Influence of Meteorology and interrelationship with greenhouse gases ( CO 2 and CH 4 ) at a suburban site of India

Atmospheric greenhouse gases (GHGs), such as carbon dioxide (CO2) and methane (CH4), are important climate forcing agents due to their significant impacts on the climate system. The present study brings out first continuous measurements of atmospheric GHGs using highprecision LGR-GGA over Shadnagar, a suburban site of Central India during the year 2014. The annual mean CO2 and CH4 over the study region are found to be 394± 2.92 and 1.92± 0.07 ppm (μ± 1σ ) respectively. CO2 and CH4 show a significant seasonal variation during the study period with maximum (minimum) CO2 observed during pre-monsoon (monsoon), while CH4 recorded the maximum during postmonsoon and minimum during monsoon. Irrespective of the seasons, consistent diurnal variations of these gases are observed. Influences of prevailing meteorology (air temperature, wind speed, wind direction, and relative humidity) on GHGs have also been investigated. CO2 and CH4 show a strong positive correlation during winter, pre-monsoon, monsoon, and post-monsoon with correlation coefficients (Rs) equal to 0.80, 0.80, 0.61, and 0.72 respectively, indicating a common anthropogenic source for these gases. Analysis of this study reveals the major sources for CO2 are soil respiration and anthropogenic emissions while vegetation acts as a main sink, whereas the major source and sink for CH4 are vegetation and presence of hydroxyl (OH) radicals.


Introduction
The Intergovernmental Panel on Climate Change (Stocker et al., 2013) reported that humankind is causing global warming through the emission of greenhouse gases (GHGs), particularly carbon dioxide (CO 2 ) and methane (CH 4 ).CO 2 and CH 4 concentrations, have increased by 40 and 150 % respectively since pre-industrial times, mainly from fossil fuel emissions and secondarily from net land use change emissions (Stocker et al., 2013;Huang et al., 2015).CO 2 measurements at Mauna Loa, Hawaii (Monastersky, 2013) have exceeded the 400 ppm mark several times in May 2013.CH 4 is also receiving increasing attention due to high uncertainty in its sources and sinks (Keppler et al., 2006;Miller et al., 2007;Frankenberg et al., 2008).Kirschke et al. (2013) reported that in India, agriculture and waste constitute the single largest regional source of CH 4 .Although many sources and sinks have been identified for CH 4 , their relative contribution to atmospheric CH 4 is still uncertain (Garg et al., 2001;Kirschke et al., 2013).In India, electric power generation contributes to half of India's total CO 2 equivalent emissions (Garg et al., 2001).
Arid and semi-arid areas comprise about 30 % of the Earth's land surface.Climate change and climate variability will likely have a significant impact on these regions (Huang et al., 2008(Huang et al., , 2015)).The variability of environmental factors may result in significant effects on regional climate and global climate (Wang et al., 2010), especially the radiative forcing, via the biogeochemical pathways affecting the terrestrial carbon cycle.Global climate change has serious impacts on humans and ecosystems.Due to this, many factors have been identified that may reflect or cause varia-G.Sreenivas et al.: Influence of Meteorology and interrelationship with greenhouse gases tions in environmental change (Pielke et al., 2002).Out of these, the Normalized Difference Vegetation Index (NDVI) has become one of the most widely used indices to represent the biosphere influence on global change (Liu et al., 2011).Greenhouse and other trace gases have great importance in atmospheric chemistry and for radiation budget of the atmosphere-biosphere system (Crutzen, 1991).Hydroxyl radicals (OH) are very reactive oxidizing agents, which are responsible for the oxidation of almost all gases that are emitted by natural and anthropogenic activities in the atmosphere.Atmospheric CO 2 measurements are very important for understanding the carbon cycle because CO 2 mixing ratios in the atmosphere are strongly affected by photosynthesis, respiration, oxidation of organic matter, biomass and fossil fuel burning, and air-sea exchange process (Machida et al., 2003).
The present study brings out first continuous measurements of atmospheric GHGs using high-precision LGR-GGA over Shadnagar, a suburban site of Central India during the year 2014.In addition to GHG observations, we have also made use of an automatic weather station (AWS) data along with model/satellite-retrieved observation during the study period.Details about study area and data sets are described in the following sections.

