Skip to main content
NCBI home page
Heliyon logoLink to Heliyon
. 2021 May 7;7(5):e06973. doi: 10.1016/j.heliyon.2021.e06973

Impact of crop residue burning in Haryana on the air quality of Delhi, India

Pallavi Saxena a,∗, Saurabh Sonwani b, Ananya Srivastava c, Madhavi Jain d, Anju Srivastava e, Akash Bharti f, Deepali Rangra g, Nancy Mongia g, Shweta Tejan e, Shreshtha Bhardwaj g
PMCID: PMC8120930  PMID: 34027176

Abstract

Crop residue burning (CRB) over northern India is a major air quality and human health issue. The present study assesses the impact of PM10, PM2.5, NO2 and SO2, emitted during CRB activities in Haryana on the air quality of Delhi. The transition from pre-burning to burning period, in both rabi and kharif seasons, shows considerable increase in pollutant concentrations. PM10 and PM2.5 concentrations exceeded NAAQS limits by 2–3 times, while NO2 and SO2 stayed within the limits. MODIS fire observations used to estimate CRB fire counts (confidence ≥80%) shows that rabi (burning period) fires in Haryana are ~3 times higher and more intense than in kharif. Furthermore, backward trajectories shows air mass movement from Haryana, Punjab and Pakistan. Thus, pollutants emitted reach Delhi via air masses, deteriorating its air quality.

Meteorological conditions influence pollutant concentrations during both seasons. Frequent dust storms in rabi, and Dusshera and Diwali firework celebrations in kharif season exacerbate air pollution. In rabi, PM10 and PM2.5 have a significant negative association with (relative humidity) RH and positive association with (air temperature) AT. High AT during pre-monsoon, accompanied by low RH, loosens up soil particles and they can easily disperse. Stronger winds in rabi season promote NO2 and SO2 dispersion. In kharif, lower AT, higher RH and slower winds exist. Both PM10 and PM2.5 have a negative association with AT and (wind speed) WS. With lower temperature and slower winds during winter, pollutants are trapped within the boundary layer and are unable to disperse. As expected, NO2 has a significant negative association with AT in Haryana. However, in case of Delhi, the association is significant but positive, and could be due to the odd-even scheme imposed by the Delhi government. More research is needed to determine the health effects of Haryana's rabi CRB activities on Delhi.

Keywords: Biomass burning, Air mass movement, Rabi, Kharif, Meteorology

Biomass burning; Air mass movement; Rabi; Kharif; Meteorology.

1. Introduction

Biomass burning is an important source of atmospheric particulate matter (PM) and trace gases, which has a significant impact on local and global climate, in addition to causing severe health risks for humans (Zhang et al., 2011; Wang et al., 2013). Biomass burning includes various types of forest fires and agricultural burning after crop harvest, typically known as crop residue burning (CRB) (Singh et al., 2010; Rana et al., 2019). At a global scale, as much as 90% of wildfires are attributed to CRB and forest removal activities, are mostly restricted to Amazonia, Africa and Asia (Prabhu et al., 2020). The remaining 10% is largely due to fires in wild forests and grasslands (Rana et al., 2019). Moreover, biomass burning, particularly, during and after crop harvest, emits massive quantities of gaseous and particulate pollutants, which often increases pollution at local and regional scales (Crutzen and Andreae, 1990; Langmann et al., 2009; Wang et al., 2013; Tsay et al., 2016; Jethva et al., 2019). The severity of pollution is greatly dependant on the amount of biomass burnt, air mass transport, wind direction and the distance from CRB source (Witham and Manning, 2007; Targino et al., 2013).

In developing countries, quickly clearing arable lands via CRB is a general practice adopted by farmers to directly increase the yield. In Asia, forest fires (45%), CRB (34%) and grassland fires (20%) contribute the most in terms of burning activities (Mittal et al., 2009; Tang et al., 2013). Furthermore, CRB generates a large number of air pollutants such as oxides of nitrogen (NOx), sulphur dioxide (SO2), carbon monoxide (CO), volatile organic compounds (VOCs) and PM, which alters the air quality and affects various atmospheric feedback processes (Badarinath et al., 2009a, 2009b; Alexaki et al., 2019). Several other studies report increased concentrations of tropospheric ozone (O3), CO and aerosols due to CRB over various parts of Central Africa, South America and some parts of Asia due to long-range transport effect (Arola et al., 2007; Dumka et al., 2019). Besides, the direct adverse short and long-term impacts on the environment, poor air quality due to CRB presents a serious risk to human health. A recent study estimated that people residing in Punjab, an agriculturally dominant state of India, spent over USD 1 million annually to cover for CRB-related pollution exposure illnesses (Alexaki et al., 2019).

India is one of the largest agro-based economies in the world, producing the highest rice and wheat yield. As such, crop cultivation and harvesting-related activities are conducted throughout the year. The ever increasing production of agro based products consequently generates more waste and environmental pollution (Chang and Song, 2010; Dumka et al., 2019). However, a rapidly growing demand for food translates into a constant ramping up of yield production, which increasingly forces farmers to burn fields after harvest. India is ranked as the second-highest CRB contributor (84 Tg/year) in the world (Grover and Chaudhry, 2019). Additionally, in India (as well as Haryana), rice and wheat are sown during June–July and November–December respectively, and then harvested in the coming months of October–November and April–May, respectively (Gadde et al., 2009). In practice, this leaves farmers with a short time window, typically 3–4 weeks, to switch to the next season. Moreover, there are no economical technologies available for small fields that can collect leftover agricultural residues (Cheng et al., 2014; He et al., 2015). Therefore, the farmers prefer in-situ CRB, a practice that is less time-consuming, inexpensive and quickly prepares the field for the next crop (Venkataraman et al., 2006).

The total amount of agricultural waste generated annually in India is much greater than that in other countries. According to the Ministry of New and Renewable Energy (MNRE) estimates, an average of 500 Mt of crop residue is generated in India each year, a majority portion of which is used as fuel for industrial and domestic purposes (Bhuvaneshwari et al., 2019). Concurrently, Streets et al. (2003) identify Punjab (~20 Mt), Haryana (~10 Mt) and Uttar Pradesh (~11 Mt) as the Indian states with the largest crop burning volume. These states form a part of the Indo-Gangetic Plains (IGP), over which the air quality in recent years has severely deteriorated due to CRB (Badarinath et al., 2006; Jain et al., 2014; Kaskaoutis et al., 2014; Jethva et al., 2018, 2019). In 2016, an overlapping episode of seasonal CRB and the wide-scale firecracker burning during Diwali resulted in poor air quality over Delhi and its adjoining areas (Chauhan and Singh, 2017). The study also highlights that, as a consequence, haze and smog prevailed during most days of October. In addition, Kaskaoutis et al. (2014) estimate that paddy residue generated in India is ~97 Tg/year, of which ~24% is commonly burnt in fields as surplus. However, Punjab and Haryana alone contribute about half of the paddy straw surplus generated per year (Gadde et al., 2009). Lohan et al. (2018) note that in Haryana, rabi CRB residue was nearly thrice as compared to kharif CRB. Additionally, Yadav et al. (2014) report that rabi CRB area in Haryana was also higher as compared to kharif CRB area. Besides Haryana, Punjab is one of the most widely studied contributor of CRB in northern India.

