Ambient black carbon, PM2.5 and PM10 at Patna

Science of the Total Environment 624 (2018) 1387–1400

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Science of the Total Environment journal homepage: www.elsevier.com/locate/scitotenv

Ambient black carbon, PM2.5 and PM10 at Patna: Influence of anthropogenic emissions and brick kilns Mohammad Arif a,⁎, Ramesh Kumar a, Rajesh Kumar a, Zusman Eric b, Piyush Gourav a a b

Department of Environment Science, School of Basic Sciences and Research, Sharda University, India Sustainability Governance Center, Institute for Global Environmental Strategies, Japan

H I G H L I G H T S

G R A P H I C A L

A B S T R A C T

• 76.67% of PM2.5 and 87.78% of PM10 value exceeded from NAAQS in the winter on daily basis while 46.74% and 36.96% in pre-monsoon season, respectively. • PM2.5 and PM10 show good correlations with BC (R 0.5225 with p b 0.001) which represents contribution from natural sources, transport of aged pollutants and windblown dust. • High biomass burning areas (Northwest, northern region) raise the concentration during the winter and premonsoon season. • Brick manufacturing is one of the major source of pollution in the city.

a r t i c l e

i n f o

Article history: Received 31 October 2017 Received in revised form 19 December 2017 Accepted 19 December 2017 Available online xxxx Editor: Xuexi Tie Keywords: Particulate matter Black carbon Brick kilns Dust storms Air quality Backword trajectory HYSPLIT

⁎ Corresponding author. E-mail address: arifi[email protected] (M. Arif).

https://doi.org/10.1016/j.scitotenv.2017.12.227 0048-9697/© 2017 Elsevier B.V. All rights reserved.

a b s t r a c t Particulate Matters like Black Carbon, PM2.5 and PM10 present in the atmosphere not only poses a threat to human health but also contributes to near-term regional and global atmospheric warming. There has been a large concern of this kind of pollutants in Indo-Gangetic Plains (IGP). Hence, an attempt has been made to see the impact in one of the highly developing city of IGP as Patna. This city has been ranked among the top 100 air polluted cities in the world. To establish the role of BC, PM2.5, and PM10 on local air quality, continuous measurements were conducted at seven locations of Patna from January to December 2015. The seasonal mass concentration of BC were 13.92 ± 3.48 μgm−3 in the winter, 9.65 ± 3.0 μgm−3 in the pre-monsoon, 5.83 ± 1.90 μgm−3 in the monsoon and 7.86 ± 3.66 μgm−3 in the post-monsoon. Similarly, the seasonal concentrations of PM2.5 (PM10) were 68.86 ± 18.83 μgm−3 (108.13 ± 21.49 μgm−3) in the winter; 64.62 ± 18.76 μgm−3 (93.45 ± 18.42 μgm−3) in the pre-monsoon; 37.83 ± 11.27 μgm−3 (62.82 ± 14.81 μgm−3) in the monsoon and 40.14 ± 16.66 μgm−3 (64.72 ± 22.40 μgm−3) in the post-monsoon. About 76.67% of PM2.5 and 87.78% of PM10 concentrations were greater than NAAQ Standards in the winter on a daily basis and 46.74% and 36.96% in the pre-monsoon season. The backward trajectory analysis was also carried out through HYSPLIT model which suggests that the additional source of these pollutants during the winter and pre-monsoon season from the northwest and northern region of Patna. The ratios of PM10/PM2.5 observed at brick kilns cluster monitoring locations during the brick manufacturing period were significantly higher (0.87–4.48 μg μgm−3) than other monitoring sites and increase the level of these pollutants over the city. © 2017 Elsevier B.V. All rights reserved.

