from its precursor gases [70]. The diurnal variations of O3 and solar radiation is shown in Figure 2. From the figure, it is clear that the surface O3 concentrations gradually increasing after sunrise, attaining an extreme concentration during noontime and declining during night time. The enhancement in the O3 concentration relative to the increase in solar radiation indicates an active photochemical reaction in the presence of solar radiation. In our observation, it is found that the concentration of surface O3 reaches its peak value in the noon time, 30 min after the maximum intensity of solar radiation.
The absolute water vapor content ρ V is evaluated from the measured values of relative humidity (RH) and temperature (T) using the empirical relation given by [63,71].
ρ S   =   A   exp   ( 18.9766 − 14.9595 A − 2.4388 A 2 ) ρ V =   ρ S   ( RH 100 ) [ 1 − ( RH 100 ) ( ρ S R V T P ) ] − 1 where   A = T 0 ( T 0 + t )   ,   and   T = ( T 0 + t )
The average diurnal profile of O3, NOx, and water vapor content over the study area is shown in Figure 3a. Generally, NOx (NO + NO2) concentration was observed to be low during daytime and high during late night hours, and early in the morning hours. The main reason for the increase of NOx observed at night and early morning were due to the very low BLH and motor vehicular emissions [72,73].
In the presence of shallow BLH, the elevated levels of water vapor during monsoon and post-monsoon season acts as a detergent for the atmospheric chemistry [74,75,76]. The maximum (15.2 ± 1.9 gm–3) water vapor content is observed in the morning at 09:00, and the minimum (12.1 ± 2.1 gm–3) is observed in the noontime at 13:00. During evening hours, water vapor gradually increases until late at night due to the decline in temperature. From the figure, it is clear that water vapor and NOx have the same diurnal patterns, while surface O3 has just the opposite pattern. The monthly variation of surface O3, NOx, and water content is shown in Figure 3b. The 24-h averaged monthly mean maximum (15.35 ± 2.3 gm–3) water vapor is observed in July due to monsoon and the minimum (12.2 ± 1.8 gm–3) is in January. Similarly, the daytime averaged monthly mean lowest O3 is observed in July and the maximum is in December. From the figure, it is clear that the increase in ρ V declines the O3 in this site.
The presence of water vapor in the atmosphere gives a radiative cooling and reduced photochemical process. Water vapor content in the form HOx (=OH + HO2) radicals reacts with other chemical species in the atmosphere and can have a detrimental effect on O3 in the atmosphere [77,78,79].

3.2. Impact of Meteorological Parameters on O3 and NO2

Variations in meteorological parameters influence the efficiency of photochemical production and loss of surface O3 [38,80]. Solar radiation and enhanced air temperatures play catalytic roles in chemical reactions, leading to surface O3 formation [81]. In our study, in situ O3 data shows that the O3 mixing ratio starts to increase after 9:00 IST and reaches a maximum value at about 13:30–14:30 IST and then decreases slowly. The concentrations reached its low value during the night hours. The photochemical production of surface O3 over a specific location is influenced by the concentration of its precursor gases. Consequently, the correlation among O3, NO2, and meteorological parameters was calculated for the daytime and is presented in Table 1.
It is evident that O3 variation is positively related to air temperature and is negatively related to RH. Statistical analysis of correlation coefficients showed that the most favorable parameters for the formation of O3 over the observational site are ambient air temperature, solar radiation, and the enhanced concentration of NO2.