Interestingly, some studies have shown that rabi CRB is higher than kharif CRB (Yadav et al., 2014; Grover and Chaudhry, 2019). Unlike Punjab, in Haryana rabi CRB poses a much serious issue than kharif CRB (Lohan et al., 2018). Moreover, low soil moisture and high air temperature also contribute to increased fires and enormous loss of crops during rabi harvesting. Farmers in Haryana also reported that machinery sparks generated during threshing, are also responsible for biomass fires (Jitendra et al., 2017). Scientific research on the degradation of air quality over Delhi due to prevalent CRB activities in the neighbouring state of Haryana, particularly in rabi season, is still quite sparse. Most previous studies have focussed on the issue of CRB activities in Punjab, particularly in kharif season, in degrading the air quality of northern India. But these studies fail to discuss the role of rabi CRB in Haryana on degrading the air quality of Delhi. Delhi is of focus due to the fact that it is the national capital of India, has a population of over 16 million (2011 census), and remains one of the most polluted cities in the world.

Therefore, the present study seeks to (1) quantify CRB activities through fire count analysis in Haryana, (2) identify air mass movements through backward trajectories to Delhi from source locations, and (3) assess the variations in PM10, PM2.5, NO2 and SO2 concentrations, during pre-burning and burning periods of rabi and kharif season, in Haryana and Delhi. Overall, this study highlights the role of Haryana's CRB activities, particularly during rabi season, in deteriorating the air quality of Delhi.

2. Study area

2.1. Delhi

Apart from being India's national capital, Delhi is the second most populous city in the world (Mahato et al., 2020). According to the latest census report, the metropolis had a population of 16.8 million with an annual growth rate of 21%. It occupies an area of 1483 km2, and is located at an elevation of 220 m.a.s.l. (Saxena et al., 2020a, b). Delhi experiences a semi-arid climate with four main seasons: pre-monsoon (Mar–May), monsoon (Jun–Sept), post-monsoon (Oct–Nov) and winter (Dec–Feb). The temperature varies between 4 °C and 10 °C during winter and 42 °C and 48 °C during pre-monsoon (Saxena et al., 2019). Over 80% of yearly rainfall occurs during monsoon (Perrino et al., 2011; Tiwari et al., 2013; Sonwani and Kulshrestha, 2019). Two representative sites of Delhi were chosen for the study to recognise the impact of CRB from Haryana. The locations of these sites are shown in Figure 1.

Figure 1.

Figure 1

Open in a new tab

Geographical location of (a) the states of Haryana and Delhi (shaded) within the Indian boundary and (b) selected study sites within Haryana, viz. Hisar (HSR) and Karnal (KAR), as well as Delhi, viz. Ramakrishna Puram (RKP) and Indira Gandhi International (IGI) Airport.

2.1.1. Ramakrishna Puram (RKP)

RKP (latitude: 28.56° N; longitude: 77.17° E) is one of the largest housing colonies in Delhi. It is surrounded by around 10 schools and 7 adjoining marketplaces. It is covered by the ring road to the north, outer ring road to the south with moderate traffic density.

2.1.2. Indira Gandhi International (IGI) airport

IGI Airport (latitude: 28.55° N, longitude: 77.10° E) is one of the largest and busiest sites in Delhi. It is situated at a distance of 3 km from the national highway (NH 8) in the southeast and Gurugram in the southwest. This site is a very heavy traffic zone, where low floor buses, taxis, personal cars, three-wheelers and heavy duty trucks are common. There are no industries in the vicinity of the airport.

2.2. Haryana

Haryana is a state in northern India located at 29.05° N, 76.08° E and approximately 210–275 m.a.s.l. The state has an administrative area of 44,000 km2. The population is about 25.4 million (Grover and Chaudhry, 2019). The temperature ranges from 45 °C to 47 °C in pre-monsoon and 2 °C to 5 °C in winter. Like Delhi, Haryana also experiences ~80% of its rain during June–August. Haryana is also one of the major producers of rice and wheat in the country (Grover and Chaudhry, 2019). Two representative sites of Haryana were chosen, namely Hisar and Karnal (Figure 1), as they are one of the main wheat/rice CRB regions in Haryana.

2.2.1. Hisar (HSR)

Hisar, a suburban city of Haryana is situated at 29.14° N latitude and 75.72° E longitude. It has a mean temperature of 40 °C and 10 °C in pre-monsoon and winter, respectively. It receives a mean annual rainfall of 450 mm. Frequent dust storms are a notable feature of pre-monsoon (Kaushik et al., 2012). Agricultural fields are located ~5 km from its centre. During harvesting, CRB is a regular practice in and around this area.

2.2.2. Karnal (KAR)

Karnal, another suburban city of Haryana is situated at 29.68° N latitude and 76.99° E longitude and has a geographical area of 2520 km2. It has a mean annual rainfall of 544.5 mm (SAH, 2017). Moreover, May and June are the hottest months, while January is the coldest. Agricultural fields are located ~7 km from Karnal and CRB has been a continuous practice in the past few years.

3. Data and methods

Hourly PM10, PM2.5, NO2 and SO2 concentrations for the selected sites in Delhi and Haryana during 2019 were obtained from the Central Pollution Control Board (CPCB; http://cpcb.nic.in/real-time-air-quality-data/) data portal. Meteorological parameters such as ambient temperature (AT), relative humidity (RH), wind speed (WS) and wind direction (WD) were obtained from the Indian Meteorological Department (IMD) as daily means for 2019. Air pollutant data over Haryana was analysed for pre-burning and burning periods of kharif (rice) and rabi (wheat) season. Concurrently, data was collected over Delhi to study the impact of transported air pollutants released from CRB activities in Haryana. The four main periods of the cropping system in 2019 are 1) pre-burning kharif (6th September-10th October), 2) burning kharif (11th October-20th November), 3) pre-burning rabi (10th March-15th April), and 4) burning rabi (16th April-21st May). These durations are determined based on 1) existing literature, 2) survey information collected from local population and farmers, and 3) satellite-derived fire count and intensity data.

3.1. MODIS thermal anomalies

The Moderate Resolution Imaging Spectroradiometer (MODIS) sensor is on-board two satellite platforms, viz. Aqua and Terra. For each wavelength (ranging from visible to infrared spectrum) the satellite records digital numbers (DN). DN values from specific wavelength channels can be used to estimate the top of atmosphere radiance, reflectance or brightness temperature. Of its many uses, 4, 11 and 12 μm wavelength channels are used to identify potential fire pixels. The collection 6 algorithm developed by Giglio et al. (2016) uses dynamic day and nighttime temperature thresholds to identify potential fires. Compared to the previous collection 5 dataset, dynamic thresholds greatly improve thermal anomalies and fire detection across the globe (Giglio et al., 2016).

For the present study, MODIS Aqua and Terra Thermal Anomalies (at 1 km spatial resolution) data were used to locate active fires over Haryana. This vector (point location) data was acquired from the Fire Information for Resource Management System (FIRMS) database of NASA (https://firms.modaps.eosdis.nasa.gov/download/). Collection 6 MODIS thermal anomalies can detect various types of fires, including vegetation burning, active volcanoes and other static fire sources. Supplementary Table 1 shows the number of fire counts for rabi and kharif during the pre-burning and burning period. Of the total fires burning in the selected bounding box over northern India (70–80° E longitude, 25–35° N latitude), only those flagged as biomass/vegetation burning (type 0 in MODIS data) and falling inside the Haryana state boundary were further considered. Following Chandra and Sinha (2016), fire events flagged with the confidence level <80% in the downloaded dataset were rejected and filtered out. The remaining high confidence events (≥80%) were considered for the present study. Location, brightness temperature, day/night fire event and fire radiative power (FRP) is also provided within the dataset.

3.2. HYSPLIT model (backward trajectory analysis)

The sources of the air mass were identified using the National Oceanic and Atmospheric Administration (NOAA) Air Resources Laboratory's (ARL) HYSPLIT model. This model is widely used to compute air parcel trajectories as well as the complex transport, dispersion, chemical transformation, and deposition simulations (Draxler and Hess, 1998). Performing backward trajectories from receptor sites is more efficient than forward trajectories, to find potential source locations (Stein et al., 2015; Draxler, 2011). In the present study, three days of NOAA-HYSPLIT air mass back trajectories at 500 m and 1000 m above ground level (AGL) were used to study the air mass movements over the region in rabi and kharif season.