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M. Arif et al. / Science of the Total Environment 624 (2018) 1387–1400

1. Introduction

2. Material and method

Particulate Matters like Black Carbon (BC), PM2.5 and PM10 have increased sharply in Developing Countries due to natural and anthropogenic activities (Carmichael et al., 2009; Adhikary et al., 2007). BC has considered a second most significant local and global climate change forcing agent after carbon dioxide (Gustafsson et al., 2009; Venkataraman et al., 2005; Bond et al., 2007; Forster et al., 2007; Ramanathan and Carmichael, 2008; Bond et al., 2013). Both PM2.5 and PM10 also impact the climate with effects on precipitation (Stevens and Feingold, 2009), cloud formation (Wang et al., 2011) and the earth radiation budget (Ramnathan et al., 2007). Most importantly, these pollutants contribute to the outdoor and indoor air pollution in the regional and global environment (Han and Naeher, 2006; Guttikunda et al., 2013; Joshi and Dudani, 2008; Obaidullah et al., 2012; Delfino et al., 2005). There has been a large concern of this kind of pollutants in Indo-Gangetic Plains (IGP) region. This is extremely populous region in the world and has considered as one of the major contributor to BC, PM2.5 and PM10 due to use of biofuels in traditional cookstoves (Rehman et al., 2011; Saud et al., 2012), existence of considerable number of thermal power plants (Prasad et al., 2006), vehicular emission (van Donkelaar et al., 2010; Pandey and Venkataraman, 2014), agricultural biomass burning and brick kilns activities. Hence, an attempt has been made to see the impact in one of the highly developing city of IGP as Patna. This city has been ranked among the top 100 air polluted cities in the world (WHO, 2014). Due in part to the city's rapid growth, the number of vehicles emitting pollution have increased almost threefold over the past two decades (about 32% of families has a two-wheeler, and 10% has a four-wheeler (Office of the Registrar General & Census Commissioner, 2011). Moreover, this city is now home to N 30,600 industrial units (textiles, paper, agricultural, pharmaceutical and paint) and 29% of the city's families are using a traditional cookstoves stove for cooking and heating purposes. In addition to above sources, crop residue and municipal solid waste burning, use of diesel generator, construction activities and emissions from brick kilns units (421 No.) are other key sources led to more pollution in the city. Limited studies on sources and impacts of BC, PM 10 and PM 2.5 have been carried out in IGP such as Delhi (Marrapu et al., 2014; Tiwari et al., 2015; Ali et al., 2013; Ali et al., 2015; Beig et al., 2013), Agra (Kulshrestha et al., 2009), Kanpur (Sharma and Maloo, 2005), Lucknow (Pandey et al., 2012) and Patiala (Awasthi et al., 2011). The causes and impacts of BC, PM2.5 and PM 10 pollutants are poorly determined for Patna which makes it among the top 100 air polluted cities in the world. The formulation of policies and control measures nonetheless requires an evidencebased understanding of the emission sources, periodic concentrations, seasonal variability and other meteorological factors of these pollutants. Some researcher examined concentrations of PM10 pollutant at Patna (see Table 2) and it is very important to understand the all emission sources, periodic concentrations and impacts of these pollutants to which people are exposed in the city. Due to the above stated significance, we investigated first time continuous BC and PM2.5 concentrations including PM10 at seven different locations like residential, transport, industries, open waste burning and brick kilns clusters of the city during the period of January to December 2015 to understand the pollution load, seasonal variability, climatology, source of apportionment and trajectory pathway of these air pollutants at Patna which makes it most polluted city in IGP. These seven locations were designated to inspect spatial disseminations of air quality across the city (Fig. 1). To our information, it is the first broad exercise of extensive spatial and temporal diversity of BC, PM2.5 and PM10 pollution for this town. This study will be important to design the control measures for Patna as well as urban sites of IGP and it builds upon related work on anthropogenic air pollution exposure and health effects in this community.

2.1. Topography, meteorology and emission Patna (25.35°N, 85.12°E and 53 m ASL) is located on the southern bank of Ganga River in the Bihar State of IGP. The climate of the city is ‘subtropical humid’ and classified as ‘Cwa’ type with high temperatures of 40–45 °C in pre-monsoon season while lowest of 13–15 °C during in winter period. A meteorological station was installed at Mithapur colony to capture meteorological data during the same period. The highest level of average relative humidity was observed in the month of July due to precipitation and minimum in March due to high temperature (Fig. 2a). The average monthly maximum vertical mixing height and wind speed was observed during the winter and pre-monsoon season (Fig. 2b). The Wind speed, direction, and mixing height have a significant role in the dispersion of air borne materials and influence air quality of the area. The common emissions sources of BC, PM2.5, and PM10 include fossil and biofuels combustion, industrial processes, emissions from brick kilns and biomass burning which are major sources of air pollutants in Patna. However, particularly for PM10, road dust and construction activities were important non-combustion sources.