3.3. Impact of Air Temperature on O3 Production: A Neural Network Analysis

The increase in atmospheric temperature has a significant impact on O3 production as it accelerates the chemical reactions in the atmosphere. Numerous studies have evaluated the temperature sensitivity of surface O3 production over different parts of the globe [33,82,83,84,85,86,87,88,89,90,91]. Solberg [48] reported a substantial increase in surface O3 with high temperatures during the observations of O3 and trace gases in the European Monitoring and Evaluation Programme (EMEP) network. Modeling studies by different groups [83,84,90,92] found that high atmospheric temperatures increases the surface O3 concentration keeping constant VOCs and NOx concentrations. Pusede [86], found that in a polluted environment with rich NOx concentrations, O3 and atmospheric temperature show a positive relationship and the temperature accelerates the O3 productions. As a consequence of global warming, this positive O3–temperature relationship causes a significant decrease in the quality of the air without anthropogenic emissions in industrial areas and is referred to as a climate penalty [93].
In the FFNN, the input parameters given were five-minute interval surface meteorological and observed trace gases data obtained from the ground-based measurements. We do not have the facility to carry out the measurement of other trace species; hence, their values were set to represent a typical rural environment. One-quarter (25%) of the total available datasets in the winter and summer months were selected for model analysis. The environmental parameters used for artificial neural network simulations are given in Table 2.
To detect the effect of atmospheric temperature on the production of surface O3, the air temperature was gradually raised from 16 to 44 °C, keeping the values of solar radiation and relative humidity at 850 Wm−2 and 60% respectively and other input parameters at the respective average values. These values were selected to represent a distinctive level of meteorological parameters during the days of winter months, which represent a higher concentration of O3 in the atmosphere of Kannur. Similarly, in order to detect the effect of solar radiation on the production of surface O3, the intensity of solar radiation was varied from 0 to 900 Wm−2, keeping all other parameters constant at their average value. A similar method was adopted for determining the effect of RH on surface O3. Figure 4 shows how the changes in O3 depend on the variation of temperature, solar radiation, and RH when the other parameters are fixed. From the figure, it is clear that the increase in atmospheric temperature and solar radiation enhances the concentration of surface O3. However, the values of temperature and solar radiation lines spread out at a high concentration of O3, which in turn makes solar radiation levels more nonlinear. Therefore, even if the intensity of solar radiation is increased again, the concentration of O3 cannot be increased. Thus, it must be inferred that beyond a threshold of solar radiation, the photolysis of NO2 may not be the limiting factor for in situ surface O3 creation for a particular environment. This revealed that strong solar radiation and a high atmospheric temperature are the most imperative factors controlling the enhanced photochemical O3 formation over Kannur.
Correlations between the observed temperature with the observed and ANN modeled ozone, observed solar radiation with observed and ANN modeled ozone, and observed relative humidity with observed and ANN modeled ozone has been made and are shown in Figure 5. It is found that the observed and model outputs of the ozone are statistically significant with the meteorological parameters. Statistical analysis (Pearson’s test) is performed, and the correlation coefficient is found to be significant at the 95% confidence level. The correlation coefficients obtained for the observed temperature, solar radiation, and relative humidity with the observed ozone are 0.92, 0.90, and 0.86, and for the modeled ozone, they are 0.94, 0.91, and 0.89, respectively.
Figure 6 shows the relationship between the observed daytime maximum O3 concentrations and the corresponding model predicted O3 concentrations. The lower observed concentrations yielded lower simulated results and higher simulated concentrations at observed higher values. The model-simulated output O3 concentrations with the observed concentration shows a positive linear correlation with the correlation coefficient (r = 0.89). It can be observed that given the values of other available parameters, the accuracy of the modeled output can be greatly improved. Since O3 formation is a nonlinear process involving complex reactions, it is clear that the use of an ANN model offers greater consistency and a high degree of accuracy on O3 modeling using different forecasters.
Figure 7a indicates the assessment of the annual average 24 h variations of O3 observed from January–December 2013 and from January–December 2018. The average diurnal variation of O3 on a yearly basis is computed for every hour from January to December. The average diurnal variation of O3 in 2013 shows the extreme mixing ratio (31.97 ± 8.52) ppbv at 15:00, and in 2018, it shows a maximum (35.47 ± 10.5) ppbv at 15:00. We also observed an increasing trend of O3 during noontime in 2018 as compared to 2013, and the increase is 10.94%. The corresponding minimum of O3 mixing ratio (6.22 ± 0.84) and (7.46 ± 0.9.7) ppbv was observed at 07:00 in the morning of respective years. The favorable meteorological conditions for the formation of O3 are intense solar radiation, low wind speed, shallow boundary layer height, and a high surface air temperature. The mean air temperature at any location depends on various factors, among which latitude, altitude, proximity to the sea, temperature of the sea, and exposure are the major ones [94].
Figure 7b shows the comparison of annual average diurnal variations of surface air temperature recorded from January to December 2013 and from January to December 2018. During 2013, the maximum (35.8 ± 2.9) daytime temperature was recorded at 14:00, and in 2018, the maximum (37.4 ± 3.2) temperature was recorded at 14:00. The daytime temperature was found to be increased from 35.8 ± 2.9 °C to 37.4 ± 3.2 °C (from 2013 to 2018), and this temperature increase (4.46%) was accompanied by the increase in the surface O3 concentration from 31.97 ± 8.52 to 35.47 ± 10.5 ppbv. Thus, the enhancement in O3 (10.94%) is well-matched with an enhancement in surface air temperature (4.46%). The apparent effect of O3 on atmospheric temperature was also evident in the intermediate years. Thus, a fairly small variation in temperature can significantly enhance ozone production. Surface O3 concentration reaches its peak value when the air temperature is a maximum, which indicates that O3 concentrations are directly related to temperature. The atmospheric air temperature changes with seasons of the year and time of the day. Over all the days, it was observed that the maximum temperature was reached at 14:00 IST. This indicates that the air temperature is significantly influencing the production of O3, and photochemistry is the dominant mechanism controlling the concentration level of O3 at Kannur.