4. Results and discussion

4.1. Meteorological parameters variations

The variations of meteorological variables viz., AT, RH, WS, and WD during pre-burning and burning periods of rabi (Table 1) and kharif season (Table 2) for Delhi and Haryana are presented. In rabi season, AT varied from 23.6 to 31.9 °C. AT considerably increased from pre-burning (mid-March to mid-April) to burning (mid-April to mid-May) period due to progressing pre-monsoon season (Table 1). RH is lowest (highest) during the burning (pre-burning) period of rabi (kharif) season. RH varied from 33% to 58% in selected sites in Delhi and Haryana. Winds are observed to be considerably stronger (3.8 m/s) over IGI site in Delhi in rabi season as compared to other sites and seasons. Winds in rabi season blow in the southwest direction and do not show any wind reversal from pre-burning to burning period transition (Table 1).

Table 1.

Mean of meteorological variables during pre-burning and burning periods of rabi season in Haryana and Delhi.

Haryana
Pre-burning (rabi)
 Burning (rabi)
HSR KAR HSR KAR
AT (ºC) 26.2 ± 4.7 25.6 ± 3.1 31.9 ± 3.6 30.2 ± 3.0
RH (%) 47 ± 10 50 ± 9 33 ± 15 35 ± 1
WS (m/s) 1.7 ± 0.5 0.8 ± 0.3 1.8 ± 0.5 0.9 ± 0.3
WD SW SW SW SW
Delhi
Pre-burning (rabi) Burning (rabi)
RKP IGI RKP IGI
AT (ºC) 23.6 ± 4.7 25.3 ± 4.3 29.5 ± 3.5 30.8 ± 3.0
RH (%) 43 ± 8 58 ± 10 34 ± 11 50 ± 11
WS (m/s) 0.9 ± 0.3 3.2 ± 2.0 1.0 ± 0.4 3.8 ± 1.2
WD SW SW SW SW
Open in a new tab

Table 2.

Same as Table 1, but for kharif season.

Haryana
Pre-burning (kharif)
 Burning (kharif)
HSR KAR HSR KAR
AT (ºC) 28.6 ± 2.2 26.7 ± 4.8 23.8 ± 2.7 22.9 ± 2.2
RH (%) 71 ± 7 74 ± 10 55 ± 7 66 ± 6
WS (m/s) 1.5 ± 0.4 0.5 ± 0.2 1.2 ± 0.5 0.6 ± 0.3
WD SE SE SW SW
Delhi
Pre-burning (kharif) Burning (kharif)
RKP IGI RKP IGI
AT (ºC) 26.5 ± 2.3 28.7 ± 2.2 20.6 ± 2.8 22.9 ± 2.5
RH (%) 66 ± 4 74 ± 5 61 ± 5 71 ± 5
WS (m/s) 0.9 ± 0.4 2.7 ± 1.9 0.5 ± 0.2 1.7 ± 0.9
WD SE SE SW SW
Open in a new tab

In case of kharif season, AT varied from 20.6 to 28.7 °C. It decreased from pre-burning (mostly September) to burning (mid-October to mid-November) period due to onset of winter (Table 2). Similar pattern is noted for RH. Winds at IGI site during pre-burning period (2.7 m/s) were remarkably stronger than the burning period (1.7 m/s). Unlike rabi season, a wind reversal from southeast (during pre-burning) to southwest (during burning) direction is noted in kharif season for both Delhi and Haryana (Table 2).

4.2. Fire count analysis

CRB fire events extracted from Collection 6 MODIS thermal anomalies dataset are mapped for rabi and kharif seasons (Figure 2). For both, the number of fire counts increase significantly during the burning period. Furthermore, during the pre-burning period of kharif season, only one CRB fire incidence is observed. However, in rabi, 5 such incidences are noted in the pre-burning period (Figure 2 a, c). CRB fires in the rabi season are much higher in number (277 in total) and are more widely spread across Haryana (Figure 2b). Conversely, fire events (90 in total) during the burning period of kharif are mostly restricted near the Punjab-Haryana state border (Figure 2d). Similar observations were reported by Lohan et al. (2018), where, in Haryana alone, rabi contributed to 57.31% and kharif contributed to 21.17% in CRB events. Yadav et al. (2014) noted that the maximum area under rabi CRB (100.05 thousand ha) was higher than kharif (90.84 thousand ha) in most of the agricultural regions of Haryana e.g., KAR, Kurukshetra, Kaithal, etc. Moreover, Jitendra et al. (2017) also reported that straw left rooted to the ground from mechanised farming practices, is later burned in April–May, causing poor air quality in Haryana.

Figure 2.

Figure 2

Open in a new tab

CRB fire counts (confidence ≥80%) during rabi (wheat) season (a) pre-burning period (b) burning period, as well as kharif (rice) season (c) pre-burning period and (d) burning period, for the year 2019, across Haryana.

Figure 3 shows daily incidences of CRB events (confidence ≥80%) during rabi season. Daily fire counts significantly increase from the pre-burning period (Figure 3a) to the burning period (Figure 3b). Furthermore, fire counts peak on April 28, after which a decline is noted. However, CRB gradually increases during the first week of May, reaching its highest activity on May 9. Interestingly, rabi CRB events were notably restricted from May 4–13. CRB fires in kharif season are increasingly evident after October 10 (Figure 4b). However, few CRB fires are also noted in the late pre-burning period (Figure 4a), indicating earlier harvest by some farmers. While in kharif burning period, CRB fires are consistent (October 12 to November 11). Peak fire activity is observed on November 4, during the latter half of the season. Other than CRB incidences, the radiant heat output, i.e. fire intensity from each event, is also an important parameter (Laurent et al., 2019). Therefore, the parameter FRP is helpful to estimate the pixel-integrated FRP (in megawatts; MW). Since FRP is representative of 1 km × 1 km grids, a higher FRP could imply 1) a higher quantity of stubble present in that grid for CRB or 2) more radiant heat was generated per kilogram of CRB. Figure 5 shows the frequency distribution of FRP for burning period of both seasons. Fire counts and FRP data over Punjab in rabi and kharif season have also been added as part of supporting information (Suppl. Figures 2, 3, and 4).

Figure 3.

Figure 3

Open in a new tab

Daily fire counts (confidence ≥80%) during (a) pre-burning and (b) burning period in rabi season across Haryana in 2019.

Figure 4.

Figure 4

Open in a new tab

Same as Figure 3, but for kharif season.

Figure 5.

Figure 5

Open in a new tab

Frequency distribution of pixel-integrated Fire Radiative Power (FRP; MW) for burning period in (a) rabi and (b) kharif season in Haryana.

For both seasons, majority of CRB fires have <100 MW radiant heat output and most events fall in the 25–50 MW FRP frequency bin. This means that low to medium intensity fires are prevalent during most CRB fire incidences in Haryana. However, rabi season CRB shows stronger FRP (>300 MW for some fires) compared to kharif season CRB.