Study area
Shadnagar is situated in the Mahabubnagar district of the newly formed Indian state of Telangana.It is a suburban location situated ∼ 70 km away from the urban site of Hyderabad (northern side) with a population of ∼ 0.16 million (Patil et al., 2013).A schematic map of study area is shown in Fig. 1a.Major sources of pollutants over Shadnagar can be from small-and medium-scale industries, biomass burning, and bio-fuel as well as from domestic cooking.In the present study, sampling of GHGs and related meteorological parameters is carried out in the premises of the National Remote Sensing Center (NRSC), Shadnagar Campus (17 • 02 N, 78 • 11 E).A national highway and railway track (non-electrified) are to the east of the sampling site at an aerial distance of approximately 2.25 km.
Mean monthly variations of temperature ( • C) and relative humidity (RH %) observed at Shadnagar during 2014 are shown in Fig. 1e and d respectively.The Indian Meteorological Department defined monsoon as June-July-August-September, JJAS), post-monsoon (October-November-December, OND), winter (January-February, JF), and pre-monsoon (March-April-May, MAM) seasons in India.Temperature over Shadnagar varies from ∼ 20 to ∼ 29 • C. RH in Shadnagar reached a maximum of ∼ 82 % in monsoon from a minimum of ∼ 48 % recorded during pre-monsoon.Surface wind speed (Fig. 1c) varies between 1.3 and 1.6 m s −1 with a maximum observed during monsoon and minimum in pre-monsoon.The air mass advecting (Fig. 1b) towards the study site is either easterly or westerly.The easterly wind prevails during winter and gradually shifts to south-westerlies in pre-monsoon, and it dominates during monsoon.

Data set and methodology
Details about the instrument and data utilized are discussed in this section.The availability and frequency of the observations of all data used in present study are tabulated in Table 1.

Greenhouse Gas Analyser (GGA)
The Los Gatos Research's Greenhouse Gas Analyser (model: LGR-GGA-24EP) is an advanced instrument capable of simultaneous measurements of CO 2 , CH 4 , and H 2 O.This instrument is well known for high precision and accuracy, both of which are crucial for understanding background concentrations of atmospheric GHGs, with specifications meeting WMO standards of measurement (Berman et al., 2012;Shea et al., 2013;Mahesh et al., 2015).It is based on enhanced off-axis integrated cavity output spectroscopy (OA-ICOS) technology (Paul et al., 2001;Baer et al., 2002), which utilizes true wavelength scanning to record fully resolved absorption line shapes.Considering the nature of the site, flow rate is fixed at 7 L min −1 .Ambient air entering the GGA is analysed using two near-infrared distributed feedback tunable diode lasers: one for a CO 2 absorption line near 1.60 µm (ν 0 = 6250 cm −1 ) and the other to probe CH 4 and H 2 O absorption lines near 1.65 µm (ν 0 = 6060.60cm −1 ).The concentration of the gases is determined by the absorption of their respective characteristic absorption lines with a high sampling time of 1sec.A detailed explanation regarding the configuration, working, and calibration procedure performed for GGA in NRSC can be found elsewhere in Mahesh et al. (2015).In the present study we used GGA retrieve CO 2 and CH 4 data.High-resolution data sets are diurnally averaged and used in further analysis.Due to the failure of internal central processing unit of the analyzer, data are not recorded from pre-monsoon month of 1 May to 18 June during the study period.

O 3 and NO x analyzer
Surface concentrations of O 3 and NO x have been measured continuously using online analyzers (model nos.49i and 42i for O 3 and NO x respectively) from Thermo Scientific, USA, since July 2014.The trace gas (O 3 and NO x ) sampling inlet is installed on the top of a 2 m mast fixed on the roof of an 8 m high building, and ambient air flow is supplied to the instruments.The inlet prevents the ingress of rain water and is equipped with 0.5 µm filter to prevent accumulation of dust within the instrument.The ozone analyzer is based

Moderate-resolution Imaging Spectrometer (MODIS)
The MODIS was launched in December 1999 on the polar-orbiting NASA-EOS Terra platform (Salomonson et al., 1989;King et al., 1992).It has 36 spectral channels and acquires data in three spatial resolutions of 250, 500, and 1 km (channels 8-36), covering the visible, nearinfrared, shortwave infrared, and thermal-infrared bands.In the present study we used monthly NDVI data obtained from Terra/MODIS at 5 km spatial resolution.The NDVI value is defined as the following ratio of albedos (α) at different wavelengths: NDVI values can range from −1.0 to 1.0 but typical ranges are from 0.1 to 0.7, with higher values associated with greater density and greenness of plant canopies.More details of the processing methods used in generating the data set can be found in James and Kalluri (1994).

