Impact assessment of surface ozone exposure on crop yields at three tropical stations over India

Surface ozone is a damaging pollutant for crops and ecosystems, and the ozone-induced crop losses over India remain uncertain and a topic of debate due to a lack of sufficient observations and uncertainties involved in the modeled results. In this study, we have used the observational data from MAPAN (Modelling Air Pollution And Networking) for the first time to estimate the relative yield losses, crop production losses, and economic losses for the two major crops (wheat and rice). The detailed estimation has been done focusing on three individual suburban sites over India (Patiala, Tezpur, and Delhi) and compared with other related studies over the Indian region. We have used the concentration-based metric (M7, 7-h average from 09:00 to 15:59 h) along with the cumulative ozone exposure indices (AOT40, accumulated exposure over a threshold of 40 ppb) and applied the exposure–response (E-R) functions for the calculation of the crop losses. Our study shows that the yearly crop losses can reach the level of 12.4–40.8% and 2.0–11.1% for the wheat and rice crops, respectively, at certain places like Patiala in India. The annual economic loss can be as high as $4.6 million and $0.7 million for wheat and rice crops, respectively, even at individual locations in India. Our estimated %RYL (relative yield loss) lies in the range of 0.3 + /0.6 times the recent regional model estimates which use only the AOT40 metric. Region-specific E-R functions based on factors suitable for the Indian region needs to be developed.


Introduction
Surface ozone (O 3 ) is a damaging air pollutant due to its negative effects on human health (WHO, 2013;Fu & Tai, 2015;Cohen et al., 2017) and vegetation (Li et al., 2017;Mills et al., 2018). Surface ozone also adversely affects biodiversity (Agathokleous et al., 2020). It is produced in the troposphere through the photochemical reactions between the ozone precursors like nitrogen oxides (NO x = NO + NO 2 ), carbon monoxide (CO), non-methane volatile organic compounds (NMVOCs), and methane (CH 4 ) in the presence of sunlight (Crutzen et al., 1999). Ozone precursors are emitted from the transport sector, power plants, biomass burning, and other sources of combustion. Ozone enters the plants through the stomata present in the leaves during the regular gas exchange in the daytime (Sinha et al., 2015). This decreases photosynthesis and disrupts plant metabolism resulting in weaker, 1 3 Vol:. (1234567890) underdeveloped plants, poor crop quality, and reduced yields (Buker et al., 2015;Fuhrer, 2009;Wilkinson et al., 2012;Ainsworth et al., 2012;Leisner & Ainsworth, 2012). Ozone-induced crop production losses are a threat to food security and lead to economic losses (Avnery et al., 2011a, b;Lu et al., 2015;Van Dingenen et al., 2009). In the eastern and southern parts of Asia, where emissions of ozone precursors have increased due to economic expansion, this is a matter of concern (Adams et al., 1989;Aunan et al., 2000;Holland et al., 2002;Li et al., 1999;Wang & Mauzerall, 2004). India is a hugely populated country (second largest after China) with rapid development and where the economy is mainly dependent on agriculture. So ozoneinduced crop reductions can pose a serious threat to the economic growth and food security of India. Earlier studies have applied chemistry transport models for calculating regional or global ozone fields and made an assessment of crop exposure to ozone over different parts of the world (Avnery et al., 2011a, b;Deb Roy et al., 2009;Tong et al., 2009a, b;Van Dingenen et al., 2009). As per estimates made by Van Dingenen et al. (2009), the relative yield losses of present times fall in the range of 3-4% for rice, 7-12% for wheat, 3-5% for maize, and between 6 and 16% for soybean. Further estimations show that by the year 2030, the global economic cost loss would lie between $14 and $26 billion if the year 2000 global market prices are followed (Van Dingenen et al., 2009). Estimations done by Avnery et al. (2011a) show that for the year 2000, the %RYL falls between 3.9 to 15.4%, 2.2 to 5.5%, and 8.5 to 13.9% for the wheat, maize, and soybean crops, respectively. The projection made by Avnery et al. (2011b) indicates that by the year 2030, ozone-induced crop losses would lead to a global economic cost loss falling in the range of $12 to $35 billion (based on the emission scenario). Few studies have applied chemistry transport models over the Indian region, to calculate the ozone-induced crop yield losses with the help of modeled ozone mixing ratios (Ghude et al., 2014;Sharma et al., 2019). Some of the studies in India have made estimations for ozone-induced crop losses, by