4.3. Backward trajectory analysis

Three days NOAA-HYSPLIT air mass backward trajectories were plotted for burning period of rabi (Figure 6 a, b) and kharif (Figure 6 c, d) season. Plots for pre-burning period are provided as part of supplementary information (Suppl. Fig. 5). As high pollutant load and smoke plumes are mainly restricted to the lower troposphere, the trajectory altitudes in the model were set at 500 m and 1000 m. Some studies report the typical height of air masses as 500–800 m, especially during the pre-burning period (Kaskaoutis et al., 2014), whereas others report that the smoke-laden air masses may travel even higher (Badarinath et al., 2009a). Moreover, the HYSPLIT backward trajectory analysis has been extensively used for the detection of the air mass movement (Jethva et al., 2018; Saxena et al., 2019, 2020a, b; Stein et al., 2015; Badarinath et al., 2009b). By considering Delhi as the sink location, air mass movement could sufficiently link the potential impact of CRB practices in Haryana. Two backward trajectories were plotted for burning period in each season (Figure 6). In rabi season, majority of the air masses reach Delhi from the adjacent state viz., Haryana, and from farther away states such as Punjab (Figure 6 a, b). Air masses also reach Delhi, from Pakistan, a neighboring country to India. Similar to rabi, in kharif season, many air masses reach from Haryana, Punjab and Pakistan, while some air masses from IGP also reach Delhi (Figure 6 c, d).

Figure 6.

Figure 6

Open in a new tab

Three day NOAA HYSPLIT backward trajectory analysis plots showing air mass movement to Delhi for burning period in (a, b) rabi and (c, d) kharif season in 2019.

Pollutants emitted through CRB activities in any of these adjacent or faraway states will reach via the air masses to Delhi and thus deteriorate its air quality.

4.4. Distribution of PM10, PM2.5, NO2 and SO2

4.4.1. Descriptive statistics

Pre-burning and burning concentration (mean ± SD) of PM10, PM2.5, NO2 and SO2 for both seasons over Haryana and Delhi are provided in Table 3. Additionally, box and whisker plots of these pollutants are shown in Figure 7 In case of rabi season, a maximum increase of ~34% and ~25% in PM2.5 and NO2 concentrations, respectively is observed over Haryana from pre-burning to burning period (Table 3). This increase can be associated with rampant stubble burning of wheat crop after harvest. Jain et al. (2014) also reported an increase of 18%, 34%, 25% and 17% in PM10, PM2.5, NO2 and SO2 concentrations in rabi burning period in Haryana. For Delhi, the present study observed a ~23% increase in NO2, while a ~22% decrease in PM2.5 from the pre-burning to burning period (Table 3). The 24-h National Ambient Air Quality Standards (NAAQS) for PM10, PM2.5, NO2 and SO2 are prescribed as 100, 60, 80 and 80 μg/m3, respectively. In rabi season, for both Haryana and Delhi, PM10 and PM2.5 concentrations exceeded their NAAQS limits, while NO2 and SO2 remained within the limits (Figure 7 a, b).

Table 3.

Descriptive statistics (mean ± SD) of PM10, PM2.5, NO2 and SO2 concentrations (μg/m3) during the pre-burning and burning period in rabi and kharif seasons at selected sites in Haryana and Delhi.

Rabi (Pre-burning)
 Rabi (Burning)
RKP IGI Delhi HSR KAR Haryana RKP IGI Delhi HSR KAR Haryana
PM10 211.7 ± 50.5 203.6 ± 65.2 207.6 ± 57.5 145.9 ± 50.3 1619 ± 55.0 153.9 ± 52.6 245.8 ± 89.9 245.0 ± 91.3 245.4 ± 90.5 198.4 ± 76.4 180.0 ± 60.3 189.2 ± 68.4
PM2.5 101.6 ± 58.7 71.8 ± 23.4 86.7 ± 43.5 64.8 ± 31.6 68.6 ± 27.4 66.7 ± 29.5 61.5 ± 12.3 73.6 ± 33.3 67.5 ± 45.6 98.4 ± 37.8 103.7 ± 32.8 101.0 ± 35.3
NO2 73. 5 ± 10.1 35.8 ± 18.1 54.6 ± 14.0 22.6 ± 6.8 22.0 ± 11.6 22.3 ± 9.2 71.1 ± 13.8 13.3 ± 2.6 42.2 ± 8.3 28.3 ± 10.3 31.1 ± 12.7 29.6 ± 11.5
SO2 9.2 ± 2.5 NA 9.2 ± 2.5 25.6 ± 8.7 25.1 ± 10.4 25.3 ± 9.6 9.8 ± 4.3 NA 9.8 ± 4.3 32.5 ± 12.2 28.4 ± 13.2 30.4 ± 12.5
Kharif (Pre-burning) Kharif (Burning)
RKP IGI Delhi HSR KAR Haryana RKP IGI Delhi HSR KAR Haryana
PM10 104.2 ± 44.5 110.5 ± 42.4 107.4 ± 43.2 97.7 ± 30.3 109.3 ± 43.1 103.5 ± 36.5 312.2 ± 125.1 325.9 ± 145.7 319.0 ± 135.0 279.3 ± 113.7 225.6 ± 83.6 252.4 ± 98.5
PM2.5 41.5 ± 16.7 40.5 ± 16.7 41.0 ± 16.7 51.4 ± 15.9 50.1 ± 26.9 50.7 ± 21.5 162.6 ± 106.9 187.4 ± 127.1 175.0 ± 117.0 157.3 ± 86.3 150.3 ± 84.8 153.8 ± 85.0
NO2 34.6 ± 17.8 34.5 ± 1.6 34.5 ± 10.0 13.2 ± 4.2 8.8 ± 6.6 11.0 ± 10.8 44.7 ± 22.0 40.8 ± 55.4 42.7 ± 38.5 29.1 ± 15.5 17.7 ± 9.2 23.4 ± 12.2
SO2 5.2 ± 1.5 NA 5.2 ± 1.5 24.7 ± 14.3 13.9 ± 13.3 19.3 ± 13.8 10.5 ± 2.6 NA 10.5 ± 2.6 20.7 ± 8.1 18.0 ± 6.2 19.4 ± 7.0
Open in a new tab
Figure 7.

Figure 7

Open in a new tab

Box and whisker plots showing PM10, PM2.5, NO2 and SO2 concentrations (μg/m3) variations during pre-burning (in grey) and burning (in red) periods, in rabi season in (a) Haryana and (b) Delhi, as well as kharif season in (c) Haryana and (d) Delhi. Plot representations: 75th and 25th percentiles by box bounds; maximum and minimum by whiskers; 95th and 5th percentiles by rhombus (solid); median by line (solid) in each box and mean by squares (hollow).

The transition from pre-burning to burning period in kharif season also shows a considerable increase in the pollutant concentrations. In Haryana, from pre-burning to burning period, an increase of ~67%, ~59% and ~53% is observed for PM2.5, PM10 and NO2 respectively while for Delhi, an increase of ~76%, ~66% and ~19% is observed for the same pollutants (Table 3). Similar to rabi season, in kharif, concentrations of PM10 and PM2.5 exceeded their NAAQS limits, while NO2 and SO2 remained within the limits (Figure 7 c, d). Beig et al. (2020) noted high PM concentrations during the kharif burning period in Delhi. Additionally, Chawala and Sandhu (2020) also reported a significant emission of air pollutants through CRB activity. Moreover, pollution load from fireworks during Dusshera (in October) and Diwali (in November) festival contribute towards worsening of air quality in northern India. In the present study, PM10 and PM2.5 concentrations are observed to be several times higher than that of NO2 and SO2 (Figure 7). PM can travel long distances as compared to gaseous pollutants (Kim et al., 2015). The spread of PM from open field CRB activities causes worsening of air quality in the Delhi region (Hays et al., 2005; Sharma et al., 2010; Agarwal et al., 2012; Sidhu et al., 2015). The role of meteorology is crucial in impeding vertical mixing of air masses and dispersion of air pollutants. Low temperature, stable atmospheric conditions lead to low pollutant dispersion during winter which aids in significant rise of PM concentrations in kharif. As a result, the pollutant load is essentially trapped within the boundary layer, and this causes a massive spike in the ambient concentrations over Delhi.