3.4. Long-Term Observed Variations of O3, NO, and NO2

The following Table 3 is a description of the hourly average concentration of trace gas along a day, averaged over the period 2013–2018. Depending on the amount of sunlight, the ground level O3 increases whereas the concentrations of NOx reduced. O3 levels can be seen enhancing in the early morning hours of the year except for the rainy days of the monsoon months. Usually, O3 levels are recorded high in the late afternoon. The chemical reaction with NO is the main reason for the decrease in O3 observed at night. From our ground-based observations, it is found that the maximum number of ozone concentrations were observed in the range of 5–10 ppbv, mainly belonging to the night hours. The second-largest distribution of ozone concentration was observed between 10 and 20 ppbv. A substantial upsurge was noticed between 25 and 40 ppbv and 40–60 ppbv due to the enhanced O3 mixing ratio during daytime in clear sky days. Thus, it is revealed that 65% of the total O3 concentration lies between 5 and 20 ppbv and 30% lies between 20 and 45 ppbv, whereas merely 5% fluctuates from 46 to 60 ppbv.
Statistical analysis of the concentration of observed trace gases (O3, NO, NO2, NOx) is listed in Table 4. During January–December 2013, the average concentrations of O3 was 31.97 ± 8.52 ppbv and NO, NO2, and NOx were 2.19 ± 0.45, 2.32 ± 0.65, and 4.51 ± 0.78 ppbv, respectively. Similarly, during January–December 2018, the average concentrations of O3 was 35.47 ± 10.5 ppbv and for NO, NO2, and NOx they were 2.46 ± 0.92, 2.68 ± 1.07, and 5.14 ± 1.08 ppbv respectively. The annual average O3 concentration changes from 31.97 ± 8.52 to 35.47 ± 10.5 ppbv and NOx varied from 4.51 ± 0.78 to 5.14 ± 1.08 during 2013 to 2018. This variation showing an increasing trend of O3 (10.94%) and NOx (13.96%) over the observational site during the period 2013 to 2018.
These apparent variations are caused by the changes in atmospheric boundary layer above the Earth’s surface in the presence of sunlight, chemical reactions of trace gases, and changes in the regional climate. In addition to observational studies, recent model simulations [95,96] revealed the substantial increase of surface O3 concentrations over the East and South Asian regions. Future trends in troposphere O3 are considered with greater attention in modeling studies.