applying concentration-based metrics on the observed data for selected sites (Debaje, 2014;Sinha et al., 2015;Lal et al., 2017). Much more meteorological information and data involving plant conditions are required for the application of flux-based metrics which are developed in Europe (Klingberg et al., 2014). Currently, there is a lack of parameterization of flux-response functions throughout the globe (Hollaway et al., 2012) and they are available for only limited crops (Pleijel et al., 2004;Mills et al., 2011b). Although several thousands of observational sites are present throughout the world, only a limited number of them are located in India. In this study, we have used the observational data from MAPAN for the first time, to analyze and compare the effects of surface ozone threats on crop production, at three representative sites located in different regions of India using concentration-based metrics for the year 2018. They are the three suburban sites situated at Patiala in the state of Punjab in north-west India, at Tezpur located in the state of Assam in northeast India, and at Delhi situated in the state of Delhi in northern India. This study focuses on individual locations in detail for the calculation of ozone-induced crop losses over India, and the results based on this study give a clear indication of the uncertainty and underestimation involved in the ozone-induced crop losses and economic losses over India. In this study, we have initially calculated the cumulative ozone exposure indices (AOT40) and the concentration-based Mx indices of ozone (M7) with the help of codes and programs. Thereafter, the relative yield losses and the crop losses for the two major crops (wheat and rice) have been calculated using the E-R functions  along with the AOT40 and M7 metrics. Finally, we have estimated the economic losses based on the crop production losses and the minimum support prices for these two important crops. For missing data involving short data gaps, we have interpolated the values before and after the gap to fill in the missing data. For data gaps that are a bit longer, we have utilized the corresponding measurements from adjacent years for filling in the data gap.

Site description
The monitoring site at Thapar University, Patiala (30°21′9.77″N and 76°22′18.69″E) is located in the south-eastern parts of Punjab in north-west India over the Indo-Gangetic plains (IGP). The climate of Patiala district can be classified as tropical steppe, semi-arid, and hot which is mainly dry with very hot summer and cold winter except during monsoons. July and August are the wettest months, and the mean minimum and maximum temperature range from 7.1 to 40.4°C during January and May or June, respectively. The monitoring station at Tezpur University, Tezpur (26°42′3.37″N and 92°49′49.04″E) is situated in the mid-Brahmaputra valley in the state of Assam (Sonitpur district) in north-east India at an elevation of 48 m. The hottest month is August with an average temperature of 28°C, the coldest month is January when the mean temperature is 17.6°C, and the wettest month is July with 740 mm rainfall. The monitoring site in Amity University (28°19′6.24″N and 76°54′ 49.32″E) with an elevation of 212 m is located in the state of Delhi in northern India which is a landlocked region far away from the sea with close proximity to the Himalayas and the Thar desert. Due to this reason, it experiences an extreme, composite, and continental climate with the prevalence of continental air during major parts of the year, except during the monsoon months of July, August, and September. The hottest month is June, the coldest month is January, and the wettest month is July with an annual rainfall of 700 mm.

Analytical details
At the measurement sites of Patiala and Tezpur, ozone monitoring is done by the EC9810 ozone analyzer (A&B series) which is a non-dispersive ultraviolet photometer applying the method involving absorption of UV radiation at 250 nm by the ozone molecules with an hourly time resolution. Span measure allows the cell to be filled with span gas from an external span source. At the monitoring location in Delhi, the Photometric Ozone Analyzer (O 3 42 module) is used which applies the principle of ozone detection by absorption in UV light (253.7 nm) and the span check is done with the help of in-built ozone generators (Mallik et al., 2015). Further details regarding the data source and the monitoring sites can be obtained from Rana et al. (2019) and Rahman et al. (2021).
Ozone exposure metrics AOT40 is calculated as the sum of the differences between the hourly ozone concentrations exceeding 40 ppb and 40 ppb using only the hourly values measured for daylight hours between 07:00 a.m. and 07:00 p.m. .