2.2. Instrumentation and data analysis Continuous measurements of BC, PM2.5, and PM10 were carried out at Patna to detect spatial distributions of air quality across the city during January to December 2015 (Fig.1). With this objective, we selected seven monitoring sites: Mithapur colony (25° 35′ 28.20″ N, 85° 08′ 24.57″ E), NH-30 (25° 39′ 33.57″ N, 84° 57′ 31.82″ E), Shimli Nabab Ganj (25° 35′ 15.16″ N, 85° 15′ 00.97″ E), Station Road circle (25° 36′ 21.79″ N, 85° 08′ 28.71″ E), Indira Gandhi Institute of Medical Sciences (25° 36′ 41.62″ N, 85° 05′ 21.62″ E), Adampur (25° 35′ 12.65″ N, 85° 02′ 52.88″ E) and Disha halt Railway Station (25° 38′ 44.49″ N, 85° 05′ 31.77″ E). These observational sites were surrounded by the inhibited colonies and close to national highways (NH-30 and 431). The realtime measurements of BC, PM2.5, and PM10 were recorded at five-minute intervals by using seven channel micro-aethalometer (Model AE42) from Magee Scientific, USA (Hansen et al., 1984) and portable scattering Nephelometer (pDR-1500), Thermo Scientific, USA (Gordon et al., 2014) at a height of ~7 m above ground level, respectively. BC concentrations was recorded from the attenuation of light transmitted at seven wavelengths viz., 370, 470, 520, 590, 660, 880 and 950 nm through its filter tape on which aerosol mass is deposited by a uniform air flow rate. BC emission from fossil fuel provides spectral absorption peaks at 830 nm while other aerosol species have insignificant absorption at this wavelength. So, the channel 880 nm close to this peak was considered as the standard channel for BC measurement. Details about the specification of micro-aethalometer, data analysis, restrictions, uncertainties, budget error and correction necessities were described in several earlier studies (Hansen et al., 1984; Babu and Moorthy, 2002; Weingartner et al., 2003; Arnott et al., 2005; Schmid et al., 2006 and reference cited therein) and hence not repeated here. The length of the inlet tube of both instruments was 0.15 m and placed at 0.7 m above the ground to capture emissions from their sources. The flow rate was set at 3 L minwith measurement frequency of 5 min to measure BC. The pDR Nephelometer were calibrated against Arizona Test Dust (ISO 12103-1, Powder Technology, Inc., USA) by the manufacturer before installation at seven locations with an accuracy of 5%. The pDR-1500 was operated at the flow rate of 1.52 L minfor measurement of PM10 and PM2.5 due to high mass concentrations was anticipated at these seven sites. Due to high concentrations of PM2.5 and PM10, cleaning of inlet cyclones of pDR-1500 was performed with deionized water at the end of the weekend (Sunday). During the cleaning period, pDR nephelometer were zeroed out each weekend by using a HEPA filter.


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Fig. 1. Monitoring of BC, PM2.5 and PM10 were carried out at seven location d. MITH: Mithapur Colony, NH-30: National Highway, SHI: Shimli Nabab Ganj, STR: Station Road Circle, IGIMS: Medical College, ADAM: Adampur and DISH: Dish Halth Railway Station (https://www.google.com/maps/d/?hl=en). The circles show brick kilns cluster near the city.