3.5. NCAR-MM Model Simulation

The NCAR Master Mechanism is a chemical box model developed at National Centre for Atmospheric Research, Boulder, CO, USA, which was used to simulate the diurnal variation of O3, primed with ground-based observations. This model computes the time-dependent chemical evolution of an air parcel, taking into account the detailed gas phase chemistry, which consists of 5000 reactions among the 2000 species. The model was initiated with 12 trace species (O3, H2O, CO, CH4, NO, NO2, OH, HO2, CH2O, C3H6, isoprene, and i-butane), while N2, O2, and photon energy are hard-wired in the model. Based on these species, 1031 reactions are used in the simulation. This model has been used to understand the diurnal variation of surface O3 for different seasons with the observed diurnal patterns. Many groups [61,62,63,64,97,98] used the NCAR-MMP model to realize the photochemistry of trace gas in the lower atmosphere over different regions around the globe, and they found that the simulated results are consistent with the observations. The details of the NCAR-MMP model are described by Aumont [99] and Madronich [100]. The initial and background (B.G) values of trace gases and environmental parameters used for box model simulation for different seasons are shown in Table 5. We do not have the facilities for the measurement of CH2O, C2H6, and isoprene and other trace species at the observational site; thus, the data of a typical rural environment available from the other locations were given to the modeling.
The model-simulated variation of O3 along with the observed diurnal variation for winter and summer seasons (averaged over the period 2013–2018) are shown in Figure 8a,b. In the winter season, the north easterly winds are quite predominant, which brings more pollution to this site, experiencing less humidity. The existence of a shallow boundary layer in winter may apparently increase the ozone concentration. This environment is more influenced by the land mass during winter, which elevates the ozone mixing at this site. In the monsoon season due to low temperature, solar radiation, and high humidity, O3 production is minimum. The seasonal mean values of O3 for the study period showed the daytime maximum in winter followed by summer and a minimum during the monsoon season. The clear sky days in the winter season significantly contribute an active photochemistry in the presence of a shallow boundary layer. From these figures, it is clear that the box model-simulated O3 variation is in tune with the diurnal profile of O3. Likewise, regarding the observed variations, the modeled O3 also starts increasing after the sunrise, attains its maximum at noontime, and gradually decreases over the evening hours. Figure 8c,d show the scatter plot of observed and modeled O3 in the winter and summer seasons respectively for their correlation. The correlation between the modeled and the measured O3 is well-matched for winter (r = 0.97) and summer (r = 0.91) seasons.
The model-simulated variation of O3 along with the observed diurnal variation for the monsoon and post-monsoon seasons (averaged over the period 2013–2018) are shown in Figure 9a,b, respectively. It is clear from these figures that the box model-simulated O3 variation is well-matched with the diurnal profile of O3 for both the seasons. Figure 9c,d shows the scatter plot of observed and modeled O3 for the winter and summer seasons, respectively. The linear correlation coefficient between the model simulation and the observed O3 is well-matched (r = 0.90) for the monsoon and (r = 0.88) post-monsoon seasons.
The annual average diurnal variation of O3 and its model-simulated variation is shown in Figure 10a. The model-simulated O3 variation is well-matched with the diurnal profile of observed O3. The correlation coefficient existing between the modeled simulation and the observed profile is r = 0.82, and it is shown in the Figure 10b. The results from the model simulation confirm that winter has a maximum concentration followed by summer and the post monsoon; and there is less concentration in the monsoon season. The model result shows that the O3 produced and decayed in the atmosphere of Kannur is mainly due to the photochemical reactions involving precursors. It can be seen that the increase in surface air temperature, solar radiation, and the variations in BLH also significantly affect the O3 levels during the daytime. In the model simulation, we were not able to include the transport of ozone and its precursors. Thus, the model output gives the production of ozone and its destruction employing chemical reactions only. Possibly, the observed and modeled shift may be due to the transport effect or the influence of other parameters that are currently not observed over the location.