For C i ≥ 40 ppb during daylight hours only, where "C i " is the average ozone mixing ratio (in ppb) and "i" corresponds to the hour index. The AOT40 values of 3000 ppb h accumulated over 3 months growing season and 10,000 ppb h over 6 months correspond to the critical levels (5% yield loss) for the protection of agricultural crops and forests, respectively.
M7 is the daytime 7-h average of the ozone mixing ratios from 9 to 16 h for the full duration of the crop. The following shows the definition and formula used for the M7 exposure index of ozone.
Here, "n" is the number of hours in the growing season, "C i " is the hourly ozone concentration in ppb, and "i" is the hour index.
Crop yield, crop loss, and economic loss The following semi-empirical equations have been used as crop response functions  for estimating the relative yields (RYs) with the help of codes and programs.
For wheat: (Lesser et al., 1990)  For rice: (Adams et al., 1989)  The RYL is defined as the crop yield reduction from the theoretical yield that would have resulted without ozone-induced damages (Avnery et al., 2011a).
The following equations have been considered for calculating the crop production losses (CPL i ) from the [Ci] for i = 1 to n; for 09 ∶ 00 − 15 ∶ 59 hrs.
The economic cost loss or economic loss (EL) is defined as the financial loss due to O 3 -induced damages in a financial year for any crop. The minimum EL is calculated using the CPL and the minimum support prices (MSP) for the same fiscal year through the following equation. The minimum support prices are the producer prices for that year for that particular crop. Here, "i" stands for the year of calculation.

Theory/calculation
Different kinds of ozone exposure metrics have been used for assessing the damaging effects of ozone on vegetation. Some of them are based on concentration (M7 and M12) and others are based on cumulative ozone exposure (AOT40, SUM06, and W126). Studies involving flux-based metrics (Klingberg et al., 2014;Sicard et al., 2017) require additional information in terms of meteorological parameters and information related to plant conditions (Lal et al., 2017). These were not available for the present study, and flux-response relationships are yet to be parameterized globally (Hollaway et al., 2012). Here, we have used one of the concentration-based metrics (M7) and a cumulative exposure-based metric (AOT40). Of the above two parameters, M7 gives equal weightage to all ozone concentrations while AOT40 gives more weightage to higher ozone mixing ratios (Tuovinen, 2000). In addition, we have derived the relationship between the 24-h average monthly ozone and the monthly average M7 values along with the relationship between the 24-h average monthly ozone and the monthly AOT40 values. These relationships have been derived based on the observed data at these three stations. The RY, CPL, and EL have been calculated for the wheat and rice crops using the AOT40 and M7 metrics. For this purpose, the average M7 and integrated AOT40 values have been considered for the wheat and rice crops for their respective crop periods, i.e., January to March and August to October. We have calculated the losses for the Kharif rice since almost 85-90% of total rice crops produced in India annually are contributed by the Kharif season rice crop (Lal et al., 2017). The E-R functions used here have been developed based on data collected from crop cultivars grown in North America and Europe. Crops grown in South Asia have more sensitivity toward ozone as compared to those grown in Europe and North America Emberson et al., 2009). So these exposure-response functions may not be able to appropriately represent the ozone sensitivity of the crops grown in this region. Sinha et al. (2015) have derived India-specific E-R functions for wheat and rice cultivars based on the yield of crops spread over different periods, but actual field experiments with different levels of ozone in the local environment or in the ambient air are needed. Devising plant response relationships based on the flux of ozone into the plant through the stomata gives a more accurate approach (Klingberg et al., 2014;Sicard et al., 2017). Presently flux-response relationships have not been parameterized globally (Hollaway et al., 2012) and are available for only a few crops (Pleijel et al., 2004;Mills et al., 2011b). So we have used the internationally agreed-upon comprehensive set of E-R functions available for a limited number of crops, to calculate the crop yield losses using the concentrationbased metrics and cumulative ozone exposure indices at three suburban sites over India (Patiala, Tezpur, and Delhi). The annual crop productions for wheat and rice have been considered in kilogram/Hectare (Kg/H) from the district-wise crop production report (Punjab Agricultural University, Ludhiana; https:// des. assam. gov. in/ sites/ defau lt/ files/; Development Department, Govt. of NCT of Delhi, Economic Survey of Delhi, 2019-20).

