Mriganka Sekhar Biswas1,2, Sachin D. Ghude1, Dinesh Gurnale1, Thara Prabhakaran1, Anoop S. Mahajan 1

Indian Institute of Tropical Meteorology, Ministry of Earth Sciences, Pune 411008, India
Savitribai Phule Pune University, Pune 411007, India


Received: December 28, 2018
Revised: March 14, 2019
Accepted: May 13, 2019

Download Citation: ||https://doi.org/10.4209/aaqr.2018.12.0484  

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Cite this article:

Biswas, M.S., Ghude, S.D., Gurnale, D., Prabhakaran, T. and Mahajan, A.S. (2019). Simultaneous Observations of Nitrogen Dioxide, Formaldehyde and Ozone in the Indo-Gangetic Plain. Aerosol Air Qual. Res. 19: 1749-1764. https://doi.org/10.4209/aaqr.2018.12.0484


HIGHLIGHTS

  • Simultaneous observations of NO2, HCHO and O3 in the Indo-Gangetic plain (IGP).
  • Power plants located ~110 km towards the south affect the local NO2 concentrations.
  • HCHO concentrations are homogenous over the region.
  • Ozone concentrations are affected more by local sources than long range transport.
 

ABSTRACT


This study reports the concentrations of nitrogen dioxide (NO2) and formaldehyde (HCHO), retrieved using the Multi AXis Differential Optical Absorption Spectroscopy (MAX-DOAS) technique and collocated observations of surface ozone (O3) conducted over the Indo-Gangetic Plain (IGP) during the 2014 monsoon period as part of the Cloud Aerosol Interaction and Precipitation Enhancement Experiment (CAIPEEX). The average daytime NO2 mixing ratio was 0.81 ± 0.20 ppbv (parts per billion by volume) (range: 0.08–6.06 ppbv). NO2 was observed to decrease during the morning between 06:00 and 09:00 local time and then stabilise for the rest of the day. The average daytime HCHO mixing ratio was 1.93 ± 0.60 ppbv (range: 0.32–8.81 ppbv). Unlike NO2, HCHO, driven by daytime photochemical formation from hydrocarbon precursors, increased during the early morning. The average O3 mixing ratio was 30.0 ± 13.0 ppbv (range: 2.7–81.9 ppbv) during the daytime and 22.5 ± 10.2 ppbv (range: 1–63 ppbv) during the nighttime. Analyses of the back trajectories indicatedfound that the NO2 mixing ratios during CAIPEEX-2014 were affected by long-range transport from thermal power plants situated about 110 km to the south but the HCHO mixing ratios and O3 production were influenced by local emissions. These observations suggest that in rural IGP, ozone concentrations are affected by local emission rather than by long-range transport.


Keywords: Nitrogen dioxide; Formaldehyde; Ozone; Indo-Gangetic Plain.


INTRODUCTION


Nitrogen dioxide (NO2) is one of the most important trace gases in the atmosphere (Crutzen, 1979). Apart from being an atmospheric oxidant and pollutant (Burnett et al., 2004), it acts as a precursor for ozone (O3) (Crutzen, 1970) and affects the hydroxyl radical (OH) abundance through reactive photochemistry. As a pollutant, NO2 is associated with various health hazards (World Health Organization, 2013) and contributes towards acid rain and nitrate aerosol formation. It plays an indirect role in secondary aerosol formation (Chan et al., 2010) and locally contributes towards changes in radiative forcing (Solomon et al., 1999), hence indirectly affecting the climate system. Elevated tropospheric NO2 abundance usually coincides with other atmospheric pollutants, and hence, it can be used as a proxy for atmospheric pollution in general (Mayer, 1999; Molina and Molina, 2004). Major anthropogenic sources of NO2 include high temperature combustion of fossil fuels, biomass burning, industries, thermal power plants and automobiles. Forest fires, lightning and soil microbial processes are considered to be the main natural sources (Jaeglé et al., 2005). Formation of nitric acid (HNO3) during the daytime and dinitrogen pentoxide (N2O5) hydrolysis during night along with dry deposition and transport are the dominant sinks of tropospheric nitrogen oxides (NOx) (Finlayson-Pitts and Pitts, 2000; Jacob, 2000). Understanding of current emissions (Martin et al., 2006), the performance of chemical transport models (Zyrichidou et al., 2009; Huijnen et al., 2010, and references therein) and changes in anthropogenic emissions (Stavrakou et al., 2008; Wang et al., 2012) have been studied using satellite retrieved NO2 columns. Observations of NO2 (Johansson et al., 2009; Wagner et al., 2010; Constantin et al., 2013) using the Differential Optical Absorption Spectroscopy (DOAS) technique (Noxon, 1975; Solomon et al., 1987) have been reported at various locations around the world and in several cases have been used for retrieving vertical profiles of atmospheric NO2 and also for validating satellite observations (Hönninger et al., 2004; Frieß et al., 2006; Wagner et al., 2011; Hendrick et al., 2014).