4.4.2. Time series analysis

In general, both seasons show higher pollutant concentrations during the burning period (Figures 8 and 9. For rabi season, significantly higher PM10 and PM2.5 concentrations are observed during the burning period over both Haryana and Delhi (Figure 8). Many isolated peaks are also observed from late March to the beginning of April, and can be associated with the prevalence of increased dust storm activity and the absence of pre-monsoonal precipitation over the region. Such dust storms carry a significantly high load of dust from the Thar Desert to Delhi, and therefore dramatically increase particulate concentrations in the atmosphere. As PM and NO2 are important markers of biomass burning, high peaks witnessed during the rabi burning period represent CRB activity in Haryana. On most days, the concentration of PM is found to be two to three folds higher than the NAAQS standards (Figure 8). Previous research highlights CRB activities are a significant driver of increase in pollutant concentration (Chandra and Sinha, 2016; Jain et al., 2014; Sharma et al., 2010).

Figure 8.

Figure 8

Open in a new tab

Time series of PM10, PM2.5, NO2 and SO2 during pre-burning and burning periods in the rabi season over Haryana (left panel) and Delhi (right panel). Horizontal lines in each respective sub-plot indicate NAAQS prescribed limits.

Figure 9.

Figure 9

Open in a new tab

Same as Figure 8, but for kharif season.

Kharif early pre-burning time in September, coincides with the late-monsoon period and occasional precipitation is observed over northern India. Thus, wet scavenging of atmospheric pollutants can sufficiently lower their concentration in the atmosphere (Sonwani and Kulshrestha, 2019). However, by the late pre-burning period (first week of October), monsoonal activity ceases and an increase in pollutants is observed. Over both Haryana and Delhi, PM10 and PM2.5 concentrations significantly exceed their 24-h NAAQS, while NO2 and SO2 concentrations remain within the prescribed limits (Figure 9). In kharif burning period, PM2.5 and PM10 concentrations are 33% and 25% higher over Haryana, than the rabi season. The same in case of Delhi was noted to be 61% and 23%, respectively. Several authors also mentioned the increasing concentration of PM2.5 and PM10 in kharif burning period (Grover and Chaudhry, 2019; Sharma et al., 2010; Jain et al., 2014). Apart from CRB activities, favourable meteorological conditions prevailing in winter, as well as, increased pollution load from Dusshera and Diwali firework celebrations are important factors responsible for deteriorating air quality over the region.

4.4.3. Correlation analysis

Underlying associations are determined between mean concentrations (μg/m3) of PM10, PM2.5, NO2 and SO2 and meteorological parameters viz., WS, AT and RH (Table 4). Pearson correlation coefficient (r), assumes an inherent linear relationship in the data (Jain et al., 2017) and the correlation ranges from strong (1.00–0.50) to moderate (0.49–0.30) to weak (0.29–0.00) (Xie et al., 2015). The correlations for both seasons in Haryana and Delhi are calculated only for the burning period. In Haryana, during rabi season PM10 and PM2.5 have a significant strong negative association with RH and a significant strong positive association with AT. In case of Delhi, both these parameters have a significant strong negative association with RH, but a weak positive association with AT. High AT during pre-monsoon, accompanied by low RH, helps soil particles to loosen up and these can be readily dispersed into the atmosphere, with the slightest winds. Burning of straw left rooted to the ground after harvest leads to poor air quality (Jitendra et al., 2017). In addition to CRB activities, the agricultural fields are highly vulnerable to soil erosion, especially, when left unirrigated. Thus, such PM particles are also available for dispersion. Another phenomenon unique to pre-monsoon over the study region is the aggravated dust load in the air, through frequent and intense dust storm activity. All such factors, combined, play a major role in causing high concentrations of PM, when the ambient AT and RH in rabi season, are high and low, respectively. Unlike PM, the emissions of NO2 and SO2 are limited in terms of their source. Stronger winds blown over the region in rabi season promote dispersion and/or transport of NO2 and SO2. The same can be noted from Table 4a,b where NO2 shows a significant strong to moderate, but a negative correlation with WS over Haryana and Delhi. Interestingly, PM does not show a negative correlation with WS, but in fact is weakly positively associated. This strengthens our point that PM sources are abundantly available, even after possible dispersion via winds.

Table 4.

Pearson correlation matrix between daily mean concentrations of PM10, PM2.5, NO2 and SO2 and meteorological parameters viz., AT, RH, WS and WD in the burning period of rabi and kharif seasons over Haryana and Delhi.

NO2 (μg/m3) SO2 (μg/m3) PM10 (μg/m3) PM2.5 (μg/m3) WS (m/s) AT (ºC) RH (%)
a. Rabi (Haryana)
NO2 1 -.07 .12 .07 -.37∗∗ .30 -.22
SO2 -.07 1 -.00 -.29∗ .01 -.25 .02
PM10 .12 -.00 1 .79∗∗ .22 .57∗∗ -.55∗∗
PM2.5 .07 -.29∗ .79∗∗ 1 .08 .55∗∗ -.48∗∗
WS -.37∗∗ .01 .22 .08 1 .13 -.11
AT .30 -.25 .57∗∗ .55∗∗ .13 1 -.88∗∗
RH
 -.22
 .02
 -.55∗∗
 -.48∗∗
 -.11
 -.88∗∗
 1
b. Rabi (Delhi)
NO2 1 .45∗∗ .11 -.14 -.57∗∗ .16 -.47∗∗
SO2 .45∗∗ 1 .26 .15 -.28 -.26 -.34∗
PM10 .11 .26 1 .60∗∗ -.01 .32 -.69∗∗
PM2.5 -.14 .15 .60∗∗ 1 .01 .30 -.56∗∗
WS -.56∗∗ -.28 -.01 .01 1 .13 .13
AT .16 -.26 .32 .30 .13 1 .76∗∗
RH
 -.47∗∗
 -.34∗
 -.69∗∗
 -.56∗∗
 .13
 .76∗∗
 1
c. Kharif (Haryana)
NO2 1 .52∗∗ .07 .13 .03 -.71∗∗ -.49∗∗
SO2 .52∗∗ 1 .54∗∗ .42∗∗ -.13 -.34∗ -.34∗∗
PM10 .07 .54∗∗ 1 .88∗∗ -.25∗ -.09 -.07
PM2.5 .13 .42∗∗ .88∗∗ 1 -.19 -.07 -.05
WS .03 -.13 -.25∗ -.19 1 -.20 -.26∗
AT -.71∗∗ -.34∗ -.09 -.07 -.20 1 .37∗
RH
 -.49∗∗
 -.34∗∗
 -.07
 -.05
 -.26∗
 .37∗
 1
d. Kharif (Delhi)
NO2 1 -.33∗ .13 .06 -.42∗∗ .50∗∗ .23
SO2 -.33∗ 1 .28 .39∗ .02 -.62∗∗ -.00
PM10 .13 .28 1 .94∗∗ -.45∗∗ -.22 .57∗∗
PM2.5 .06 .39∗ .94∗∗ 1 -.30 -.43∗∗ .50∗∗
WS -.42∗∗ .02 -.45∗∗ -.30 1 -.03 -.46∗∗
AT .50∗∗ -.62∗∗ -.22 -.43∗∗ -.03 1 -.19
RH .23 -.00 .57∗∗ .50∗∗ -.46∗∗ -.19 1
Open in a new tab
∗

Correlation is significant at the 0.05 level (2-tailed).

∗∗

Correlation is significant at the 0.01 level (2-tailed).