Constellation Observation System for Meteorology, Ionosphere and Climate radio occultation (COSMIC-RO)
COSMIC is a GPS (Global Positioning System) RO observation system (Wang et al., 2013).It consists of six identical microsatellites and was launched successfully on 14 April 2006.GPS RO observation has the advantage of near-global coverage, all-weather capability, high vertical resolution, high accuracy, and self-calibration (Yunck et al., 2000).Geophysical parameters (such as temperature and humidity profiles) have been simultaneously obtained from re-

HYSPLIT model
The general air mass pathway reaching over Shadnagar is analysed using the HYSPLIT model (Draxler and Rolph, 2003; http://www.arl.noaa.gov/ready/hysplit4.html.We computed 5-day isentropic model backward air mass trajectories for all study days with each trajectory starting at 00:60 UTC and reaching the study site (Shadnagar) at different altitudes (1, 2, 3, and 4 km).Even though the trajectory analysis have inherent uncertainties (Stohl, 1998), they are quite useful in determining long-range circulation.winter, pre-monsoon, monsoon, and post-monsoon respectively.Minimum CO 2 during winter (dry season) can be due to respiratory loss of carbon (Gilmanov et al., 2004;Aurela et al., 2004) as decreased temperature and solar radiation during this period inhibit increases in local CO 2 assimilation (Thum et al., 2009).A steady increase in CO 2 concentration is observed as season changes from winter to pre-monsoon months.Enhancement in pre-monsoon is due to higher temperature and solar radiation prevailing during these months, which stimulates the assimilation of CO 2 in the daytime and respiration in the night (Fang et al., 2014).The enhanced soil respiration during these months also compliments the increase in CO 2 concentration during this period.In addition to these natural causes, biomass burning over the Indian region can also have a significant effect on pre-monsoon CO 2 concentration.A more detailed explanation of biomass burning influence on pre-monsoon GHG concentration over the study area is discussed in Sect.4.2.Surface CO 2 concentration recorded a minimum during monsoon months mainly because of enhanced photosynthesis processes with the availability of greater soil moisture.A decrease in CO 2 concentration is also observed as the monsoon progress.The decreases in temperature (due to cloudy and overcast conditions prevailing during these months) reduce leaf and soil respiration, which contributes to the enhancement of carbon uptake (Patil et al., 2013;Jing et al., 2010).Further increase during postmonsoon CO 2 is associated with high ecosystem productivity (Sharma et al., 2014) as well as an enhancement in soil microbial activity (Kirschke et al., 2013).
CH 4 concentration in the troposphere is principally determined by a balance between surface emission and destruction by OH.The major sources for CH 4 in the Indian region are rice, paddies, wetlands, and ruminants (Schneising et al., 2009).The annual CH 4 concentration over study area is observed to be 1.92 ± 0.07 ppm, with a maximum (2.02 ± 0.01 ppm) observed in post-monsoon and minimum (1.85 ± 0.03 ppm) in monsoon.Seasonal mean (average) values of CH 4 observed during different seasons are 1.93 ± 0.05, 1.89 ± 0.05, 1.85 ± 0.03, and 2.02 ± 7 ppm in respectively winter, pre-monsoon, monsoon, and postmonsoon.The highest concentration appears during postmonsoon and may be associated with the Kharif season (Goroshiet al., 2011).Hayashida et al. (2013) reported that the seasonality of CH 4 concentration over monsoon Asia is characterized by higher values in the wet season and lower values in the dry season; possibly because of the effects of strong emissions from rice paddies and wetlands during the wet season.Low mixing ratios of CH 4 observed during monsoon season were mainly due to the reduction in atmospheric hydrocarbons because of the reduced photochemical reactions and the substantial reduction in solar intensity (Gaur et al., 2014).The rate of change of CH 4 was found to be high during post-monsoon.Both biological and physical processes control the exchange of CH 4 between rice paddy fields and the atmosphere (Nishanth et al., 2014;Goroshiet al., 2011).Due to this, enhanced CH 4 is observed during postmonsoon in the present study area.