Formaldehyde (HCHO) is the most abundant and smallest carbonyl compound observed in the troposphere (Hak et al., 2005, and references therein). It is an intermediate oxidation product of various volatile organic compounds (VOCs). HCHO has biogenic (e.g., vegetation), pyrogenic (mainly biomass burning) and anthropogenic (e.g., industrial emissions and automobiles) sources (Carlier et al., 1986; Lee et al., 1997; Fu et al., 2007; Hak et al., 2005; Herndon et al., 2005; Smedt et al., 2010). The background levels of HCHO are mainly sustained by oxidation of long-lived VOCs, such as methane, whereas the spatial variability of HCHO is primarily associated with the oxidation of shorter lived, reactive non-methane VOCs of biogenic (e.g., isoprene) or anthropogenic (e.g., butane) origin. Being an oxidation product, HCHO is a useful indicator of biogenic and anthropogenic emissions of hydrocarbons (Andreae and Merlet, 2001; Geiger et al., 2002; Seco et al., 2007; Stavrakou et al., 2009; Barkley et al., 2013; Stavrakou et al., 2014). Photolysis and oxidation by OH radicals, yielding hydroperoxyl radicals (HO2) and carbon monoxide (CO) (hence affecting the global CO budget and the oxidative capacity of the atmosphere), is the main removal process for atmospheric HCHO. Dry and wet depositions are the other important removal processes of HCHO (Atkinson, 2000). Being a crucial participant in tropospheric O3 formation (thereby affecting air quality), the monitoring of the spatial and temporal variability of HCHO is essential (Abbot, 2003; De Smedt et al., 2008). Satellite observations of tropospheric HCHO columns have been successfully reported for over two decades (Chance et al., 2000; Palmer et al., 2001; Wittrock et al., 2006; De Smedt et al., 2008). These observations have helped us study the spatial and temporal trends and to estimate the emissions of VOCs (Gonzi et al., 2011; Barkley et al., 2013). Higher concentrations of HCHO associated with areas of dense vegetation and biomass burning indicate that on a global scale, the major sources are of biogenic origin (Vrekoussis et al., 2010).

O3 plays an important role in atmospheric chemistry (Crutzen, 1974, 1970; Logan et al., 1981; Barrie et al., 1988). Surface O3 is a major pollutant and a greenhouse gas affecting the global radiation budget (IPCC, 2007). In the troposphere, O3 acts as a secondary pollutant formed in the presence of sunlight and its precursors, e.g., NOx or VOCs (Fishman and Crutzen, 1978). Stratospheric subsidence is another source of tropospheric ozone (Holton et al., 1995). Ozone participates in the formation of OH, thus affecting the oxidising capacity of the atmosphere. Increased levels of tropospheric ozone have an adverse effect on human health (Desqueyroux et al., 2002) and crop yield (Morgan et al., 2006; Ghude et al., 2014). Increased anthropogenic emissions from fossil fuel combustion in power plants, large-scale industries and automobiles have led to an increase in the surface O3 concentrations over the past few decades (Sillman and Samson, 1995; Peleg et al., 1997; Ryerson et al., 2001). Solar radiation and higher water vapour content in the atmosphere, along with increased NOx and VOC concentrations, lead to higher photochemical production of O3 in the tropical regions (Crutzen, 1970; Andreae and Crutzen, 1997; Sánchez et al., 2005). NO2 participates as a catalyst and, in the presence of solar radiation, photochemically dissociates to form NO (nitrogen oxide) and O(3P).

 

The resultant oxygen atom then reacts with molecular oxygen to form O3. NO reacts with the HO2 radical and converts back into NO2, which continues the catalytic O3 production process.

 

O3 also photolyses at wavelengths < 310 nm to form O(1D), which then reacts with water vapour to yield OH radicals.

 

The oxidation of methane (and other NMVOCs) by OH radical forms HO2 and HCHO (higher aldehydes in the case of NMVOC oxidation). Further photochemical oxidation of formaldehyde with OH radicals also results in the formation of HO2 (R10)–(R12).

 

At night, in the absence of sunlight, O3 reacts with NO2 to form the nitrate radical (NO3), which can eventually lead to the removal of NOx from the atmosphere, as mentioned earlier.

 

The Indo-Gangetic Plain (IGP) region covers ~21% of the Indian subcontinent land area, accommodating ~40% of the Indian population (Nair et al., 2007). Coal-based thermal power plants (Prasad et al., 2006), coal-based small and medium industries, biomass burning and bio-fuel burning for domestic cooking (Reddy and Venkataraman, 2002) are the main sources for atmospheric pollutants in the IGP. Precise knowledge of NO2, HCHO and O3 sources and the monitoring of their abundance are very important for determining their exact role in atmospheric chemistry on a local, regional and global scale. Recent studies over India have studied the inter-annual variation of NO2 and HCHO using several years of satellite based data (De Smedt et al., 2008; Smedt et al., 2010; Ghude et al., 2013; Hilboll et al., 2013; Mahajan et al., 2015). However, scant information from ground based instruments is available in India to validate the satellite observations (Pandey et al., 1992; Sharma et al., 2010; Mandal et al., 2012; Reddy et al., 2012). Ground based observations of HCHO in India are rare (Khare et al., 1997a, b; Dutta et al., 2010), and further satellite validation is necessary, especially considering a model-satellite discrepancy (Mahajan et al., 2015). In a recent study, MAX-DOAS observations of NO2 and HCHO have been reported from the IGP region (Pantnagar; 29.03°N, 79.47°E) for the year 2017 (Hoque et al., 2018). The HCHO mixing ratios were found to be in the range of 2–4 ppbv for July and August 2017 and of 4–6 ppbv for September 2017. The NO2 mixing ratios were found to be between 0.5 and 1 ppbv for July and August 2017 and less than 0.5 ppbv in September 2017. Various groups have conducted studies over India and reported elevated levels of O3 during late autumn and winter, extending through May (Lal et al., 2000; Nair et al., 2002; Naja and Lal, 2002; Jain et al., 2005; Beig et al., 2007; Mittal et al., 2007; Ghude et al., 2008). Low altitude rural sites in India show maximum O3 concentrations during summer and winter and minimum concentrations during the Asian Summer Monsoon (ASM) season (Debaje and Kakade, 2006; Reddy et al., 2011). In another recent study, decadal changes of surface ozone were reported over ~40 years (1973–2014) from Thiruvananthapuram (8.542°N, 76.858°E) in peninsular India (Nair et al., 2018). The ozone mixing ratios were found in the range of 10–20 ppbv for the months of July, August and September over the 40 years of observations.