2.3. Source of apportionment Patna receives air from the northwest and west direction during premonsoon period which carries emissions from the open burning of agriculture residue and forest fires and dust (Ramachandran and Cherian, 2008). These emissions can transport to the east direction and will impact on local air quality of neighboring cities. To understand the impacts of emissions from biomass burning, forest fires and dust on local air quality of Patna, eight days' backward trajectories were computed using a HYbrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT4) model (Draxler and Rolph, 2014; Dumka et al., 2013, 2015; Bisht et al., 2015) at an altitude of 500, 1000, 1500 m mean sea level. This made it possible to examine the influence of potential source regions on the observed variability of aerosol absorption properties and the impacts on other surrounding regions. The National Centers for Environmental Prediction/National Center for Atmospheric Research global reanalysis data was used as an input to the model for computing eight days' isentropic air mass backward trajectory. 3. Results and discussion 3.1. Seasonal variation of BC, PM2.5, PM10 The variation of BC, PM2.5, and PM10 is important to see the emission contributions from local, regional and national sources. The daily mass concentration of BC, PM2.5, and PM10 in the proximity of seven selected locations varied from 0.06 to 21.86 μgm−3, 7.03–110.36 μg m−3 and 9.78–148.90 μg m−3 respectively during the whole study period. The monthly average mass concentrations of BC, PM2.5, and PM10 reached

their maximum during winter and pre-monsoon season while they fell to their minimum during the monsoon period (Fig. 3). The monthly average concentrations of BC, PM2.5, and PM10 are summarized in Table 1. The mean mass concentration of BC was at its highest level (13.92 ± 3.48 μg m−3) in the winter and lowest level (5.83 ± 1.90 μg m−3) in the monsoon season due to rainfall. The highest level (21.86 μg m−3) of BC was observed in the month of January due to brick kilns manufacturing, biomass burning for heating purpose and lower planetary boundary layer (PBL) depth as well as mixing height (Fig. 3a). The seasonal mass concentration of BC were 13.92 ± 3.48 μg m− 3 in the winter (Dec–Feb), 9.65 ± 3.0 μg m−3 in the pre-monsoon (March–May), 5.83 ± 1.90 μg m−3 in the monsoon (June–Sept.) and 7.86 ± 3.66 μg m−3 in the post-monsoon (Oct.–Nov.). Similarly, the mean mass concentration of both PM2.5 and PM10 reached its highest point at 68.86 ± 18.83 μg m−3 and 108.13 ± 21.49 μg m−3 in the winter (Fig. 3b) and lowest point at 37.83 ± 11.27 μg m-3 and 62.82 ± 14.81 μg m-3 in the monsoon period, respectively. The seasonal mean concentrations of PM2.5 (PM10) were 68.86 ± 18.83 μg m−3 (108.13 ± 21.49 μg m−3) in the winter; 64.62 ± 18.76 μg m−3 (93.45 ± 18.42 μg m−3) in the pre-monsoon; 37.83 ± 11.27 μg m− 3 (62.82 ± 14.81 μg m− 3) in the monsoon and 40.14 ± 16.66 μg m−3 (64.72 ± 22.40 μg m−3) in the post-monsoon. The daily and year mean value of both PM2.5 and PM10 was also compared with the national ambient air quality standard (NAAQS) of India. This comparison demonstrated that about 76.67% of PM2.5 and 87.78% of PM10 data exceeded the threshold limit of NAAQS (http:// cpcb.nic.in/National_Ambient_Air_Quality_Standards.php) in the winter on a daily basis while 46.74% and 36.96% in pre-monsoon season, respectively. The annual average concentration of BC, PM2.5, and PM10 were 9.13 ±4.27 μg m−3, 52.62 ± 21.54 μg m− 3 and 82.03 ± 26.99

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Fig. 2. Meteorological conditions at Patna (a) daily average temperature and humidity at seven sites during the January–December 2015 (b) Daily average mixing height and wind speed during the January–December 2015 and (c) monthly surface wind direction.