Results and discussion
Monthly ozone exposure indices In Fig. 1, we find that at Patiala the monthly AOT40 values are higher in the summer months of March-May with the highest value of 13,257 ppb h in May 2018. At Tezpur, the monthly AOT40 values show the maximum level of 9653 ppb h in August which is the warmest month of the year. The monthly AOT40 values reach the highest level of 8042 ppb h in March which marks the start of the summer season in Delhi. The monthly AOT40 value reaches its lowest level of 96 ppb h in the early winter month of November 2018 at Patiala when the temperature starts decreasing. Throughout the winter period, due to very low temperature, there is lesser formation and destruction of ozone and there is a gradual build-up of ozone. At Tezpur, the ozone mixing ratios are generally below 40 ppb in the winter months starting from October and the monthly AOT40 values reached zero levels in the months of October and December. At Delhi, the ozone concentrations do not cross 40 ppb and show zero levels in the hot summer months of May and June and in the monsoon month of July. In the winter months of November-January, the ozone levels are mostly below 40 ppb at Patiala probably due to very low temperatures and reduction in solar radiation during that period at the monitoring site. Temperature influences ozone formation by enhancing the rates of photochemical reactions and increasing the emissions of VOCs (volatile organic compounds) such as isoprene from vegetation (Coates et al., 2016). High temperatures which are associated with weak winds and atmospheric stagnation accelerate the production of ozone. At Tezpur, although the hot season prevails during the period from February end until June end, the ozone levels and the monthly AOT40 values are comparatively on the lower side. This is probably because during this period, the wind speeds are higher as compared to that in the rest of the year and this period comprises the windier part of the year. The long, hot summer season starts in March at Delhi when the wind direction also changes from north-westerly to south-westerly and it lasts until June. In the cold winter extending from December to February, there is lesser production and loss of ozone and there is a gradual accumulation of ozone over the winter period. It is very hot in summer and very cold in winter with heat waves and cold waves prevailing during the summer and winter seasons, respectively. Also, the months from February to July comprise the windier part of the year and the calmer time falls in the months from August to January. So at Delhi, the monthly AOT40 value reaches the maximum level in March when the summer starts and the temperature starts increasing. The monthly AOT40 values touch zero levels in May and June when the temperatures are very high and heat wave conditions prevail in the region along with the south-westerly winds. The monthly AOT40 values also reach zero level in the monsoon month of July. Throughout the monsoon period from July to September, the monthly AOT40 values are comparatively lower and increase again at the end of the monsoon period at the sites of Patiala and Delhi. During the monsoon months, cloudiness and wet scavenging of ozone precursors lower the photochemical production of ozone. At Tezpur, the rainy season falls in the summer months and August is the warmest month of the year when the monthly AOT40 values are maximum. So at Tezpur, although there is wet scavenging of ozone precursors during the monsoon period, the photochemical production of ozone does not decrease due to the presence of sufficient solar insolation and high temperatures. This feature distinguishes the site Tezpur from what is observed at some of the other sites in India where there is lower photochemical production of ozone during the monsoon period due to insufficient solar radiation and associated monsoon washout Sinha et al., 2015;Lal et al., 2017).