In this study, we report the retrieval of NO2 and HCHO using the Multi axis Differential Optical Absorption Spectroscopy (MAX-DOAS) technique along with surface O3 observations carried out during the Cloud Aerosol Interaction and Precipitation Enhancement Experiment (CAIPEEX-2014). We also investigate the effect of long-range transport of O3 precursors and ascertain the main drivers of O3 over a rural site in the IGP.


MEASUREMENT SITE AND METHODS



Measurement Site

The CAIPEEX-2014 campaign was carried out on the Rajiv Gandhi South Campus of Banaras Hindu University in Barkachha, Mirzapur District, Uttar Pradesh, 169 m above mean sea level (25.06°N, 82.59°E; Fig. 1). The site represents a rural location in the IGP, with extensive grasslands and cropland surrounding the measurement location. The site is approximately 300 km south of the Himalayan range. It is located 47 km south-west of the city of Varanasi and 8 km south of Mirzapur. Farther to the south, there are two large thermal power plants ~110 km from the site: Vindhyachal Thermal Power Station and Rihand Thermal Power Station (Fig. 1). A state highway passes through the university campus about ~100 m from the observation site on the western side. Towards the south, at a distance of ~750 m, is a marketplace, which attracts traffic during the daytime. The Majhawati River flows on the eastern side of the measurement location. The climate in this region is predominantly dry (sub-tropical to dry). The winter season is short (December–February), and the summer (March–June) is followed by the monsoon (July–September) and a brief autumn (October–November). The temperature peaks at ~48°C during the summer and drops to a low of ~4°C during December and January. The average annual rainfall at the site is 1059 mm, of which 90% is received during the ASM period. The onset of ASM in this region is usually during the third to fourth week in June and lasts up to the end of September. Meteorological parameters were measured (using an all in one weather sensor for temperature, RH and winds) and a Kipp and Zonen CNR4 net radiometer for radiation for the period of 23 July 2014 to 20 September 2014, with a data gap during 15 August 2014 to 23 August 2014 due to instrumental problems (Fig. 3).


Fig. 1. CAIPEEX-2014 location with nearby cities and thermal power plants indicated. The area around the measurement site was divided into five sectors. Sectors 1 and 2 represent the nearby town of Mirzapur (which, at a distance of 10 km, is closest to the measurement site) and the city of Varanasi (at a distance of 60 km). Sector 3 represents a vegetated area to the east of the measurement site. Sector 4 represents the region with the two thermal power plants, and Sector 5 represents the rural IGP region.Fig. 1. CAIPEEX-2014 location with nearby cities and thermal power plants indicated. The area around the measurement site was divided into five sectors. Sectors 1 and 2 represent the nearby town of Mirzapur (which, at a distance of 10 km, is closest to the measurement site) and the city of Varanasi (at a distance of 60 km). Sector 3 represents a vegetated area to the east of the measurement site. Sector 4 represents the region with the two thermal power plants, and Sector 5 represents the rural IGP region.


Ozone Measurements

An O3 analyser (EC9810; Ecotech) was used for surface O3 measurements. It is a non-dispersive ultraviolet photometer, which alternately switches between a selective ozone scrubber in and out of the measuring stream and computes the ratio of transmitted light, giving a measure of ozone concentration. The lower detection limit is 0.5 ppbv (equivalent to nmol mol1) or 0.2% of the concentration reading, whichever is greater, and the instrument has a precision of 1 ppbv or 1% of the reading, whichever is greater. The instrument was active from 23 July 2014 to 7 Jan 2015. There was no significant data gap aside from power failures at the station, which lasted for short periods.


MAX-DOAS Instrumental Setup

MAX-DOAS is a passive DOAS technique where scattered sunlight along multiple viewing directions are analysed and combined to get the distribution of various trace gases. As the differential absorption patterns of individual absorbers are unique, simultaneous measurements of different absorbers can be made, provided that the cross-interference from other absorbers and contribution from broadband scattering is eliminated. MAX-DOAS measurements at lower elevation angles are more sensitive towards lower tropospheric trace gas layers, as the photons travel longer paths through the lower troposphere compared to higher elevation angles. The MAX-DOAS instrument (Envimes) used for this campaign has two ultra-low stray light 75 mm Avantes spectrometers. The first spectrometer covers a range of 306.08–468.77 nm, and the second spectrometer operates in the range of 441.91–583.36 nm. Both the spectrometers have a full width, half maximum resolution of 0.6 nm and a 100 µm slit. The spectrometers are temperature stabilised (maintained at an average temperature of 20°C) with a deviation of < 0.05°C.