In kharif season, conditions such as lower AT, higher RH and slower winds exist (Tables 1 and 2). These winter conditions aid in lesser soil erosion from open agricultural fields and low dispersion of pollutants (Lal et al., 2000; Xie et al., 2015). Table 4 shows that both PM10 and PM2.5 have a negative association with AT and WS. This implies that with lower temperature and slow winds, pollutant concentration is increasing or is unable to disperse. In Delhi, PM10 and PM2.5 has a significant strong negative association with RH. In both Haryana and Delhi, there exists a significant positive (strong to moderate) association of SO2 with AT and RH. Again, meteorological conditions in winter exacerbate pollutant concentrations. As expected, NO2 is significantly strongly negatively associated with AT in Haryana. However, in case of Delhi, the association is significant, strong but positive. In the year 2019, Delhi government imposed an odd-even scheme from November 4–15, in order to combat the hazardous smog that had enveloped the national capital (Jain et al., 2021). This meant that on any given day the either an odd number plate car could ply, or an even number, thereby significantly reducing the car density and emissions. Thus, meteorological factors also play a role in either increasing or decreasing the pollutant concentrations.

5. Conclusion

The study highlights the role of Haryana's CRB activities, particularly during rabi season, in deteriorating the air quality of Delhi. CRB fire counts (burning period; confidence ≥80%) in Haryana are ~3 times higher and more intense (>300 MW for some fires) in rabi season compared to kharif. CRB fires in rabi are evenly spread across Haryana, but are restricted to Punjab-Haryana state border in kharif. Moreover, Yadav et al. (2014) and Lohan et al. (2018) highlight a higher area under CRB in rabi season, as opposed to kharif. Backward trajectories during burning period of both seasons show air mass movement from adjacent state viz., Haryana, and from faraway places such as Punjab and Pakistan. Some backward trajectories from IGP, also reach Delhi in kharif season. Pollutants emitted through CRB activities in any of these places reach Delhi via air masses, thus deteriorating its air quality.

The transition from pre-burning to burning period in both seasons showed a considerable increase in the pollutant concentrations. Moreover, PM10 and PM2.5 exceeded their NAAQS limits (2–3 times higher), while NO2 and SO2 remained within the limits. During late March-early April (i.e., rabi burning period) prevalence of frequent and intense dust storms (high dust load from Thar desert) and the absence of pre-monsoonal precipitation over the region, considerably increases the PM10 and PM2.5 concentrations. These pollutants have a significant strong negative association with RH and positive association with AT. High AT during pre-monsoon, accompanied by low RH, loosens up soil particles, especially when left unirrigated. These particles can disperse in the air, with the slightest winds. Unlike PM, the emissions of NO2 and SO2 are limited in terms of their source. Stronger winds in rabi season promote dispersion of NO2 and SO2. NO2 shows a significant strong to moderate, but a negative correlation with WS.

Occasional precipitation is observed in early-September (i.e., onset of kharif pre-burning) and by early-October (i.e., late kharif pre-burning period) monsoonal activity ceases, and an increase in pollutant concentrations is observed. In kharif burning period, PM2.5 and PM10 concentrations are 33–61% and 23–25% higher over the region, than rabi season. In kharif season, lower AT, higher RH and slower winds exist. Both PM10 and PM2.5 have a negative association with AT and WS. With lower temperature and slower winds, pollutants are unable to disperse. Apart from CRB activities and favourable meteorological conditions in winter, Dusshera and Diwali firework celebrations is an important factor for deteriorating air quality over the region. As expected, NO2 has a significant, strong and negative association with AT in Haryana. However, in case of Delhi, the association is significant, strong but positive. This could be due to the impact of the odd-even scheme imposed by the Delhi government from November 4–15, 2019.

Strong initiatives are needed to mitigate the ill-effects of CRB activities over the region, in both rabi and kharif season. Large-scale farmer awareness camps and the use of sustainable CRB management practices are suggested. Moreover, isotopic or marker studies can help the scientific community in analysing the role of rabi CRB emissions coming from Haryana to Delhi and its impacts on air-quality and human health.

Declarations

Author contribution statement

Pallavi Saxena: Conceived and designed the experiments; Analyzed and interpreted the data; Contributed reagents, materials, analysis tools or data; Wrote the paper.

Saurabh Sonwani: Conceived and designed the experiments; Analyzed and interpreted the data; Wrote the paper.

Ananya Srivastava, Akash Bharti, Deepali Rangra, Nancy Mongia, Shweta Tejan and Shreshtha Bhardwaj: Analyzed and interpreted the data; Contributed reagents, materials, analysis tools or data.

Madhavi Jain: Analyzed and interpreted the data; Contributed reagents, materials, analysis tools or data; Wrote the paper.

Anju Srivastava: Analyzed and interpreted the data; Wrote the paper.

Funding statement

This work was supported by Hindu College, University of Delhi Innovation Project (Ref no. IP-2019-20/SC/13) Grant.

Data availability statement

Data associated with this study has been deposited at www.cpcb.nic.in and included in article/supplementary material/referenced in article.

Declaration of interests statement

The authors declare no conflict of interest.

Additional information

No additional information is available for this paper.

Acknowledgements

The authors acknowledge the Central Pollution Control Board (CPCB), Delhi and Haryana State Pollution Control Board (HSPCB) for access to secondary data on air pollutants and meteorological parameters. The authors also thank NASA for the provision of MODIS active fire and thermal anomalies data. Furthermore, the authors are grateful to NOAA Air Resources Laboratory (ARL) for the provision of the HYSPLIT transport and dispersion model and/or the READY website (https://www.ready.noaa.gov) used in this publication. All data used in this manuscript are available in the public domain and cited in the text. The authors wish to thank the editor and reviewers for their valuable suggestions that will enhance the quality of this manuscript.

Appendix A. Supplementary data

The following is the supplementary data related to this article:

Supplementary Figures_PS_V2
mmc1.docx (1.4MB, docx)
Supplementary Table_PS_V2
mmc2.docx (15.5KB, docx)