In Fig. 2, we find that the seasonal variation of the monthly average M7 values at Patiala, Tezpur, and Delhi follow a similar pattern as that observed for the monthly variation of AOT40 values for the corresponding sta-  (Coates et al., 2016). At Tezpur, although the period from February end to the end of June falls under the hot season, the M7 values are relatively on the lower side during this period. This can be due to the fact that this period falls in the windier part of the year with higher wind speeds as compared to the rest of the year. At Patiala, the monthly average M7 values touch the highest level in May when the temperature reaches the maximum values and the minimum levels of M7 are found to be in November when the winter season starts. In the winter months of November-January when the temperature is very low at Patiala, the ozone levels are comparatively on the lower side. As mentioned earlier, this can be due to the lack of optimum temperature and meteorological conditions necessary for the photochemical production of ozone (Coates et al., 2016) during this period. At Delhi, the long hot summer season starts from March when the wind direction changes from north-westerly to south-westerly and it extends until June. During the cold season from December to January, due to very low temperatures, there is very less production and destruction of ozone and there is a gradual cumulative build-up of ozone throughout the winter months. It is very hot in summer and very cold in winter with heat waves and cold waves prevailing during the summer and winter seasons, respectively. The windier part of the year falls in the months from February to July, and the calmer time of the year prevails from August to January. So the highest M7 values are observed in March when the temperature starts increasing with the start of summer. The minimum levels of M7 are found in May when the temperatures are very high and heat wave conditions prevail in the region along with south-westerly winds. At Patiala and Tezpur, the minimum value of the monthly average M7 is found to be at the start of the cold season when the temperature starts decreasing. As the winter period continues, due to low temperatures, there is lesser production and destruction of ozone, resulting in a gradual accumulation of ozone during the period of time. The monthly average M7 ozone values are comparatively lower during the monsoon months of July-September and increase again at the end of the monsoon at Patiala and Delhi. Due to cloudiness and wet scavenging of ozone precursors, there is lower photochemical production of ozone during the monsoon period. At Tezpur, the rainy period coincides with the summer months with August being the warmest month of the year when the monthly average M7 values shows the maximum levels. So at Tezpur, during the monsoon period when there is wet scavenging of ozone precursors, the photochemical production of ozone does not decrease due to the presence of sufficient solar radiation and high temperature. As mentioned earlier, this can be considered to be a distinctive feature of the site Tezpur as compared to what is observed at some of the other sites located over India Sinha et al., 2015;Lal et al., 2017). Figure 3 shows the relationship between the 24-h average monthly ozone and the monthly average M7 values derived from the observational data at Patiala, Tezpur, and Delhi. At all three sites, we find that the 24-h average ozone has a linear relation (R 2 = 0.96 for Patiala; R 2 = 0.9 for Tezpur; R 2 = 0.9 for Delhi) with the monthly average M7 values. This kind of linear relationship between the 24-h average monthly ozone and the monthly average M7 values is also observed at some of the other monitoring sites in India (Lal et al., 2017;Sinha et al., 2015). Figure 4 provides the derived relationship between the 24-h average monthly ozone and the monthly AOT40 values for the year 2018 at Patiala, Tezpur, and Delhi. At all three locations, we find that a polynomial fits well (R 2 = 0.96 for Patiala; R 2 = 0.98 for Tezpur; R 2 = 0.9 for Delhi) between the 24-h average ozone and the monthly AOT40 values of ozone. Such a nonlinear relationship between the 24-h average ozone and the monthly AOT40 values has already been noticed at some of the other sites over the Indian region (Sinha et al., 2015;Lal et al., 2017).

Crop loss and economic loss
We have calculated the average M7 and integrated AOT40 values for the wheat and rice crops for their respective crop periods, viz., January to March and August to October. Thereafter, the RYL has been calculated based on this data using Eqs. (3) to (7). Based on the CP for wheat and rice, the CPL has been calculated using Eq. (8). Finally, the EL has been calculated using the data for CPL and the MSP using Eq. (9). Table 1 shows the RYL values and crop losses for the wheat and rice crops using the M7 and AOT40 metrics at the three sites Patiala, Tezpur, and Delhi. The RYL values for the wheat crop fall in the range of 11-29%, 4-17%, and about 2% at Patiala, Delhi, and Tezpur, respectively. The %RYL values for the rice crop lie in the range of 2-10%, 1-7%, and 1-10% at Patiala, Delhi, and Tezpur, respectively. For both the crops at Patiala and Delhi, we find that the RYL values calculated using the AOT40 metrics are on the higher side as compared to those estimated from the M7 metrics (Lal et al., 2017;Sinha et al., 2015;Debaje, 2014;Hollaway et al., 2012;Van Dingenen et al., 2009). Since AOT40 is linearly related to the relative yield loss, so for a given change in AOT40, the changes in RYL and the subsequent crop losses are more. The M7 index has a non-linear Weibull relationship with the relative