The MAX-DOAS instrument was installed on the rooftop of a water tower at the campaign site, at a height of ~10 m above ground level. The scanner unit pointed towards the geometric north, with a clear line of sight to the horizon. Spectra were recorded between 23 July 2014 to 20 September 2014, with a gap during 1–2 August 2014 and 17–25 August 2014 due to instrumental issues. Each full scan measured scattered sunlight along 10 different elevation angles (90°, 40°, 20°, 10°, 7°, 5°, 3°, 2°, 1° and 0.5°) when the solar zenith angle (SZA) was < 80°. For 80° < SZA < 97°, sunlight spectra were measured only in the zenith direction. The exposure time per individual spectrum was calculated according to 70% saturation of the charged coupled device (CCD) sensor. The total exposure per elevation angle was set to 60 seconds. The dark current, offset and calibration spectra were recorded at the end of every day and were used to correct the measured spectra. Nonlinearity of the spectrometers was also taken into account. All the results from the campaign are presented in local time (Indian Standard Time).


DOAS Analysis Settings

The measured spectra were pre-processed with Matlab® for dark current and offset correction and then analysed using the QDOAS spectral fitting software (Danckaert, 2014). Zenith spectra from each scan was taken as a reference to remove the stratospheric contribution in off-axis measurements (Hönninger et al., 2004).

HCHO differential slant column densities (DSCDs) were retrieved in the 332–358 nm wavelength window. Although HCHO shows characteristic absorption in the ultraviolet spectral region of 240–320 nm, the abovementioned wavelength range was chosen to avoid interference from strong ozone absorption bands below 320 nm. For O4 and NO2, the 350–386 nm and 433–460 nm intervals were chosen. The details of the cross-sections used in QDOAS retrieval are mentioned in Table 1. For all three fitting windows, a third-order polynomial and an offset with a zero order polynomial was also fitted. A ring spectrum (Grainger and Ring, 1962) was fitted (Chance and Kurucz, 2010, 250°K) in addition to the fourth power ring spectrum following Wagner et al. (2009). Examples of the fits for O4, NO2 and HCHO are shown in Fig. 2.


Table 1. MAX-DOAS retrieval settings for the different species retrieved during the campaign.


Fig. 2. DOAS fit for O4, NO2 and HCHO. Top panel: O4; 30 August 2014, 08:03:43; SZA = 58.4°; elevation angle = 10.0°; DSCD = 2.32 × 1043 molecules2 cm−5; RMS = 3.4 × 10−4. Middle panel: HCHO; 30 August 2014, 08:27:38; SZA = 52.9°; elevation angle = 3.0°; DSCD = 4.08 × 1016 molecules cm−2; RMS = 4.0 × 10−4. Bottom panel: NO2; 30 August 2014, 08:09:56; SZA = 56.9°; elevation angle = 0.5°; DSCD = 1.53 × 1016 molecules cm−2; RMS = 5.9 × 10−4.Fig. 2. DOAS fit for O4, NO2 and HCHO. Top panel: O4; 30 August 2014, 08:03:43; SZA = 58.4°; elevation angle = 10.0°; DSCD = 2.32 × 1043 molecules2 cm−5; RMS = 3.4 × 10−4. Middle panel: HCHO; 30 August 2014, 08:27:38; SZA = 52.9°; elevation angle = 3.0°; DSCD = 4.08 × 1016 molecules cm−2; RMS = 4.0 × 10−4. Bottom panel: NO2; 30 August 2014, 08:09:56; SZA = 56.9°; elevation angle = 0.5°; DSCD = 1.53 × 1016 molecules cm−2; RMS = 5.9 × 10−4.

Boundary layer volume mixing ratios (vmr) for NO2 and HCHO were retrieved from the DSCDs using the O4 DSCDs. DSCDs from elevation angles < 3° were used, as the DSCDs from higher elevation angles contain information from the free troposphere. Only scans with SZA < 60° were taken into account. Standard atmospheric temperature, pressure and O4 concentration profiles were used for the calculation of the path length. The trace gas mixing ratios were then calculated over the path towards the north per the methodology used by other groups in the past (Sinreich et al., 2010; Mahajan et al., 2012; Gómez Martín et al., 2013; Prados-Roman et al., 2015). 


RESULTS AND DISCUSSIONS



Meteorology

Fig. 3 shows the time series for various meteorological parameters measured during the campaign. The left column shows the temperature, relative humidity and wind speed (top to bottom), and the right column shows the atmospheric pressure, incoming solar radiation and wind direction (top to bottom). There was a gap in the data from the late  afternoon of 14 August to the morning of 24 August due to instrumental issues. The average atmospheric temperature was 28.6°C with a minimum and maximum temperature of 24°C and 36.7°C, respectively. The average atmospheric pressure was 982.6 hPa with a minimum and maximum pressure of 973.6 hPa and 987.6 hPa, respectively. The average relative humidity was 78.2% with a minimum and maximum relative humidity of 39.4% and 94.5%, respectively. The average incoming solar radiation was 192.2 Wm−2. There were several cloud free days, and even on days with clouds, several cloud free hours were recognised using the spectrometer data along with a sky imager, and these data were used to retrieve mixing ratios from the DOAS analysis. The average wind speed was 2.1 m s−1 with a minimum and maximum wind speed of 0.17 m s−1 and 6.86 m s−1.