References

  1. Agarwal R., Awasthi A., Singh N., Gupta P.K., Mittal S.K. Effects of exposure to rice-crop residue burning smoke on pulmonary functions and oxygen saturation level of human beings in Patiala (India) Sci. Total Environ. 2012;429:161–166. doi: 10.1016/j.scitotenv.2012.03.074. [DOI] [PubMed] [Google Scholar]
  2. Alexaki N., van den Hof M., Jol K. Netherlands Enterprise Agency; Utrecht: 2019. From Burning to Buying: Creating A Circular Production Chain Out of Left-Over Crop Residue from Indian Farm Land; pp. 1–30.https://www.rvo.nl/sites/default/files/2019/12/MVO-Nederland-rapport-India.pdf Available online at. [Google Scholar]
  3. Arola A., Lindfors A., Natunen A., Lehtinen K.E.J. A case study on biomass burning aerosols: effects on aerosol optical properties and surface radiation levels. Atmosp. Chem. Phys. 2007;7(16):4257–4266. European Geosciences Union, 2007. [Google Scholar]
  4. Badarinath K.V.S., Chand T.K., Prasad V.K. Agriculture crop residue burning in the Indo-Gangetic Plains–a study using IRS-P6 AWiFS satellite data. Curr. Sci. 2006:1085–1089. [Google Scholar]
  5. Badarinath K.V.S., Kharol S.K., Sharma A.R. Long-range transport of aerosols from agriculture crop residue burning in Indo-Gangetic Plains—a study using LIDAR, ground measurements and satellite data. J. Atmos. Sol. Terr. Phys. 2009;71(1):112–120. [Google Scholar]
  6. Badarinath K.V.S., Kharol S.K., Sharma A.R., Prasad V.K. Analysis of aerosol and carbon monoxide characteristics over Arabian Sea during crop residue burning period in the Indo-Gangetic Plains using multi-satellite remote sensing datasets. J. Atmos. Sol. Terr. Phys. 2009;71(12):1267–1276. [Google Scholar]
  7. Beig G., Sahu S.K., Singh V., Tikle S., Sobhana S.B., Gargeva P., Ramakrishna K., Rathod A., Murthy B.S. Objective evaluation of stubble emission of North India and quantifying its impact on air quality of Delhi. Sci. Total Environ. 2020;709:136126. doi: 10.1016/j.scitotenv.2019.136126. [DOI] [PubMed] [Google Scholar]
  8. Bhuvaneshwari S., Hettiarachchi H., Meegoda J.N. Crop residue burning in India: policy challenges and potential solutions. Int. J. Environ. Res. Publ. Health. 2019;16(5):832. doi: 10.3390/ijerph16050832. [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Chandra B.P., Sinha V. Contribution of post-harvest agricultural paddy residue fires in the NW Indo-Gangetic Plain to ambient carcinogenic benzenoids, toxic isocyanic acid and carbon monoxide. Environ. Int. 2016;88:187–197. doi: 10.1016/j.envint.2015.12.025. [DOI] [PubMed] [Google Scholar]
  10. Chang D., Song Y. Estimates of biomass burning emissions in tropical Asia based on satellite-derived data. Atmos. Chem. Phys. 2010;10(5):2335–2351. [Google Scholar]
  11. Chauhan A., Singh R.P. 2017 IEEE International Geoscience and Remote Sensing Symposium. 2017. Poor air quality and dense haze/smog during 2016 in the Indo-Gangetic Plains associated with the crop residue burning and Diwali festival; pp. 6048–6051. [Google Scholar]
  12. Chawala P., Sandhu H.A.S. Stubble burn area estimation and its impact on ambient air quality of Patiala and Ludhiana district, Punjab, India. Heliyon. 2020;6(1) doi: 10.1016/j.heliyon.2019.e03095. [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Cheng Z., Wang S., Fu X., Watson J.G., Jiang J., Fu Q., Chen C., Xu B., Yu J., Chow J.C., Hao J. Impact of biomass burning on haze pollution in the Yangtze River delta, China: a case study in summer 2011. Atmos. Chem. Phys. 2014;14(9):4573–4585. [Google Scholar]
  14. Crutzen P.J., Andreae M.O. Biomass burning in the tropics: impact on atmospheric chemistry and biogeochemical cycles. Science. 1990;250(4988):1669–1678. doi: 10.1126/science.250.4988.1669. [DOI] [PubMed] [Google Scholar]
  15. Draxler R.R., Hess G.D. An overview of the HYSPLIT_4 modelling system for trajectories. Aust. Meteorol. Mag. 1998;47(4):295–308. [Google Scholar]
  16. Draxler R.R. 2011. HYSPLIT (Hybrid Single-Particle Lagrangian Integrated Trajectory) Model. Accessed via NOAA ARL ready website. http://ready. arl. noaa. gov/HYSPLIT. php. [Google Scholar]
  17. Dumka U.C., Tiwari S., Kaskaoutis D.G., Soni V.K., Safai P.D., Attri S.D. Aerosol and pollutant characteristics in Delhi during a winter research campaign. Environ. Sci. Pollut. Res. 2019;26:3771–3794. doi: 10.1007/s11356-018-3885-y. [DOI] [PubMed] [Google Scholar]
  18. Gadde B., Bonnet S., Menke C., Garivait S. Air pollutant emissions from rice straw open field burning in India, Thailand and the Philippines. Environ. Pollut. 2009;157(5):1554–1558. doi: 10.1016/j.envpol.2009.01.004. [DOI] [PubMed] [Google Scholar]
  19. Giglio L., Schroeder W., Justice C.O. The collection 6 MODIS active fire detection algorithm and fire products. Rem. Sens. Environ. 2016;178:31–41. doi: 10.1016/j.rse.2016.02.054. [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Grover D., Chaudhry S. Ambient air quality changes after stubble burning in rice–wheat system in an agricultural state of India. Environ. Sci. Pollut. Control Ser. 2019;26(20):20550–20559. doi: 10.1007/s11356-019-05395-5. [DOI] [PubMed] [Google Scholar]
  21. Hays M.D., Fine P.M., Geron C.D., Kleeman M.J., Gullett B.K. Open burning of agricultural biomass: physical and chemical properties of particle-phase emissions. Atmos. Environ. 2005;39(36):6747–6764. [Google Scholar]
  22. He Q., Zhao X., Lu J., Zhou G., Yang H., Gao W., Yu W., Cheng T. Impacts of biomass-burning on aerosol properties of a severe haze event over Shanghai. Particuology. 2015;20:52–60. [Google Scholar]
  23. Jain N.K., Kaushik K., Choudhary P. Sustainable perspectives on transportation: public perception towards odd-even restrictive driving policy in Delhi, India. Transport Pol. 2021;106:99–108. [Google Scholar]
  24. Jain N., Bhatia A., Pathak H. Emission of air pollutants from crop residue burning in India. Aerosol Air Qual. Res. 2014;14:422–430. [Google Scholar]
  25. Jain M., Dimri A.P., Niyogi D. Land-air interactions over urban-rural transects using satellite observations: analysis over Delhi, India from 1991–2016. Rem. Sens. 2017;9(12):1283–1297. [Google Scholar]
  26. Jethva H., Chand D., Torres O., Gupta P., Lyapustin A., Patadia F. Agricultural burning and air quality over northern India: a synergistic analysis using NASA’s a-train satellite data and ground measurements. Aerosol Air Qual. Res. 2018;18:1756–1773. [Google Scholar]
  27. Jethva H., Torres O., Field R.D., Lyapustin A., Gautam R., Kayetha V. Connecting crop productivity, residue fires, and air quality over northern India. Sci. Rep. 2019;9(1):1–11. doi: 10.1038/s41598-019-52799-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Jitendra, Venkatesh S., Kukreti I., Pandey K., Niyogi D.G., Mukerjee P. DTE; 2017. India’s Burning Issue of Crop Burning Takes a New Turn.http://www.downtoearth.org.in/coverage/river-of-fire - 57924 Retrieved from. [Google Scholar]
  29. Kaskaoutis D.G., Kumar S., Sharma D., Singh R.P., Kharol S.K., Sharma M., Singh A.K., Singh S., Singh A., Singh D. Effects of crop residue burning on aerosol properties, plume characteristics, and long-range transport over northern India. J. Geophys. Res.: Atmospheres. 2014;119(9):5424–5444. [Google Scholar]
  30. Kaushik C.P., Sangwan P., Haritash A.K. Association of polycyclic aromatic hydrocarbons (PAHS) with different sizes of atmospheric particulate in Hisar city and its health aspects. Polycycl. Aromat. Comp. 2012;32(5):626–642. [Google Scholar]