yield loss and the changes in M7 produces smaller changes in the RYL and associated crop losses (Hollaway et al., 2012). At Tezpur for the rice crop, the RYL values calculated using the AOT40 metrics are comparatively higher than the RYL values based on the M7 metrics. However, for the wheat crop at Tezpur, the RYL values calculated both from the AOT40 and the M7 metric fall in the same range. During the wheat cropping season at Tezpur, in the winter period from January to March, the temperatures are very low and the ozone mixing ratios seldom crosses the cut-off level of 40 ppb which is considered for the calculation of AOT40. So the monthly AOT40 values are quite low, and the RYL values from the AOT40 metric are close to that based on the M7 metric. Table 2 shows the annual crop loss (Mt, metric ton) for the wheat and rice crops based on the AOT40 and M7 metrics at the three stations (Patiala, Tezpur, and Delhi).   (1-10%) at Patiala, Delhi, and Tezpur, respectively. For both wheat and rice crops at Patiala and Delhi, the losses estimated from the AOT40 metrics are comparatively higher than that estimated using the M7 metrics (Lal et al., 2017;Sinha et al., 2015;Debaje, 2014;Hollaway et al., 2012;Van Dingenen et al., 2009). At Tezpur for the rice crop, the losses based on the AOT40 metric are higher as compared to the losses calculated from the M7 metric. However, for the wheat crop at Tezpur, the crop production losses calculated from the AOT40 and M7 metrics are close to each other. In the wheat growing season from January to March at Tezpur, the temperatures are quite low and the ozone concentrations rarely cross the threshold level of 40 ppb which is considered for the calculation of AOT40. So the monthly AOT40 values are very low and the crop losses calculated using the AOT40 metric come very close to the losses estimated from the M7 metric. At Patiala and Delhi, the crop production loss for wheat is seen to be higher as compared to the crop loss for rice. During the Kharif rice crop growing season towards the end of the southwest monsoon at Patiala and Delhi, the ozone levels are lower as compared to those during the wheat crop growing season, especially at the end of winter. So the impact of damage due to ozone exposure on the rice crop is lesser as compared to that on the wheat crop. Also, the production area for the wheat crop is quite higher as compared to that for the rice crop in Delhi. However at Tezpur, the loss for the rice crop is seen to be higher as compared to the loss for the wheat crop. At Tezpur, the monsoon season coincides with the summer months with August being the warmest month of the year when the monthly average M7 values and the monthly AOT40 values reach their maximum levels. Also, the area of production for the rice crop is much higher as compared to that for the wheat crop at Tezpur. Since the cropping season for the Kharif rice falls from August to October, so the ozone-induced damages and the crop losses for the rice crop are quite higher as compared to that for the wheat crop at Tezpur. Table 3 shows the comparison of the wheat and rice crop loss estimates at Patiala, Tezpur, and Delhi  (Sinha et al., 2015) have estimated the crop losses for wheat and rice for the two states of Punjab and Haryana using observational data from a single site at Mohali and have considered the crop growing period of 4-4.5 months. Debaje (2014) estimated the losses in wheat and rice crops over the Indian region based on the observed data available from the three sites in Maharashtra and the data obtained from the six other sites in other states of India. However, only M7 was calculated from the observed data and AOT40 was derived using a linear relation . Since the M7 and  Ghude et al. (2014) used the WRF/Chem model and estimated the wheat and rice crop losses using only the AOT40 metric for the calculation of the crop loss estimates. Also, they have considered the crop growing season for rabi wheat to be from December to February instead of January to March as considered in the present study and also in Lal et al. (2017) and Debaje (2014). So there can be an underestimation of AOT40 values, RYL values, and crop yield losses due to the exclusion of the ozone-sensitive growth period of the month of March for the rabi wheat crop. Avnery et al. (2011a, b) used a global chemistry transport model (MOZART2) for estimating the annual loss of wheat crops over the Indian region for the year 2000 using the crop data for the same year. Since the agricultural production data which has been considered and the emission data used for generating the ozone fields is almost two decades old, so their calculations for crop losses are underestimated. Van Dingenen (2009) applied the global chemistry transport model TM5 for estimating crop yield losses over India for the year 2000. These two studies would have given much higher estimates for crop losses over the Indian region if the calculations were done with crop production data of the recent years and the emission data of recent times were used for the generation of the ozone mixing ratios. Also, global models tend to over-predict the ozone levels for the Indian region particularly over northern India as compared to observational values as seen from the comparisons they have provided (Lal et al., 2017). This would have further enhanced the levels of AOT40 and M7 and subsequently the estimates for RYL and crop losses over India. It is worth mentioning that the losses for the wheat and rice crop over India, as estimated in some of these studies surpasses the losses for these two crops across China for the year 2015 (8% yield loss for wheat and 6% for rice) (Feng et al., 2019).