μg m−3, respectively. Several researchers have reported that high mass concentrations of particulate matters (BC, PM2.5 and PM10) impair pulmonary, respiratory and lung functions as well as cardiac systems (Wilson and Suh, 1997; Pope, Ezzati, and Dockery, 2009; Gauderman et al., 2007; Zanobetti et al., 2010; Shakya et al., 2016; Ghio and Devlin, 2001; Janssen et al., 2013; Wu et al., 2013). In addition to local emission and meteorological conditions, various factors such as long-range transport, biomass burning, and dust play an important role in the accumulation or dispersion of pollutants at urban sites (Sahu et al., 2016). However, the predominant factors governing the ambient concentrations of pollutants vary from one season to the next. Similar, conditions were also observed at these monitoring sites, and it was noticed that the monthly average concentrations of BC, PM2.5, and PM 10 show clear seasonality with the maximum falling during the winter pre-monsoon period

and minimum falling during the monsoon and post-monsoon seasons. 3.2. Diurnal variability of BC, PM2.5, and PM10 The mass concentration of BC, PM2.5, and PM10 showed strong diurnal variability with two peaks occurring during the morning (06:00– 10:00 h) and evening (17:00–21:00 h). These peaks corresponded with times during which business activities, cooking, and traffic reach their highest levels. The average diurnal concentrations of BC, PM2.5, and PM10 are strongly influenced by the diurnal changes in meteorology, anthropogenic emissions, and PBL depth in the city. The average diurnal plots of BC, PM2.5, and PM10 for different seasons are shown in Fig. 4. In all seasons, the lowest concentrations of BC (0.06 μg m−3), PM2.5 (7.03 μg m− 3) and PM10 (9.78 μg m− 3) were recorded during


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Fig. 3. Average daily time series variations (a) Daily BC concentrations at seven sites in Patna during the January–December 2015 (b) Daily PM2.5 and PM10 with daily and yearly National Ambient Air Quality Standards.

the afternoon hours (13:00–17:00 h). The morning and evening peaks of BC, PM2.5, and PM10 appear to be associated with heavy traffic, residential cooking and other anthropogenic activities. During the morning peak hours, the mass concentrations of PM2.5 (BC) were about 103.61 ± 15 μg m− 3 (18.31 ± 6.48 μg m− 3), 81 ± 20 μg m−3 (15.32 ± 7.21 μg m−3), 53 ± 22 μg m−3 (5.61 ± 1.66 μg m−3) and 73 ± 16 μg m−3 (7.88 ± 2.26 μg m−3) during the winter, pre-monsoon, monsoon and post-monsoon seasons, respectively. The morning concentrations of PM10 were highest (148 ± 15 μg m−3) in winter and lowest (11.81 ± 3 μg m− 3) in the monsoon season. During the evening hours, the mass concentrations of PM2.5 (BC) were about 95.2 ± 30 μg m− 3

Table 1 Monthly average value of PM2.5, PM10, BC and meteorological parameters at Patna during January to December 2015. Month

BC PM2.5 PM10 (μgm) (μgm) (μgm)

Temp Wind (C) Speed (m/s)

RH (%)

Rainfall Mixing (mm) layer depth (m AGL)

January February March April May June July August September October November December