Influence of vegetation on GHGs
In India, cropping season is classified into (i) Kharif and (ii) Rabi based on the onset of monsoon.The Kharif season is from July to October during the south-western monsoon and Rabi season is from October to March (Koshal, 2013).NDVI is one of the indicators of vegetation change, and monthly variations of CO 2 and CH 4 against NDVI are studied to understand the impact of land use and land cover on mixing ratios of CO 2 and CH 4 .Monthly mean changes in NDVI, CO 2 , and CH 4 are shown in Fig. 2c and d.Monthly mean of GHGs represented in this analysis is calculated from daily mean in daytime (10:00-16:00 LT).Analysis of the figure reveals that an inverse relationship exists between NDVI and 2 , while a positive relation is observed w.r.t.CH 4 .Generally over this part of the country vegetation starts during the month of June with the onset of south-western monsoon; as vegetation increases a decrease in CO 2 concentration is observed due to enhancement in photosynthesis.Further, a decline in NDVI is observed as the season advances from postmonsoon to winter and then to pre-monsoon, and it is associated with an increase in CO 2 concentration.Similarly, the main source for CH 4 emissions are soil microbial (Kirschke et al., 2013) activity which are more active during monsoon and post-monsoon seasons.
Biomass burning (forest fire and crop residue burning) is one of the major sources of gaseous pollutants such as carbon monoxide (CO 2 ), methane (CH 4 ), nitrous oxides (NO x ), and hydrocarbons in the troposphere (Crutzen et al., 1990(Crutzen et al., , 1985;;Sharma et al., 2010).In one of the recent studies, Jose et al. (2015a) analysed the atmospheric impact due to biomass mass burning over Hyderabad.In order to study the role of biomass burning on GHGs a case study is discussed.Figure 3c shows the spatial distribution of MODIS-derived fire counts over the Indian region during 14-21 April 2014 with air mass trajectories ending over study area overlaid on it at different altitudes, viz.1000, 2000, and 4000 m respectively.Analysis of the figure shows a number of potential fire locations on the north-western and south-eastern side of study location and trajectories indicate its possible transport to study area.Daily mean variation of GHGs during the month of April 2014 (Fig. 3b) indicates an enhancement in GHGs during the same period (14-21 April 2014).Analysis reveals that CO 2 and CH 4 have increased by ∼ 2 and ∼ 0.06 % respectively during event days with respect to monthly mean.This analysis reveals that long-range/regional transported biomass burning have a role in enhancement of GHGs over study site.Further to understand the seasonal variation of biomass burning contribution to GHGs we analysed long-term (2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013) Fire Energetics and Emissions Research version 1.0 (FEER v1) data over study area.Emission coefficient (C e ) products during biomass burring are developed from coinciding measurements of fire radiative power and aerosol optical depth from MODIS Aqua and Terra satellites (Ichoku and Ellison, 2014).Figure 3a shows seasonal variation of CO 2 emission due to biomass burning over the study site.Enhancement in CO 2 emission is seen during pre-monsoon months, which also supports earlier observation (Fig. 2a).This analysis reveals that biomass burning has a role in premonsoon enhancement of CO 2 over the study site.For a qualitative analysis of this long-range transport, we have analysed air mass trajectories ending over the study site during different seasons.