Fig. 3. Time series of various meteorological parameters measured during CAIPEEX-2014. The left column represents temperature, relative humidity and wind speed from top to bottom. The right column represents air pressure, incoming solar radiation and wind direction from top to bottom.Fig. 3. Time series of various meteorological parameters measured during CAIPEEX-2014. The left column represents temperature, relative humidity and wind speed from top to bottom. The right column represents air pressure, incoming solar radiation and wind direction from top to bottom.


O4

O4 DSCDs were found to be higher at lower elevation angles, as expected. This is due to the fact that the intensity of O4 absorption is proportional to the square of the oxygen pressure. At lower elevation angles, photons travel longer paths in the lower troposphere and interact more with tropospheric absorbing species before reaching the instrument. There is a decrease in the O4 DSCD at the lowest three angles (2°, 1° and 0.5°), indicating the presence of aerosols in the boundary layer. This is in accordance with the fact that the campaign happened during the ASM season. The average RMS and detection limit for O4 DSCDs were 7 × 10−4 and 3.1 × 1042 molecules2 cm−5, respectively, at the 1° elevation angle. The average O4 DSCD at a 1° elevation angle was 1.4 × 1043 molecules2 cm−5 with a maximum DSCD of 2.5 × 1043 molecules2 cm−5 (Fig. 4). The O4 DSCDs were then used to estimate the path length and hence the trace gas mixing ratios, as described earlier.


Fig. 4. Slant column densities of O4, HCHO and NO2 from CAIPEEX-2014 campaign. Different colours represent measurements at different viewing elevation angles.Fig. 4. Slant column densities of O4, HCHO and NO2 from CAIPEEX-2014 campaign. Different colours represent measurements at different viewing elevation angles.


NO2

NO2 DSCDs were also found to be higher at lower elevation angles (Fig. 4). This, in conjunction with the O4 DSCDs, indicates that most of the NO2 is present close to the surface with a decreasing gradient with altitude. For the three lowest elevation angles, the DSCDs do not increase with a decrease in elevation angle, which can be attributed to the presence of aerosols in the boundary layer, as indicated by the decreasing O4 DSCDs. The average RMS and detection limit for NO2 DSCDs were 6 × 10−4 and 3 × 1015 molecules cm−2 at the 1° elevation angle. The average NO2 DSCD at the 1° elevation angle was 1.5 × 1016 molecules cm−2, with a maximum DSCD of 7.2 × 1016 molecules cm−2 (Fig. 4, bottom panel).

Using the method described earlier, NO2 mixing ratios were estimated for elevation angels < 3° to get the contribution to NO2 within the boundary layer near the surface (e.g., Wagner et al., 2004). The resulting time series for NO2 is shown in Fig. 5. The average NO2 mixing ratio was 0.81 ± 0.20 ppbv (range: 0.08–6.06 ppbv). The average detection limit for NO2 was 0.16 ppbv. NO2 mixing ratios decreased from an early morning high until mid-day and increased again later in the day (Fig. 6). This can be attributed to the daytime photochemical decomposition of NO2 by solar radiation as discussed in the introduction (R1).


Fig. 5. Time series for NO2, HCHO and O3 vmr. The top panel shows NO2 (blue dots) and HCHO (red dots) mixing ratios. The bottom panel shows daytime (blue dots) and nighttime (red dots) O3 mixing ratios. Fig. 5. Time series for NO2, HCHO and O3 vmr. The top panel shows NO2 (blue dots) and HCHO (red dots) mixing ratios. The bottom panel shows daytime (blue dots) and nighttime (red dots) O3 mixing ratios.


Fig. 6. Diurnal variation in NO2, HCHO and O3 mixing ratios during CAIPEEX-2014. The error bars represent the standard deviations.Fig. 6. Diurnal variation in NO2, HCHO and O3 mixing ratios during CAIPEEX-2014. The error bars represent the standard deviations.

The average vertical column densities (VCDs) for NO2, as observed by the satellite-based ozone monitoring instrument (OMI), were studied to understand the spatial distribution over the IGP region (Fig. 7). The annually averaged satellite based NO2 VCD for 2014 was found to be 2.5 × 1015 molecules cm−2 around the observation site, with the highest NO2 VCD (3.4 × 1015 molecules cm−2) found in the month of December. The lowest NO2 VCD (1.9 × 1015 molecules cm−2) was observed in the month of February. During the months of July, August and September 2014, higher NO2 VCDs (~8 × 1015 molecules cm−2) were observed over the two thermal power plants situated to the south of the measurement site than over the measurement site (~2.2 × 1015 molecules cm−2). To investigate the effect of long-range transport from the thermal power plants on the observed NO2 at the measurement site, we computed the 12-hour and 24-hour back trajectories reaching the site every hour using the HYbrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model (Draxler and Hess, 1998). The area around the measurement site was divided into five sectors; with geometric north being 0°, the sectors were: Sector 1: 305–35°; Sector 2: 35–75°; Sector 3: 75–125°; Sector 4: 125–185° and Sector 5: 185–305° (Fig. 1). Sectors 1 and 2 represent the nearby town of Mirzapur (which is closest to the measurement site, at a distance of 10 km) and the city of Varanasi (at a distance of 60 km) (Fig. 1). Sector 3 represents a vegetated area to the east of the measurement site. Sector 4 represents the region with the two thermal power plants, and Sector 5 represents the rural IGP region. For each of the 12-/24-h back trajectories, information about where (sector-wise) the air parcel spends at least 70% of its total time before arriving at the measurement site was extracted. The box-and-whisker plot in Fig. 8 (left column) shows the sector-wise contribution to the observed NO2, where the air parcel spends at least 70% of its time in a particular sector (the top and bottom panels correspond the 12-h and 24-h back trajectories, respectively). The median for the corresponding sector along with the 75th and 25th percentile values are indicated in the box. The whiskers correspond to the 2.7 sigma (99.3%) when the data are normally distributed. The median NO2 value for air parcels passing over Sector 4 was the highest (1 ppbv and 1.44 ppbv for 12-h and 24-h back trajectories), which indicates the transport of NO2 from the power plants to the CAIPEEX-2014 site. Sector 5 and Sector 3 show the lowest median values (0.51 ppbv and 0.51 ppbv) for the 12-h and 24-h back trajectories, respectively. This is expected, considering that there are no large sources of NOx in these two sectors.