3.6. Comparison of O3 with Other Observational Sites

Continuous observations and studies of surface O3 in India began about 30 years ago and are currently underway in more than 20 various locations [14,24,60,62,76,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118]. The diurnal and seasonal variation of surface O3 over Kannur shows a classic profile of a rural area, which is strongly prejudiced by seasonal changes, similar to other places in India. For a comparison, Table 6 represents the observed higher and lower surface O3 during the daytime at Kannur and other locations in India. The important thing to understand from this table is that in the southwestern parts of India, the highest concentration of O3 are observed in the winter and summer seasons. The enhanced O3 concentrations during the winter seasons are mainly due to th higher local emissions of precursor gases, long-range transport of continental pollutants, and the presence of a shallower boundary layer height. Due to a reduced photochemical process, all the observational sites show a very low concentration of O3 in the monsoon season. Strong convective activities, intense rainfall, and the flow of marine air are the major causes of reduced photochemical activities in monsoon. The monthly average daytime O3 concentration observed at Kannur is lower than the O3 concentration observed at Bhubaneswar, Mohal Kullu, Ootty, Pantnagar, Dayalbag and Nainital, Jodhpur, NCR Delhi, Udaipur, and Trivandrum. Kannur shows a wintertime maximum O3 similar to that observed at Trivandrum, Delhi, Port Blair, and Ootty. At the other observational sites, such as Agra, Kanpur, Anantapur, and Dayalbag, maximum O3 concentrations are observed during the summer seasons. The enhanced concentration of O3 in the northern and northeastern parts of India are predominant during the pre-monsoon season. The intrusion of O3 from the free troposphere is the main reason for the increase in O3 concentrations observed at nighttime hours in the mountainous regions. From these analyses, we conclude that the variations of surface O3 at different locations in the Indian sub-continent mainly depend on the latitude/longitude variation, weather parameters, availability of solar radiation, variations in boundary layer height, concentrations of precursor gases, and anthropogenic activities.

4. Conclusions

This describes a review on the impact of ground-level ozone on air quality changes in the atmosphere of Kannur. This study is based on the observational data from January 2013–December 2018 by employing ground-based gas analyzers. The observations showed a well-marked diurnal cycle of O3 concentration with a minimum during the night-time hours and maximum at noon hours. It is understood that climatic factors can significantly influence the photochemical production and the loss of surface O3. The variation of O3 has been shown to significantly influence the solar radiation, atmospheric temperature, and the concentrations of NO2. The enhancement in the O3 concentration relative to the increase in solar radiation indicates an active photochemical reaction in the presence of solar radiation. On a diurnal and monthly basis, daytime O3 is negatively correlated with the absolute water vapor content in the atmosphere. An artificial neural network study has been conducted to understand how the increase in atmospheric temperature from 2013 to 2018 will impact ozone fluctuations. The neural network analysis revealed that the atmospheric temperature positively affects the productions of O3 over the observational site, and O3 values are consistently enhancing from 2013 to 2018. The enhancement (10.94%) in O3 from 2013 to 2018 is matched with an enhancement (4.46%) in the surface air temperature. Thus, a fairly small variation in temperature can significantly enhance ozone production. The model-simulated output O3 concentrations with the observed concentration shows a positive linear correlation with correlation coefficient (r = 0.89). The NCAR-MMP box model study was conducted to validate the variations of O3 in different seasons and years, and the simulations resulted in a validation of the information obtained in the observations. This study reveals that a significant amount of surface O3 is produced from its precursors, even in a location with very little industrial activities.

Author Contributions

Data curation, R.C.T.; Investigation, N.T. and S.K.M.K.; Project administration, S.K.M.K.; Resources, S.K.M.K. and B.M.; Supervision, S.K.M.K. and V.K.T. All authors have read and agreed to the published version of the manuscript.

Funding

This work was funded by Indian Space Research Organization–Geosphere Biosphere Programme (ISRO–GBP) through their Atmospheric Chemistry-Transport and Modelling Project (AT-CTM), for the long term study of surface O3 and its precursor gases over Kannur.

Acknowledgments

The authors are grateful to the Indian Space Research Organization, Bangalore for the financial support provided through Atmospheric Chemistry, Transport, and Modeling project of Geosphere Biosphere Programme. The authors wish to thank Shyam Lal, Physical Research Laboratory, Ahmadabad for his constant inspiration and support throughout the programme. The authors are also grateful to R.K Sunil Kumar, Director, School of Information Science and Technology, Kannur University for his support and suggestions to develop the MLP artificial neural network model. Resmi expresses her gratitude to R. Venkatachalam (Principal) and D. Manivannan (HOD of Physics) of Erode Arts and Science College, Tamil Nadu for providing the necessary facilities and constant encouragement. Valsaraj thanks the Charles and Hilda Roddey Distinguished Professorship at LSU which supported the preparation of this review.