Correlation between CO 2 and CH 4
A correlation study is carried out between hourly averaged CO 2 and CH 4 during all season for the entire study period.The statistical analysis for different seasons is shown in Table 2. Fang et al. (2015) suggest the correlation coefficients (R s ) value higher than 0.50 indicates a similar source mechanism of CO 2 and CH 4 .Also a positive correlation dominance of anthropogenic emission on carbon cycle.Our study also reveals a strong positive correlation observed between CO 2 and CH 4 during winter, pre-monsoon, monsoon, and postmonsoon with R equal to 0.80, 0.80, 0.61, and 0.72 respectively.The regression coefficients showed maximum (minimum) during winter and pre-monsoon (monsoon), which indicates the hourly stability of the mixing ratios between CO 2 and CH 4 .This can be due to relatively simple source/sink processes of CO 2 in comparison to CH 4 .Figure 4 shows the seasonal variation of CH 4 / CO 2 .Dilution effects during transport of CH 4 and CO 2 can be minimized to some extent by dividing the increase of CH 4 over time by the respective increase in CO 2 (Worthy et al., 2009).In this study, background concentrations of respective GHGs are determined as mean values of the 1.25 percentile of data for monsoon, postmonsoon, pre-monsoon, and winter (Pan et al., 2011;Worthy et al., 2009).Annual CH 4 / CO 2 over the study region during the study period is found to be 7.1 (ppb ppm −1 ).This low value clearly indicates the dominance of CO 2 over the study region.The reported CH 4 / CO 2 values from some of the rural sites, viz.Canadian Arctic and Hateruma Island (China), are of the order 12.2 and ∼ 10 ppb ppm −1 respectively (Worthy et al., 2009;Tohjima et al., 2014).Average CH 4 / CO 2 ratio during winter, pre-monsoon, monsoon, and post-monsoon are 9. 40, 6.40, 4.40, and 8.20 ppb respectively.The monthly average of CH 4 / CO 2 is relatively high from late post-monsoon to winter, when the biotic activity is relatively dormant (Tohjima et al., 2014)   The vertical bar represents the standard deviation from the respective mean.Irrespective of seasonal variation, GHGs showed a similar diurnal variation, with maximum mixing ratios observed during early morning (6 h) as well as early night hours (20 h) and minimum during afternoon hours.However, the difference observed in the maximum diurnal amplitudes can be attributed to seasonal changes.The observed diurnal cycle of GHGs is closely associated with diurnal variation of the planetary boundary layer height.For better understanding of the diurnal behaviour of CO 2 / CH 4 , we used European Centre for Medium-range Weather Forecasting (ECMWF) Interim Reanalysis (ERA) PBL data set, which gives data for every 3 h, viz.00:00, 03:00, 06:00, 09:00, 12:00, 15:00, 18:00, and 21:00 UTC with a resolution of 0.25 • × 0.25 • (http://data-portal.ecmwf.int).Figure 5a to d portray the diurnal evolution of CO 2 / CH 4 during different seasons along with the evolution of BLH (m) on the secondary y axis.The morning peak arises due to combined influence of the fumigation effect (Stull 1988) and morning build-up of local anthropogenic activities (household and ve-hicular transport).The low value of GHGs as the day progresses can be attributed to increased photosynthetic activity during daytime and the destruction of stable boundary layer and residual layer due to convective activity.In the evening hours, surface inversion begins and form a shallow stable boundary layer, causing the enhancement in GHG concentration near the surface.A similar trend in diurnal variation of GHGs is reported from other parts of the country (Patil et al., 2013;Mahesh et al., 2013;Sharma et al., 2014;Nishanth et al., 2014).