16.50 11.57 11.66 10.63 6.70 5.14 5.89 5.17 7.25 5.98 9.80 13.45

16.98 19.71 25.02 31.54 36.98 38.14 33.67 32.4 31.5 26.34 22.62 18.45

80.3 70.42 59.06 43.2 52.38 58.58 77.61 80.16 76.23 72.45 70.63 74.16

11.8 0.0 18.2 8.0 1.4 74.5 251.0 172.7 61.8 9.4 0.0 0.0

84.83 67.52 79.87 67.17 46.91 35.81 41.23 35.91 39.06 30.55 50.06 54.50

110.88 113.11 109.53 87.06 83.56 62.91 63.75 61.41 64.13 54.14 75.66 102.50

2.47 2.43 3.34 3.63 3.31 4.83 5.1 3.39 2.81 2.04 2.09 2.02

203 256 423 714 847 979 620 471 436 305 235 214

(16.25 ± 6.15 μg m−3), 61.3 ± 25.2 μg m−3 (6.51 ± 1.26 μg m−3), 85 ± 28.1 μg m−3 (8.97 ± 2.12 μg m−3) and 112.97 ± 30 μg m−3 (20.3 ± 5.31 μg m− 3) during the pre-monsoon, monsoon, post-monsoon and winter seasons, respectively. The evening concentrations of PM10 shows similar trend like morning hours. With the exception of the emissions from various sources, the mixing height and seasonal change in PBL depth is one of the main causes for dissimilar diurnal variations. During the winter season, the higher concentrations of morning hours compared to other seasons could be due to compressed PBL and additional emission sources such as burning of agriculture residue and shrubs used for heating purposes. The early morning peak hours also coincides with slow winds favoring the accumulation of pollutants. On the other hand, the low concentrations were recorded in the afternoon hours at periods due to reduced traffic activities, deeper PBL depth, high mixing heights and stronger winds. However, the concentrations of (BC, PM2.5, and PM10) in the night hours are high due to the formation of the shallow nocturnal boundary layer (NBL) and stagnant wind, leading to the accumulation of pollutants. In the monsoon period, rainfall, clear windblown, insignificant brick kilns manufacturing activities and limited biofuel burning lead to minimum concentrations of air pollutants. The highest variances in peak amplitudes of BC and both PM2.5 and PM10 fall between the winter and pre-monsoon seasons, suggesting the influence of weaker and strong vertical mixings, respectively. Emissions from brick kilns sources show a clear dependence on time and season which alter both pre-monsoon and winter concentration of BC, PM2.5, and PM10. The diurnal ratio of PM10/PM2.5 were 1.98– 3.87 μg μg m−1, 2.1–4.1 μg μg m−1, 2.1–3.9 μg μg m−1 and 1.3–2.7 μg μg m-1 during the winter, pre-monsoon, monsoon and post-monsoon seasons, respectively. The PM10/PM2.5 ratio also coincides with both the morning and evening, suggesting the predominance of the fresh emissions from vehicles and brick kilns.

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Fig. 4. Diurnal variations of (a) BC, (b) PM2:5 and (c) PM10 concentrations observed at seven locations at Patna during the January–December 2015.

3.3. Effect of weekends on BC, PM2.5 and PM10 emissions Earlier research shows that the weekly cycle of aerosols and gases are site dependent due to different habitats and atmospheric conditions. Weekly cycles are useful for studying the amount of anthropogenic emission during different days of a week. For example, ~25%, 15%, and ~24% reductions in BC, aerosol optical depth (AOD) and composite aerosol mass concentration were observed in weekends compared to weekdays in Bangalore (Satheesh et al., 2011). Similarly, the impact on BC, PM2.5 and PM10 concentration due to changes in anthropogenic

activities (traffic movement and industrial activities) around seven locations from weekdays (Monday to Friday) to the weekend (Saturday and Sunday) are shown in Fig. 5(a–c). The average mass concentrations of BC, PM2.5 and PM10 during weekdays (weekends) were 9.98 ± 3.34 (8.64 ± 2.1) μg m−3, 90.63 ± 15 (65.39 ± 17.84) μg m−3 and 142.36 ±8.16 (130.82 ± 26.71) μg m−3 during the winter season while 4.01 ± 1.21 (2.54 ± 1.2) μg m−3, 28.09 ± 11 (19.24 ± 8.12) μg m−3 and 90.45 ± 8.15 (70.25 ± 16.21) μg m−3 during the monsoon season. The season wise average of BC, PM2.5, and PM10 during the weekdays and weekend are also shown in Fig. 5(d–f). The highest weekdays/


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Fig. 5. Diurnal variations of concentrations of BC, PM2.5 and PM10 at seven locations showing seasonal variability at Patna during the January–December 2015.

weekend differences in mass concentration of BC and PMs were observed during rush hours and lowest during afternoons. The reductions of BC PM2.5 and PM10 during the weekend clearly show the role of

decreased emissions mainly from the industrial and vehicular exhaust. The annual average percentage reductions of PM2.5, BC, and PM10, were 32%, 30% and 18% during the weekends. However, the highest

Fig. 6. Scatter plots showing the relations between BC with PM2.5 and PM10.