  31. Kim K.H., Kabir E., Kabir S. A review on the human health impact of airborne particulate matter. Environ. Int. 2015;74:136–143. doi: 10.1016/j.envint.2014.10.005. [DOI] [PubMed] [Google Scholar]
  32. Lal S., Naja M., Subbaraya B.H. Seasonal variations in surface ozone and its precursors over an urban site in India. Atmos. Environ. 2000;34(17):2713–2724. [Google Scholar]
  33. Langmann B., Duncan B., Textor C., Trentmann J., van der Werf G.R. Vegetation fire emissions and their impact on air pollution and climate. Atmos. Environ. 2009;43(1):107–116. [Google Scholar]
  34. Laurent P., Mouillot F., Moreno M.V., Chao Y., Ciais P. Varying relationships between fire radiative power and fire size at a global scale. Biogeosciences. 2019;16(2):275–288. [Google Scholar]
  35. Lohan S.K., Jat H.S., Yadav A.K., Sidhu H.S., Jat M.L., Choudhary M., Peter J.K., Sharma P.C. Burning issues of paddy residue management in north-west states of India. Renew. Sustain. Energy Rev. 2018;81:693–706. [Google Scholar]
  36. Mahato S., Pal S., Ghosh K.G. Science of the Total Environment; 2020. Effect of Lockdown amid COVID-19 Pandemic on Air Quality of the Megacity Delhi, India; p. 139086. [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. Mittal S.K., Singh N., Agarwal R., Awasthi A., Gupta P.K. Ambient air quality during wheat and rice crop stubble burning episodes in Patiala. Atmos. Environ. 2009;43(2):238–244. [Google Scholar]
  38. Perrino C., Tiwari S., Catrambone M., Dalla Torre S., Rantica E., Canepari S. Chemical characterization of atmospheric PM in Delhi, India, during different periods of the year including Diwali festival. Atmosp. Poll. Res. 2011;2(4):418–427. [Google Scholar]
  39. Prabhu V., Soni A., Madhwal S., Gupta A., Sundriyal S., Shridhar V., Sreekanth V., Mahapatra P.S. Black carbon and biomass burning associated high pollution episodes observed at Doon valley in the foothills of the Himalayas. Atmos. Res. 2020:105001. [Google Scholar]
  40. Rana M., Mittal S.K., Beig G., Rana P. The impact of crop residue burning (CRB) on the diurnal and seasonal variability of the ozone and PM levels at a semi-urban site in the north-western Indo-Gangetic Plain. J.Earth Syst. Sci. 2019;128(6):166. [Google Scholar]
  41. Saxena P., Chakraborty M., Sonwani S. Phytotoxic effects of surface ozone exposure on rice crop—a case study of tropical megacity of India. J. Geosci. Environ. Protect. 2020;8(5):322–334. [Google Scholar]
  42. Saxena P., Srivastava A., Tyagi M., Kaur S. Impact of tropospheric ozone on plant metabolism–a review. Pollut. Res. 2019;38(1):175–180. [Google Scholar]
  43. Saxena P., Srivastava A., Verma S., Singh L., Sonwani S. Measurement, Analysis and Remediation of Environmental Pollutants. Springer; Singapore: 2020. Analysis of atmospheric pollutants during fireworks festival ‘Diwali’ at a residential site Delhi in India; pp. 91–105. [Google Scholar]
  44. Sharma A.R., Kharol S.K., Badarinath K.V.S., Singh D. Impact of agriculture crop residue burning on atmospheric aerosol loading–a study over Punjab State, India. Ann. Geophys. 2010;28(2) [Google Scholar]
  45. Sidhu R., Bansal M., Bath G.S., Garg R. Impact of stubble burning on the ambient air quality. Int. J. Mech. Prod. Eng. 2015;3(10):46–50. [Google Scholar]
  46. Singh S., Soni K., Bano T., Tanwar R.S., Nath S., Arya B.C. Annales Geophysicae. Vol. 28. European Geosciences Union; 2010. Clear-sky direct aerosol radiative forcing variations over mega-city Delhi; pp. 1157–1166. No. 5. [Google Scholar]
  47. Sonwani S., Kulshrestha U.C. PM 10 carbonaceous aerosols and their real-time wet scavenging during monsoon and non-monsoon seasons at Delhi, India. J. Atmos. Chem. 2019;76(3):171–200. [Google Scholar]
  48. Statistical Abstract of Haryana (SAH) Department of Economic and Statistical Analysis; Haryana: 2017. pp. 1–707.http://esaharyana.gov.in/en-us/State-Statistical-Abstract-of-Haryana Publication No. 1180Accessed online at. [Google Scholar]
  49. Stein A.F., Draxler R.R., Rolph G.D., Stunder B.J., Cohen M.D., Ngan F. NOAA’s HYSPLIT atmospheric transport and dispersion modelling system. Bull. Am. Meteorol. Soc. 2015;96(12):2059–2077. [Google Scholar]
  50. Streets D.G., Bond T.C., Carmichael G.R., Fernandes S.D., Fu Q., He D., Klimont Z., Nelson S.M., Tsai N.Y., Wang M.Q., Woo J.H. An inventory of gaseous and primary aerosol emissions in Asia in the year 2000. J. Geophys. Res.: Atmospheres. 2003;108(D21) [Google Scholar]
  51. Tang H., Liu G., Zhu J., Han Y., Kobayashi K. Seasonal variations in surface ozone as influenced by Asian summer monsoon and biomass burning in agricultural fields of the northern Yangtze River Delta. Atmos. Res. 2013;122:67–76. [Google Scholar]
  52. Targino A.C., Krecl P., Johansson C., Swietlicki E., Massling A., Coraiola G.C., Lihavainen H. Deterioration of air quality across Sweden due to transboundary agricultural burning emissions. Boreal Environ. Res. 2013;18:19–36. [Google Scholar]
  53. Tiwari S., Srivastava A.K., Bisht D.S., Parmita P., Srivastava M.K., Attri S.D. Diurnal and seasonal variations of black carbon and PM2. 5 over New Delhi, India: influence of meteorology. Atmos. Res. 2013;125:50–62. [Google Scholar]
  54. Tsay S.C., Maring H.B., Lin N.H., Buntoung S., Chantara S., Chuang H.C., Gabriel P.M., Goodloe C.S., Holben B.N., Hsiao T.C., Hsu N.C. Satellite-surface perspectives of air quality and aerosol-cloud effects on the environment: an overview of 7-SEAS/BASELInE. Aerosol Air Qual. Res. 2016;16(11):2581–2602. [Google Scholar]
  55. Venkataraman C., Habib G., Kadamba D., Shrivastava M., Leon J.F., Crouzille B., Boucher O., Streets D.G. Emissions from open biomass burning in India: integrating the inventory approach with high-resolution Moderate Resolution Imaging Spectroradiometer (MODIS) active-fire and land cover data. Global Biogeochem. Cycles. 2006;20(2):1–12. [Google Scholar]
  56. Wang Y., Zhang Q.Q., He K., Zhang Q., Chai L. Sulfate-nitrate-ammonium aerosols over China: response to 2000–2015 emission changes of sulfur dioxide, nitrogen oxides, and ammonia. Atmos. Chem. Phys. 2013;13(5):2635. [Google Scholar]
  57. Witham C., Manning A. Impacts of Russian biomass burning on UK air quality. Atmos. Environ. 2007;41:8075–8090. [Google Scholar]
  58. Xie Y., Zhao B., Zhang L., Luo R. Spatiotemporal variations of PM2.5 and PM10 concentrations between 31 Chinese cities and their relationships with SO2, NO2, CO and O3. Particuology. 2015;20:141–149. [Google Scholar]
  59. Yadav M., Sharma M.P., Prawasi R., Khichi R., Kumar P., Mandal V.P., Salim A., Hooda R.S. Estimation of wheat/rice residue burning areas in major districts of Haryana, India, using remote sensing data. J. Indian Soc. Remote Sens. 2014;42(2):343–352. [Google Scholar]
  60. Zhang H., Hu D., Chen J., Ye X., Wang S.X., Hao J.M., Wang L., Zhang R., An Z. Particle size distribution and polycyclic aromatic hydrocarbons emissions from agricultural crop residue burning. Environ. Sci. Technol. 2011;45(13):5477–5482. doi: 10.1021/es1037904. [DOI] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary Figures_PS_V2
mmc1.docx (1.4MB, docx)
Supplementary Table_PS_V2
mmc2.docx (15.5KB, docx)

Data Availability Statement

Data associated with this study has been deposited at www.cpcb.nic.in and included in article/supplementary material/referenced in article.

Articles from Heliyon are provided here courtesy of Elsevier

RESOURCES