Fig. 7. Satellite-based (OMI) vertical columns of NO2 and HCHO for the months of July, August and September 2014. (A) HCHO over India. (B) HCHO over the CAIPEEX-2014 site. (C) NO2 over India. (D) NO2 contour over the CAIPEEX-2014 site. (E) HCHO/NO2 ratio over India. (F) HCHO/NO2 ratio over the CAIPEEX-2014 site. “o” represents the city of Varanasi, and “+” indicates the thermal power plants.Fig. 7. Satellite-based (OMI) vertical columns of NO2 and HCHO for the months of July, August and September 2014. (A) HCHO over India. (B) HCHO over the CAIPEEX-2014 site. (C) NO2 over India. (D) NO2 contour over the CAIPEEX-2014 site. (E) HCHO/NO2 ratio over India. (F) HCHO/NO2 ratio over the CAIPEEX-2014 site. “o” represents the city of Varanasi, and “+” indicates the thermal power plants.


Fig. 8. Box-and-whisker plot of NO2, HCHO and O3 mixing ratios and the number of the sector where the air parcel has spent more than 70% of its time in the last 12/24 hours. All the top panels correspond to the 12-h back trajectory data, whereas the lower panels represent the 24-h back trajectory data.Fig. 8. Box-and-whisker plot of NO2, HCHO and O3 mixing ratios and the number of the sector where the air parcel has spent more than 70% of its time in the last 12/24 hours. All the top panels correspond to the 12-h back trajectory data, whereas the lower panels represent the 24-h back trajectory data.

All the sectors were found to have outliers beyond the upper 2.7 sigma limit. For the 24-h back trajectories, Sectors 1, 2, 3, 4 and 5 had 9.2%, 0.8%, 6.8%, 2.9% and 5.6% outliers, respectively. For the 12-h back trajectories, Sectors 1, 2, 3, 4 and 5 had 8.1%, 3.4%, 7.8%, 0.4% and 6% outliers, respectively. The probable sources of the outliers were (1) contamination of the air parcels with air parcels coming from Sector 4 (associated with high NO2 from the power plants) and (2) local sources originating from automobiles, as there was a road close to the measurement site. To test the first hypothesis, air parcels which travelled over the power plants, even though their residence time was larger in other sectors, were identified. The reasoning behind this was that if the power plants were the source of outliers, then these air parcels would contain more outliers.

To identify the effect of power plant emissions on NO2 outliers, we check whether the air parcels from the power plants have contaminated the air parcels which have travelled predominantly over other sectors. We identified the region over the power plants, which shows higher NO2 VCDs (> 6 × 1015 molecules cm−2) in the satellite observations. We found that a box within latitudes 23.5°–24.5° and longitudes 82.5°–83.5° contained the highest NO2 VCDs. We identified air parcels that have travelled through this box in the past 12 or 24 hours. Fig. 9 shows all the NO2 data (blue dots) along with the outlier NO2 data (red dots). Data points which correspond to air parcels passing through the box are represented with black dots. Outliers that correspond to air parcels passing through the box are represented with green dots. The top and bottom panels represent 12-h and 24-h back trajectory plots. Out of 48 days of observation, 35 days contained data points which were identified as outliers in the 12-h back trajectories (480 total outlier data points out of 7412 data points). For 24-h back trajectories, 31 days contained outlier data points in the different sectors (465 total outlier data points out of 7106 data points). For 12-h back trajectories, on 8 days, air parcels passed through the box containing high NO2 VCDs (492 data points), and out of these 8 days, only 3 days (43 data points) contained outlier data points. For 24-h back trajectories, 6 days contained air parcels that passed through the box containing high NO2 VCDs (255 data points), of which 2 days (15 data points) had outlier data points. This leads us to conclude that contamination of air parcels coming from power plants do not explain the outlier NO2 data points. Hence, we conclude that the outliers are not caused by long-range transport but rather by local emissions.


Fig. 9. Time series of NO2 and NO2 outliers from different sectors. The blue dots represent all NO2 data; the red dots represent NO2 outliers from different sectors; the black dots represent NO2 from parcels corresponding to the power plant region; the green dots represent NO2 outliers affected by emissions from the power plant region. The top and bottom panels represent 12-h and 24-h back trajectory plots.Fig. 9. Time series of NO2 and NO2 outliers from different sectors. The blue dots represent all NO2 data; the red dots represent NO2 outliers from different sectors; the black dots represent NO2 from parcels corresponding to the power plant region; the green dots represent NO2 outliers affected by emissions from the power plant region. The top and bottom panels represent 12-h and 24-h back trajectory plots.