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reductions of PM2.5, BC, and PM10 were 31%, 36% and 22% during the monsoon season while lowest in the winter season (27%, 13%, and 8%, respectively). The diurnal variations for the study period reveal low BC concentration during weekends, especially on Saturday at IGIMS (50%) and NH-30 (40%). The reduction in the mass concentration of PM2.5 and PM10 during weekends was also observed at Udaipur, Mexico City and Bangkok (Yadav, Sahu, Beig, Tripathi, and Jaaffrey, 2017; Stephens et al., 2008; Leong et al., 2002).

3.4. Correlation between BC, PM2.5 and PM10 The correlation between BC, PM2.5 and PM10 are also examined to understand how emissions interact with each other. The correlation between daily concentrations of PM2.5 and PM10 and their dependence on the mixing ratios of BC are shown in Fig. 6. The least squares regression analysis was performed, and the concentrations of PM2.5 and PM10 show good correlations with BC (R 0.5225 with p b 0.001); this suggests that natural sources, transport of aged pollutants and windblown dust are major contributors to pollution. This interrelationship also underlines that agriculture field biomass burning during the winter and pre-monsoon are an important emission source, leading to high levels of air pollution and might contribute high concentrations at the sampling sites due to high pressure, intensive photochemistry, and lack of an intensive wet removal process (Zhang and Kim Oanh, 2002).

Winter

Monsoon

3.5. Role of long-range transport pathway It was observed that the backward trajectories are coming at Patna from the high biomass/crop residue burning areas of Northwest Countries (Pakistan and Afghanistan), northern region of India (Punjab, Haryana and Uttar Pradesh) and dust aerosol from west (Thar desert) during the winter and pre-monsoon season that raise the surface concentration of BC, PM2.5 and PM10 to 21.86 μg m−3, 110.36 μg m−3 and 148.90 μg m−3 despite the strong convective mixing during this season (Fig. 7). However, India's National Green Tribunal has banned crop residue burning in the country, though the practice continues in the northwest, north and central regions of India. Similar findings were also reported at Peshawar, Pakistan (Khan et al., 2015), Iran (Shahsavani et al., 2012) and Beijing, China (Zhao et al., 2009). Wheat is usually harvested in months of May–June and leaves behind crop residue in the field. These residues are subjected to open burning to clear the field for the next crop, which produces plenty of biomass aerosols over the region during the dry season (Venkataraman et al., 2006; Singh et al., 2014). In the summer/pre-monsoon period, the Patna is strongly affected by the transport of dust from the desert regions of Iran, Afghanistan, Pakistan and Thar desert. In the monsoon season, backward trajectories show transport of clean air from the India Way of Bengal Sea and the Arabian Sea due the south and southeast winds (see Fig. 2c). The reduction of BC, PM2.5, and PM10 were observed about of 36%, 31% and 22% during monsoon compared to other season due to cleaner air, rainfalls

Pre-monsoon

Post-monsoon

Fig. 7. Back trajectories at 500, 1000 and 1500 m level for different seasons (winter, pre-monsoon, monsoon and post-monsoon) at Patna during the January–December 2015.


and negligible contribution from the brick kilns and biomass burning sources. Interestingly, the lowest values of BC, PM2.5, and PM10 were observed in the month of July because of highest monthly rainfall of 251 mm (see Table 1). During the monsoon period, the cumulative rainfalls of around 560 mm were recorded at Patna. In the post-monsoon season, BC, PM2.5 and PM10 concentrations were moderate, which could be due to the prevailing mixed air. The concentrations of BC, PM2.5, and PM10 in the current study are also compared with other recent research reported for BC and PMs in India and are summarized in Table 2. The mass concentrations of BC in the current study are more than other towns of IGP, such as at Gorakhpur (Vaishya, Singh, Rastogi, and Suresh, 2017), Balia (Tiwari et al., 2016b) and Agra (Safai et al., 2008). The concentrations of PM2.5 were higher than Delhi (Tiwari et al., 2015), Lucknow (Pandey et al., 2012), Kanpur (Sharma and Maloo, 2005; Mallik et al., 2014), Patiala (Awasthi et al., 2011) and Udaipur (Yadav et al., 2017) while PM10 was lower than Kanpur (Sharma and Maloo, 2005; Mallik et al., 2014), Delhi (Tiwari et al., 2015), Lucknow (Pandey et al., 2012) and Patna (Tiwari et al., 2016a) while higher than Udaipur (Yadav et al., 2017; Yadav et al., 2014), Patiala (Awasthi et al., 2011) and Balia (Tiwari et al., 2016b).