Conflicts of Interest

The authors declare no conflict of interest.

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Atmosphere 11 00193 g001 550
Figure 1. Geographical location of observational site at Kannur.
Figure 1. Geographical location of observational site at Kannur.
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Figure 2. Diurnal variation of O3 and solar radiation.
Figure 2. Diurnal variation of O3 and solar radiation.
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Figure 3. (a) Diurnal, (b) monthly variation of O3, NOx, and water content for the period January 2013–December 2018.
Figure 3. (a) Diurnal, (b) monthly variation of O3, NOx, and water content for the period January 2013–December 2018.
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Figure 4. Neural network output for O3 concentration with respect to the variation of temperature, solar radiation, and relative humidity.
Figure 4. Neural network output for O3 concentration with respect to the variation of temperature, solar radiation, and relative humidity.
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Figure 5. Scatter plot showing the correlations among (a) observed temperature, (b) solar radiation, and (c) relative humidity with observed and modelled ozone.
Figure 5. Scatter plot showing the correlations among (a) observed temperature, (b) solar radiation, and (c) relative humidity with observed and modelled ozone.
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Figure 6. Correlation between predicted daytime O3 concentration using neural model and observed daytime maximum O3 concentration.
Figure 6. Correlation between predicted daytime O3 concentration using neural model and observed daytime maximum O3 concentration.
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Figure 7. Annual mean 24-h variations of (a) O3 and (b) surface air temperature for the years 2013 and 2018.
Figure 7. Annual mean 24-h variations of (a) O3 and (b) surface air temperature for the years 2013 and 2018.
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Figure 8. Diurnal variation of measured (averaged over the period 2013–2018) and modeled O3 for (a) winter, (b) summer, (c) scatter plot showing the correlation between model O3 and measured O3 for winter, (d) summer.
Figure 8. Diurnal variation of measured (averaged over the period 2013–2018) and modeled O3 for (a) winter, (b) summer, (c) scatter plot showing the correlation between model O3 and measured O3 for winter, (d) summer.
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Figure 9. Diurnal variation of measured (averaged over the period 2013–2018) and modeled O3 for (a) monsoon, (b) post monsoon, and (c) scatter plot showing the correlation between model O3 and measured O3 for monsoon, (d) post monsoon.
Figure 9. Diurnal variation of measured (averaged over the period 2013–2018) and modeled O3 for (a) monsoon, (b) post monsoon, and (c) scatter plot showing the correlation between model O3 and measured O3 for monsoon, (d) post monsoon.
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Figure 10. Yearly average (over the period 2013–2018) (a) diurnal profile of measured and modeled O3, (b) correlation between modeled O3 and measured O3.
Figure 10. Yearly average (over the period 2013–2018) (a) diurnal profile of measured and modeled O3, (b) correlation between modeled O3 and measured O3.
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Table