The reason behind the fact that Sector 4 contains the fewest outliers is that the median and sigma values (2.7 sigma values limiting the whiskers) are higher in Sector 4 compared to other sections due to higher emissions from the power plants. Most elevated NO2 values caused by the local effect are within the 2.7 sigma range. It should be noted that for Sector 2, the lack of outliers could also be a result of sampling bias, as during the ASM season, the dominant wind pattern is south-westerly. However, for other sectors, the median and sigma values (2.7 sigma values limiting the whiskers) are lower, and most elevated NO2 values caused by local emissions are observed as outliers. It is thus highly probable that the main cause behind the presence of large outliers is local emissions from automobiles on the highway to the south or the nearby marketplace mentioned earlier. 


HCHO

Similar to O4 and NO2, the HCHO DSCDs were found to have lower values for higher elevation angles, except the three lowest elevation angles of 2°, 1° and 0.5°, due to the presence of aerosols in the boundary layer. This indicates that most of the HCHO is present near the surface with a decreasing gradient upwards, although the decrease was not as strong as that for NO2 (Fig. 4). The average RMS and detection limit for HCHO DSCDs were 4.1 × 10−4 and 1.06 × 1015 molecules cm−2, respectively, at the 1° elevation angle. The average HCHO DSCD at the 1° elevation angle was 3.56 × 1016 molecules cm−2 with a maximum DSCD of 8.06 × 1016 molecules cm−2 (Fig. 4).

The average HCHO mixing ratio was found to be 1.93 ± 0.60 ppbv, with values ranging between 0.32 ppbv and 8.81 ppbv (Fig. 5). The diurnal variation of HCHO is unlike NO2; HCHO was found to increase through the morning till 11:00 and then gradually decrease towards late evening (Fig. 6). This can be attributed to daytime photochemical oxidation of VOCs to form HCHO (R6)–(R9). Photochemical oxidation is also the major sink process for HCHO (R10)–(R11), which is why after an increase in the morning hours, HCHO starts to reduce as destruction starts to dominate production.

The annually averaged satellite-based HCHO VCD for 2014 was 11.2 × 1015 molecules cm−2 around the observation site, with the highest HCHO VCD (15.6 × 1015 molecules cm−2) found in the month of November. The lowest HCHO VCD (6.6 × 1015 molecules cm−2) was observed in the month of September. Satellite retrieved HCHO VCDs averaged for the months of July, August and September of 2014 were found to be within 8–12 × 1015 molecules cm−2 around the observation site (Fig. 7) without any larger spatial differences, making it more homogeneous compared to NO2. The box-and-whisker plot for HCHO (Fig. 8, middle column) with 12-h back trajectories shows that Sector 1 had the highest median value (2.34 ppbv) and Sector 5 displayed the lowest median value (1.5 ppbv). For the 24-h back trajectory plot, Sector 1 has the highest (2.29 ppbv) and Sector 5 has the lowest (1.53 ppbv) median value. Compared to NO2, the HCHO does not show significant differences between different sectors. This can also be seen in the satellite observations (Fig. 7). Unlike NO2, which has a significant source in Sector 4, there is no similarly strong “point” source for HCHO in any sector. The numbers of outliers are also lower for HCHO compared to NO2. For 24-h back trajectories, Sectors 1, 2, 3, 4 and 5 had 2.74%, 0.0%, 3.92%, 0.0% and 5.47% outliers, respectively. For 12-h back trajectories, Sectors 1, 2, 3, 4 and 5 had 3.58%, 2.78%, 4.63%, 1.03% and 3.32% outliers, respectively. The source for HCHO outliers also seems to be local anthropogenic emissions, which include biomass burning and emissions from automobiles. 


Ozone

For this study, we analysed O3 data for the period of 23 July 2014 to 20 September 2014, which coincides with the MAX-DOAS observations. Being located near a highway, O3 mixing ratios were observed to be affected by emissions from vehicles. To discard effects from nearby emissions, ozone data was filtered for spikes from periods when vehicles passed close to the site. The filtered data was averaged every 10 minutes to match the MAX-DOAS observations. Fig. 6 shows the time-series for O3 during the CAIPEEX-2014 campaign.

The blue points in the plot represent the ozone for SZA < 90°, i.e., daytime ozone. The red points represent data for SZA > 90°, i.e., nighttime ozone. The average O3 mixing ratio during daytime (solar zenith angle < 90°) was 30 ± 13 ppbv (range: 2.7–81.9 ppbv), and during the nighttime (solar zenith angle > 90°), the mean was 22.5 ± 10.2 ppbv (range: 1–63 ppbv). The right hand panel in Fig. 6 shows the hourly diurnal variation of O3 with the corresponding standard deviation. Observed O3 levels were at the lowest during the early morning (06:00–07:00) and increased during the daytime due to photochemical production. The O3 mixing ratios reached a peak around 12:00 and remained elevated until 16:00. In the presence of solar radiation, NO2 photochemically dissociates to NO and O(3P) and results in the formation of O3 per reactions described in the introduction. During midday, ozone concentrations reach an equilibrium, which continues until the late afternoon. During the evening, there is a gradual decrease due to the lack of photochemical production, which continues after sunset. At night, O3 oxidises olefins, NO, NO2 etc.; this contributes to a decrease in the O3 concentrations. The diurnal trend for ozone (Fig. 6) matches with the previously observed pattern at sub-urban sites in India (Jain et al., 2005; Beig et al., 2007; Reddy et al., 2011; Ojha et al., 2012).