Influence of prevailing meteorology
Redistribution (both horizontal and vertical) of GHGs also plays a role in their seasonal variation, as it controls transport and diffusion of pollutants from one place to another (Hassan, 2015).A good inverse correlation between wind speed and GHGs suggests the proximity of sources near the measurement site, while a not so significant correlation suggests the influence of regional transport (Ramachandran and Rajesh, 2007).Figure 6a and b show a scatter plot of the GHGs and wind speed during different seasons.Analysis of Fig. 6 shows that an inverse correlation exists between daily mean wind speed and GHGs.Correlation coefficients (R s ) between  wind speed and CO 2 during pre-monsoon, monsoon, postmonsoon, and winter are 0.56, 0.32, 0.06, and 0.67 respectively, while for CH 4 they are found be 0.28, 0.71, 0.21, and 0.60 respectively.The negative correlation indicates that the influence of local sources on GHGs; however, the poor correlation coefficients during different seasons suggest the role of regional/local transport (Mahesh et al., 2014).Also, an understanding of prevailing wind direction and its relationship with GHGs helps in determining their probable source regions.Table 3 shows the monthly mean variation of CO 2 and CH 4 with respect to different wind directions.Enhancements in the CO 2 and CH 4 levels over Shadnagar are observed to mainly come from north-west and north-east, while the lowest are from the south and south-west.This can be associated to some extent with industrial emissions located on the western side of the sampling site and the influence of emission and transport from the nearby urban centre on the northwestern side of the study site.
The influence of meteorological parameters (temperature and relative humidity) on trace gases is also examined.ative humidity as a function of GHGs during different seasons.Daily mean data are used instead of hourly mean data to avoid the influence of the diurnal variations on correlations.CO 2 showed a positive correlation with temperature during all seasons except winter.This negative correlation can be attributed to different responses of the photosynthesis rate to different air temperature.IPCC (1990) reports that many mid-latitude plants show an optimum gross photosynthesis rate when temperature varied from 20 to 35 • C. The rate of plant respiration tends to be slow below 20 • C.However, at higher temperatures, the respiration rate accelerates rapidly up to a temperature at which it equals the rate of gross photosynthesis and there can be no net assimilation of carbon.While CH 4 showed a weak positive correlation with temperature during pre-monsoon and post-monsoon, a weak negative correlation is observed during monsoon and winter.This could be due to the rate of chemical loss reaction with OH being faster in summer and slower in other seasons.A case study on CH 4 sink mechanism is discussed in Sect.4.6.Seasonal variation of GHGs also showed an insignificantly negative correlation with RH.One of the supporting arguments is that in humid conditions these stoma can fully open to increase the uptake of CO 2 without a net water loss.Also, wetter soils can promote decomposition of dead plant mate-rials, releasing natural fertilizers that help plants grow (Gaur et al., 2014).Figure 8a and b illustrate the daily mean variation of GHGs with respect to soil moisture and soil temperature (top panel represents the seasonal variation of CO 2 w.r.t.soil moisture and soil temperature, while the bottom panel represents the seasonal variation of CH 4 against the same parameters).It is quite interesting to observe that GHGs behave differently w.r.t.soil moisture during different seasons.CH 4 shows a positive relationship during monsoon and postmonsoon and an inverse relationship during pre-monsoon and winter, while a reverse relationship exists for CO 2 .During the wet season aeration is restricted (Smith et al., 2003); hence soil respiration is limited, which decrease CO 2 flux.This may be one of the factors leading to low values of CO 2 during monsoon months; during dry months soil may act as a sink of CH.

Influence of BLH on GHG mixing ratios
The PBL is the lowest layer of the troposphere, where wind speed as a function of temperature plays a major role in its thickness variation.It is an important parameter for controlling the observed diurnal variations and potentially masking the emissions signal (Newman et al., 2013).Since a complete set of COSMIC-RO data is not available during the study period, in this analysis we have analysed RO data from July 2013 to June 2014, along with simultaneous observations of GHGs.Monthly variations (figure not show) of BLH are computed from high-vertical-resolution COSMIC-RO data and compared against CO 2 and CH 4 concentrations.Monthly BLH is observed to be minimum (maximum) during winter and monsoon (pre-monsoon) seasons and it closely resembles with the air temperature pattern.The highest (lowest) BLH over the study region was identified as 3.20 km (1.50 km).The monthly average air temperature was at maximum (minimum) 29 • C (20 • C) during the summer (winter) months.
Seasonal BLHs during winter, pre-monsoon, monsoon, and post-monsoon are 2.10, 3.15, 1.74, and 2.30 km respectively.The influence of BLH on CO 2 and CH 4 mixing ratios is shown in Fig. 9a and b.The x axis represents the seasonal transition, i.e. monsoon to post-monsoon, and the y axis indicates seasonal difference of BLH and GHG concentrations.As seasonal BLHs increase, mixing ratios of CO 2 (CH 4 ) decrease from 8.68 to 5.86 ppm (110 to 40 ppb).This effect was clearly captured by seasonal diurnal averaged BLH data sets used from ECMWF-ERA.The influence of biosphere emissions on CO 2 and CH 4 can be estimated through atmospheric boundary layer processes.Since the study region is a flat terrain, variations in CO 2 and CH 4 were mostly influenced by BLH through convection and biosphere activities.