Summary and conclusions
Very few studies have investigated the ozone-induced relative yield losses and crop losses over the Indian region, especially over the IGP. The IGP has been known to be a high emission-prone area as seen in some of the studies over the Indian region (Beig & Brasseur, 2006;Beig & Ali, 2006;Ghude et al., 2008;Roy et al., 2008;Sahu et al., 2008;Kulkarni et al., 2009). In this study, we present a comparison of the ozone exposure indices, relative yield losses, crop losses, and associated economic losses for the two important crops (wheat and rice) at three suburban sites (Patiala, Tezpur, and Delhi) situated in different regions of India. We have used the observational data from MAPAN and compared our crop loss estimates for the year 2018 with those obtained from other studies over the Indian region. It was found that over the Indian region, the 24-h monthly average ozone is linearly related to M7 and a polynomial only fits better between the 24-h average ozone and the monthly AOT40 values (Lal et al., 2017;Sinha et al., 2015). The yearly economic cost loss comes out to be around $0.13 million for the wheat crop and about $0.04 million for the rice crop at Patiala. At Tezpur, the annual economic loss for the rice crop is about $0.7 million, and for the wheat crop, it is around $0.005 million only. The economic loss per annum for the wheat and rice crop in Delhi is around $4.6 million and $0.5 million, respectively. It is important to note that at places like Patiala where the crop yield is high and the AOT40 levels are on the higher side, the relative yield losses can reach the level of 11-29% and 2-10% for wheat and rice crops, respectively. The annual crop losses fall in the high range of 12.4-40.8% and 2.0-11.1% for the wheat and rice crops, respectively, at Patiala. The economic cost loss per annum can be as high as $4.6 million and $0.7 million for wheat and rice crops, respectively, even at individual locations within India. Such ozone-induced crop losses and high economic losses can be a potential threat to food security and the annual GDP (gross domestic product) of India. In this study, the monitoring sites are located in suburban areas where ozone levels are lower as compared to the rural areas where most of the cropping lands are present. The ozone mixing ratios in rural areas can be higher than that of the urban areas due to the transport of ozone-rich air to remote and distant locations Kumar et al., 2013;Lal et al., 2013Lal et al., , 2014Naja et al., 2004). So the estimations for ozone-induced crop losses are slightly underestimated and can be considered as a limitation of this work. This work focuses on individual sites in detail for analyzing the damaging effects of ozone on crop yields over India using the MAPAN network data for the first time. This can be considered a relatively new feature or aspect as compared to earlier observational studies done in this regard, where the crop losses over India are calculated as an average of the data obtained from selected sites over the Indian region. The results based on this study which are more focused on individual locations over the Indian domain clearly add to the debate regarding the uncertainty and underestimation involved in the ozone-induced crop losses and economic losses over India. Timely sowing of the crops by adjusting the sowing date when the ozone levels are comparatively lower can help in the reduction of the ozone exposure of crops and increase the crop yield. Also, high yielding and ozone-resistant wheat and rice cultivars which are less vulnerable to ozone damage need to be identified and promoted further (Lal et al., 2017). Further studies and field experiments are required to derive the region-specific E-R functions for the Indian crop cultivars, since the seed types, climatic conditions, and agriculture practices are quite different in this region as compared to those in the mid and high-latitude regions (Lal et al., 2017).
Further field experiments are needed to develop the ozone response functions based on the stomatal-flux approach. Effective emission mitigation strategies can help in reducing ozone-induced crop production losses and boost the economy while ensuring food security in India.