Table 1. Correlation between O3, NO2, and weather parameters from January 2013 to December 2018 (correlation is significant at 0.01 level).
Table 1. Correlation between O3, NO2, and weather parameters from January 2013 to December 2018 (correlation is significant at 0.01 level).
ParametersO3NO2TemperatureSolar RadiationRHWind Velocity
O310.680.890.74−0.82−0.76
NO20.681−0.62−0.48−0.44−0.38
Temperature0.89−0.6210.64−0.48−0.42
Solar radiation0.74−0.480.641−0.54−0.38
Relative humidity−0.82−0.44-0.48−0.541−0.36
Wind speed−0.76−0.38−0.42−0.38−0.361
Table
Table 2. Input parameters used for artificial neural network analysis.
Table 2. Input parameters used for artificial neural network analysis.
ParametersMinimumMaximumAverage
Ozone (ppbv)105030
Total ozone column (DU)200280240
NO (ppbv)0.532
NO2 (ppbv)0.553
Surface air temperature (°C)164425
Relative humidity (%)409065
Solar radiation (Wm−2)0900400
Wind speed (ms−1)0102
Table
Table 3. The average hourly O3, NO, and NO2 at Kannur.
Table 3. The average hourly O3, NO, and NO2 at Kannur.
Time O3 (ppbv)NO (ppbv)NO2 (ppbv)
15.79 ± 1.051.67 ± 0.221.89 ± 0.28
25.66 ± 0.901.65 ± 0.301.91 ± 0.36
35.49 ± 0.881.64 ± 0.351.90 ± 0.42
45.29 ± 0.881.65 ± 0.371.90 ± 0.44
55.06 ± 0.821.61 ± 0.411.90 ± 0.47
64.77 ± 0.901.56 ± 0.381.88 ± 0.48
74.57 ± 0.801.56 ± 0.321.93 ± 0.50
85.50 ± 1.631.67 ± 0.382.06 ± 0.50
98.76 ± 3.151.81 ± 0.422.22 ± 0.46
1013.99 ± 5.741.85 ± 0.502.22 ± 0.39
1120.33 ± 8.741.71 ± 0.511.99 ± 0.42
1226.06 ± 10.431.55 ± 0.481.75 ± 0.43
1330.04 ± 10.721.36 ± 0.411.53 ± 0.39
1431.42 ± 10.481.13 ± 0.251.35 ± 0.39
1532.29 ± 11.091.05 ± 0.231.25 ± 0.43
1630.51 ± 11.801.04 ± 0.221.21 ± 0.44
1725.63 ± 11.111.12 ± 0.251.24 ± 0.40
1819.23 ± 7.551.24 ± 0.301.3 ± 0.34
1912.79 ± 3.881.35 ± 0.311.44 ± 0.28
209.95 ± 2.351.46 ± 0.301.57 ± 0.27
218.45 ± 1.361.55 ± 0.311.66 ± 0.28
227.50 ± 1.071.64 ± 0.331.76 ± 0.30
236.83 ± 1.041.75 ± 0.331.89 ± 0.30
246.30 ± 1.101.84 ± 0.342.17 ± 0.29
Table
Table 4. Statistical analysis of the observed trace gases during 2013–2018 at Kannur.
Table 4. Statistical analysis of the observed trace gases during 2013–2018 at Kannur.
DurationStatisticsGases
O3NONO2NOx
1 January 2013–
31 December 2013		Average31.972.192.324.51
Standard deviation8.520.450.650.78
Daytime maximum50.22.682.785.21
Daytime minimum11.60.980.852.22
Number of data37,44037,12037,12037,120
1 January 2014–
31 December 2014		Average32.22.322.454.77
Standard deviation9.60.650.740.89
Daytime maximum52.22.752.885.32
Daytime minimum10.81.021.122.32
Number of data38,16038,12038,12038,120
1 January 2015–
31 December 2015		Average33.752.272.484.75
Standard deviation10.60.690.780.88
Daytime maximum53.552.822.785.46
Daytime minimum12.21.121.082.38
Number of data38,88036,52036,52036,520
1 January 2016–
31 December 2016		Average34.882.352.524.87
Standard deviation11.10.720.880.94
Daytime maximum56.122.882.985.48
Daytime minimum12.41.081.122.42
Number of data41,76038,45038,45038,450
1 January 2017–
31 December 2017		Average35.122.412.544.95
Standard deviation12.20.880.980.98
Daytime maximum57.62.862.895.59
Daytime minimum12.02 1.211.182.46
Number of data40,88040,66040,66040,660
1 January 2018–
31 December 2018		Average35.472.462.685.14
Standard deviation10.50.921.071.08
Daytime maximum58.52.922.965.72
Daytime minimum12.29 1.241.222.41
Number of data41,32040,24040,24040,240
Table
Table 5. Initial and background (BG) values of trace gases and environmental parameters set for model simulation for different seasons.