Methane (CH 4 ) sink mechanism
Methane (CH 4 ) is the most powerful greenhouse gas in the atmosphere after CO 2 due to its strong positive radiative forcing (Stocker et al., 2013).Atmospheric CH 4 is mainly (70-80 %) of biological origin, produced in anoxic environments by anaerobic digestion of organic matter (Crutzen and Zimmermann, 1991).The major CH 4 sink is oxidation by OH, which account for 90 % of CH 4 sink (Vaghjiani and Ravis-hankara, 1991;Kim et al., 2015).OH radicals are very reactive and are responsible for the oxidation of almost all gases in the atmosphere.The primary source for OH radical formation in the atmosphere is photolysis of ozone (O 3 ) and water vapour (H 2 O).Eisele et al. (1997) defined primary and secondary sources of OH radicals in the atmosphere.The primary source of OH radical is as follows: where O ( 1 D) is the electronically excited atom. O Removal of CH 4 is constrained by the presence of OH radicals in the atmosphere.A 1 min time series analysis of CH 4 , NO x , O 3 , and H 2 O and associated wind vector for August 2014 is shown in Fig. 10a and b to understand the CH 4 chemistry.Low NO x (1-2 ppb) values are shown in horizontal elliptical region of Fig. 10a and observed corresponding low CH 4 (1.80 ppm) concentrations.The low NO x in turn produces high OH radicals in the atmosphere due to conversion of HO 2 radical by NO, which removes CH 4 through oxidation process as shown below.
when NO x levels are 1-2 ppb.The main CH 4 removal process is if NO x > 2 ppb (OH ↓, CH 4 ↑).Crutzen and Zimmermann (1991) and Eisele et al. (1997) observed that at low NO x (0.5-2.0 ppb) levels most HO x family radicals such as HO 2 and peroxy radicals (RO 2 ) react with NO to form OH radicals.Therefore OH radicals are much higher in the case of low NO x .When NO x levels increase more than 2 ppb, most of the OH radicals react with NO 2 to form nitric acid (HNO 3 ).In first order, the levels of CH 4 in the atmosphere depend on the levels of NO x though the production of OH radicals in the atmosphere is still uncertain.Figure 10a

Long-range circulations
To understand the role of long-range circulation, we separated the trajectory into four clusters based on their pathway, namely north-east, north-west, south-east, and south-west.
The main criterion of trajectory clustering is to minimize the variability among trajectories and maximize variability among clusters.Cluster mean trajectories of air mass (Jose et al., 2015b) and their percentage contribution to the total calculated for each season over the study period at 3 km altitude are depicted in Fig. 11.The majority of air mass trajectories during winter (∼ 44 %), pre-monsoon (∼ 64 %), monsoon (∼ 80 %), and post-monsoon (∼ 41 %) originate from north-western parts of the study site.For a comprehensive analysis, percentage occurrences of cluster mean trajectories of air mass over study area during different seasons at different altitudes are also tabulated in Table 4. Agriculture residue burning is commonly observed in the NW and NE regions of India during the post-harvest period extending from the postmonsoon period to the pre-monsoon period (Sharma et al., 2010).Our analysis reveals that during this period, the majority of air mass reaching the study site at different altitudes comes from this part of the country.