Fig. 8 represents a box-and-whisker plot for daytime (SZA < 90°) O3 from corresponding sectors as discussed above (top right plot is for 12-h back trajectories, and bottom right plot is for 24-h back trajectories). For both 12-h and 24-h back trajectories, Sectors 1 and 2 have the highest and lowest median values (Sector 1 with 40.7 ppbv and 38.5 ppbv for 12-h and 24-h back trajectories, respectively; Sector 2 with 22.1 ppbv and 10.7 ppbv for 12-h and 24-h back trajectories, respectively).

It is well known that at low NOx concentrations, O3 formation is linearly proportional to NOx concentrations and is independent of VOC concentration (Chameides et al., 1992), whereas in regions with lower VOC concentrations, ozone production is inversely proportional to NOx but linearly proportional to VOCs. Chameides et al. (1992) reported that HCHO/NO2 ratio can be indicative of surface ozone sensitivity towards NOx limited and VOC limited regimes. Using satellite based observation, Martin et al. (2004) reported that HCHO/NO2 ratio < 1 is an indication of VOC limited regime and > 1 is an indication of NOx limited regime for ozone formation. In a recent study, Schroeder et al. (2017) further studied the critical ratio for VOC and NOx limited regimes. They found that HCHO/NO2 column ratio from 1.1 to 4.3 cannot be reliably classified as either NOx or VOC limited regime, depending upon regional variability. From satellite data, the HCHO/NO2 column ratio was found to be > 4 over the CAIPEEX-2014 site (bottom left and bottom right panels in Fig. 4). The average HCHO/NO2 from the ground based instruments was found to be > 2. Both the results indicate that the CAIPEEX-2014 site is most likely an NOx limited region; hence, the ozone formation should be dependent on the NOx concentrations (Martin et al., 2004), although the ratio is within the “uncertain range”. We studied the correlation between hourly averaged HCHO and ozone mixing ratios, and a positive correlation (R = 0.65, p < 0.001) was observed over the observation period. The hourly averaged NO2 and ozone mixing ratio displayed a slight negative correlation (R = −0.2, p = 0.005). The daily averaged HCHO and ozone mixing ratios also yielded a positive correlation (R = 0.78, p < 0.001), while the correlation between daily averaged NO2 and ozone mixing ratios was insignificant. Hourly averaged HCHO and NO2 mixing ratio did not show any significant correlation, suggesting different sources. The correlational analysis suggests that the ozone mixing ratios are more dependent on the HCHO mixing ratios, contrary to what is expected from the observed HCHO/NO2 ratio. However, on close inspection of variations within short periods, it is found that ozone resembles neither the variation of HCHO nor NO2. As mentioned above, the O3 back trajectories indicate higher daytime O3 mixing ratios associated with air masses coming from Sector 1, whereas NO2 is higher in air masses from Sector 4. This suggests that O3 formation at the CAIPEEX-2014 site was more affected by local production than long-range transport from the power plants, during which elevated NO2 was observed. The city of Mirzapur (Sector 1), being closer to the measurement site compared to the city of Varanasi, shows an effect on the observed O3 concentrations, as can be seen from the sectorial analysis. 


CONCLUSIONS


In this study, we reported the NO2 and HCHO concentrations, obtained via MAX-DOAS technique, as well as the surface O3 concentrations measured during the ASM season as part of the Cloud Aerosol Interaction and Precipitation Enhancement Experiment (CAIPEEX-2014). We discussed the effect of long-range transport on the NO2 and HCHO mixing ratios and on O3 production during the campaign. The NO2 mixing ratios ranged from 0.08 ppbv to 6.06 ppbv with an average of 0.81 ± 0.20 ppbv. Observations indicated that most of the NO2 lay near the surface with a decreasing gradient upward. The NO2 mixing ratios decreased from early in the morning till mid-day and then increased during the afternoon. Through satellite observation, an NO2 emission hotspot was identified ~110 km to the south of the CAIPEEX-2014 site, where two large thermal power plants are located, and air parcels travelling from the direction of these power plants exhibited higher NO2 mixing ratios. Local emissions (from automobiles on a highway near the campaign site and from a nearby marketplace) also contributed to the high NO2 mixing ratios. The HCHO mixing ratios ranged from 0.32 ppbv to 8.81 ppbv with an average of 1.93 ± 0.60 ppbv. Due to the daytime photochemical oxidation of VOCs, which resulted in the formation of HCHO, the HCHO increased during the morning until 11:00. Satellite observations indicated that the concentrations of HCHO around the campaign site were almost homogeneous compared to those of NO2. The average O3 mixing ratio observed during the daytime and the nighttime was 30 ± 13 ppbv (range: 2.7–81.9 ppbv) and 22.5 ± 10.2 ppbv (range: 1–63 ppbv), respectively. The O3 mixing ratios were low in the early morning (06:00–07:00) but increased during the daytime due to photochemical production, reaching their maximum values around 12:00. Following this peak, they remained steady until 16:00, after which they decreased through the evening. Furthermore, our results suggested that the background surface O3 mixing ratio depended on the HCHO mixing ratio. Based on the sectorial analysis, surface O3 concentrations at the CAIPEEX-2014 site were contributed by local emissions rather than long-range-transported precursors. 


ACKNOWLEDGEMENTS


The Indian Institute of Tropical Meteorology is funded by the Ministry of Earth Sciences, Government of India. We thank all the participants of the CAIPEEX-2014 campaign for support during the measurement period.



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