Table 5. Initial and background (BG) values of trace gases and environmental parameters set for model simulation for different seasons.
ParametersSeason
WinterSummerMonsoonPost MonsoonAnnual
InitialB.GInitialB.GInitialB.GInitialB.GInitialB.G
O3 (ppbv)363230381428223425.533
CO (ppbv)320 325280120220300260220270241
CH4 (ppbv)3200 180022501700180016002100165023301687.5
CH2O (ppbv)0.40.50.50.490.60.480.70.450.550.48
C2H6 (ppbv)0.961.110.90.80.950.91.050.921.01
Isoprene (ppbv)11.10.81.20.8610.921.050.891.08
Temperature (K)298308300304302
RH (%)7266807473
O3 column (DU)340 360 260 280 310
AOD at 550 nm0.580.520.300.420.45
Aerosol single scattering albedo0.660.720.580.620.65
Aerosol Angstrom coefficient0.980.880.600.720.80
Table
Table 6. Observed maxima and minima surface O3 concentrations at Kannur and other locations in India for a comparison.
Table 6. Observed maxima and minima surface O3 concentrations at Kannur and other locations in India for a comparison.
LocationsCategoryPeriod of Observations Daytime Observed (ppbv)Reference
Maximum (Season)Minimum (Season)
KannurRural2013–201835.47 ± 10.5 Winter13.5 ± 5.6 (Monsoon)Present study
JodhpurSemi-Arid, Urban2012–201347 ± 11.5,
Pre monsoon		27 ± 12
(Monsoon)		[72]
TrivandrumCoastal Site2007–200940 ± 8.5, Winter18 ± 5
(Monsoon)		[111]
AgraUrban2012–201332.5 ± 19.3, Summer8.74 ± 3.8
(Monsoon)		[8]
DelhiUrban2012–201338 ± 7, Winter28 ± 6
(Monsoon)		[11]
NCR Delhi Urban2014–201545.3 ± 9.5, Winter23.8 ± 10.9
(Monsoon)		[25]
UdaipurSemi-Arid, Urban 2011–201246 ± 12.5, Pre monsoon26 ±4.6
(Monsoon)		[73]
Port BlairMarine Site2005–200730 ± 5, Winter10 ± 5
(Monsoon)		[119]
KanpurUrban2009–2013 27.9 ± 17.8, Summer 10.5 ± 5.6
(Monsoon)		[120]
AnantapurSemi-Arid, Rural2012–201364.9 ± 5.3, Summer19.9 ± 1.02
(Monsoon)		[61]
DibrugarhSub Himalayan2009–201342.9 ± 10.3, Pre monsoon 17.3 ± 7.0
(Monsoon)		[103]
BhubaneswarUrban2009–2011 61.7 ± 12.7, Winter 20.57 ± 5.8
(Monsoon)		[121]
Mohal, KulluSemi-Urban2010–201184 ± 23.9, Pre monsoon10 ± 6.5
(Monsoon)		[116]
OottyHigh-Altitude Mountain2010–201253.5 ± 8.2, Winter19.81 ± 2.4
(Monsoon)		[122]
PantnagarSemi-Urban2009–201148.7 ± 13.8, Spring10.8 ± 12.1
(Monsoon)		[62]
DayalbagSuburban2008–200960±10, Summer20 ± 6
(Monsoon)		[117]
NainitalHigh Altitude in Himalaya2006–200867.2 ± 14.2, Late spring24.9 ± 8.4
(Monsoon)		[14]

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C T, R.; T, N.; M K, S.K.; M, B.; K T, V. Long-Term Variations of Air Quality Influenced by Surface Ozone in a Coastal Site in India: Association with Synoptic Meteorological Conditions with Model Simulations. Atmosphere 2020, 11, 193. https://0-doi-org.brum.beds.ac.uk/10.3390/atmos11020193

AMA Style

C T R, T N, M K SK, M B, K T V. Long-Term Variations of Air Quality Influenced by Surface Ozone in a Coastal Site in India: Association with Synoptic Meteorological Conditions with Model Simulations. Atmosphere. 2020; 11(2):193. https://0-doi-org.brum.beds.ac.uk/10.3390/atmos11020193

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C T, Resmi, Nishanth T, Satheesh Kumar M K, Balachandramohan M, and Valsaraj K T. 2020. "Long-Term Variations of Air Quality Influenced by Surface Ozone in a Coastal Site in India: Association with Synoptic Meteorological Conditions with Model Simulations" Atmosphere 11, no. 2: 193. https://0-doi-org.brum.beds.ac.uk/10.3390/atmos11020193

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