Conclusions
The present study analysed the seasonal variations of atmospheric GHGs (CO 2 and CH 4 ) and associated prevailing meteorology over Shadnagar, a suburban site of Central India, during the period 2014.The salient findings of the study are the following.
-Irrespective of seasons, major sources for CO 2 are soil respiration and anthropogenic emissions while vegetation acts as a main sink, whereas the major source and sink for CH 4 are vegetation and the presence of OH radicals.In addition, boundary layer dynamics and longrange transport also play a vital role in GHG mixing ratios.
-The annual means of CO 2 and CH 4 over the study region are found to be 394 ± 2.92 and 1.92 ± 0.07 ppm (µ ± 1σ ) respectively.CO 2 and CH 4 show a significant seasonal variation during the study period.Maximum (minimum) CO 2 is observed during pre-monsoon (monsoon), while CH 4 recorded maximum during postmonsoon and minimum during monsoon.Seasonal analysis of FEER data also showed maximum emission of CO 2 due to biomass burning during pre-monsoon months, which indicates the influence of biomass burning on local emissions.
-Correlation coefficients (R s ) between wind speed and CO 2 during pre-monsoon, monsoon, post-monsoon, and winter are 0.56, 0.32, 0.06, and 0.67 respectively, while for CH 4 they are found be 0.28, 0.71, 0.21, and 0.60 respectively.The negative correlation indicates that the influence of local sources on GHGs; however, poor correlation coefficients during different seasons suggest the role of regional/local transport.
-CO 2 showed a positive correlation with temperature during all seasons except winter.CH 4 showed a weak positive correlation with temperature during premonsoon and post-monsoon while showing a weak negative correlation during monsoon and winter.
-CO 2 and CH 4 showed a strong positive correlation during winter, pre-monsoon, monsoon, and post-monsoon with R s equal to 0.80, 0.80, 0.61, and 0.72 respectively.This clearly indicates common anthropogenic sources for these gases.

Figure 1 .
Figure 1.(a) Schematic representation of study area; (b-e) seasonal variation of prevailing meteorological conditions during 2014.

Figure 2 .
Figure 2. (a-b) Temporal variations of CO 2 and CH 4 ; (c-d) seasonal variations of CO 2 and CH 4 in conjunction with NDVI (Normalized Difference Vegetation Index) during 2014.

Figure 3 .
Figure 3. (a) Long term analysis of CO 2 biomass burning emissions over the study region; (b) biomass signatures on CO 2 / CH 4 during 14-21 April 2014 (a case study); (c) spatial distribution of MODIS-derived fire counts over Indian region during 14-21 April 2014.

Figure 4 .
Figure 4. Monthly variation of CH 4 / CO 2 during the study period.
Figure 5. (a-d) Seasonal variations of diurnal averaged CO 2 / CH 4 against boundary layer height during 2014.

Figure 6 .
Figure 6.(a-b) Daily mean scatter plot of wind speed and GHGs (CO 2 and CH 4 ).

Figure 7 .
Figure 7. (a-b) Daily means seasonal variation of CO 2 and CH 4 as a function of humidity and air temperature during 2014.

Figure 8 .
Figure 8. (a-b) Daily means seasonal variation of CO 2 and CH 4 as a function of soil temperature and soil moisture during 2014.
Figure 7a and b (top panel corresponds to CO 2 and bottom panel represents CH 4 ) show the scatter plot of temperature vs. rel-

Figure 9 .
Figure 9. Seasonal differences in BLH against respective change in (a) CO 2 and (b) CH 4 .

Figure 10 .
Figure 10.Time series analysis of (a) CH 4 vs.NO x and (b) H 2 O vs. O 3 .

Figure 11 .
Figure 11.(a-d) Long-range circulation of air mass trajectories ending over Shadnagar at 3 km during winter, pre-monsoon, monsoon, and post-monsoon.

Table 2 .
Statistical correlation between CO 2 and CH 4 .

2016 3960 G. Sreenivas et al.: Influence of Meteorology and interrelationship with greenhouse gases
. During premonsoon the decease in CH 4 / CO 2 ratio indicates the enhancement of CO 2 relative to that of CH 4 .

Table 3 .
Seasonal amplitudes of CO 2 and CH 4 over the study region arriving from different directions.

4 Diurnal variations of CO 2 and CH 4
Figure 5a to d show the seasonally averaged diurnal cycle of CO 2 and CH 4 over Shadnagar during the study period.

Table 4 .
Cluster analysis of air mass trajectories reaching Shadnagar at various heights during different seasons.) km km km km km km km km km km km km km km km km and b show high CH 4 , H 2 O, O 3 , and NO x over a few days in August 2014.High concentrations of CH 4 , NO x , and other gases are observed in the eastern direction of study site.Very high NO x levels above 10 ppb are observed, and subsequently CH 4 concentrations also increased to 2.40 ppm from 1.80 ppm.In the eastern direction of study site a national highway and single line broad gauge railway network are present, which act as possible sources of NO x , CH 4 , and CO 2 .An increase in emissions of NO x causes a decline in the levels of OH radicals and subsequently observed high CH 4 over the study region.