3.6. Influence of brick kiln emissions on local air quality Brick kilns are recognized as a major polluting industry, contributing to air pollution in developing countries (Weyant et al., 2014; Skinder et al., 2014; Le and Oanh, 2010). Brick making is a highly energy-intensive process and consumes coal and large quantities of biomass fuels. It is also an unorganized sector due to limited control measures, old technologies and type of fuels lead to enormous pollutant emissions like particulate matters and other gaseous pollutants (sulphur dioxide, carbon monoxide, etc.) at the local and regional scales (Pariyar et al., 2013; Motalib et al., 2015). The brick manufacturing activities start mainly during the winter and pre-monsoon season and close in monsoon season in Patna, and about 421 brick kilns are operational around the city (Fig. 8). To understand the influence of the particulate matter on local air quality, the concentrations of BC, PM2.5, and PM10 were also measured near two brick kilns clusters during the January–December 2015 (see Fig. 1). The mass concentrations of BC, PM2.5 and PM10 were observed significantly high during winter and pre-monsoon season at all monitoring sites at brick kiln clusters. The concentrations of BC, PM2.5, and PM10 at brick kilns clusters were comparatively higher than other monitoring sites during the winter and pre-monsoon season. The daily concentrations of BC, PM2.5 and PM10 in the proximity of two cluster and five other locations varied from 0.06 to 17.79 μg m−3; 8.10–111.08 μg m−3 and 11.25–148.90 μg m−3 at brick kilns cluster while 0.08 to 21.86 μg m−3; 7.03–96.01 μg m−3 and 9.78–129.48 μg m−3 at other sampling sites, respectively across the entire period (Fig. 9). The daily mean concentrations of BC, PM2.5 (PM10) at both clusters and other five sampling sites were 8.69 ± 4.08 μg m−3 and 50.13 ± 20.56 μg m−3 (78.14 ± 25.76 μg m−3) at the

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kiln cluster and 7.42 ± 3.48 μg m−3 and 43.59 ± 17.87 μg m−3 (67.95 ± 22.42 μg m−3), respectively during the whole monitoring period. It should be noted that the daily mean concentrations of these pollutants are much lower than all other sampling sites in comparison to the brick kilns cluster. In December/January, during peak brick kilns manufacturing activities period the mean values of BC, PM2.5 and PM10 were 15.75 μg m−3, 80.60 μg m−3 and 104.05 μg m−3, respectively. Later, from April onwards, the levels of these pollutants decreased gradually (6.63 μg m−3, 41.23 μg m−3 and 65.63 μg m−3, respectively) and reached normal levels at the kiln cluster. The periodic mass concentrations of BC were 13.27 ± 3.34 μg m−3 in the winter; 9.19 ± 2.86 μg m−3 in the pre-monsoon; 5.55 ± 1.81 μg m−3 in the monsoon and 7.48 ± 3.49 μg m−3 in the post-monsoon at brick kilns cluster and 11.32 ± 2.83 μg m−3 in the winter; 7.85 ± 2.44 μg m−3 in the pre-monsoon; 4.74 ± 1.54 μg m−3 in the monsoon and 6.39 ± 2.98 μg m−3 in the post-monsoon at other sampling sites (Fig. 9a). Correspondingly, the mean concentration of both PM2.5 and PM10 was recorded as a maximum of 65.65 ± 18.08 μg m−3 and 102.86 ± 20.63 μg m−3 in the winter (Fig. 9b) and a minimum of 36.02 ± 10.72 μg m− 3 and 59.44 ± 14.14 μg m− 3 in the monsoon season at brick kilns cluster monitoring sites in comparison to other monitoring sits, respectively. The periodic mean concentrations of PM2.5 (PM10) were 65.65 ± 18.08 μg m−3 (102.86 ± 20.63 μg m−3) in the winter; 61.55 ± 17.87 μg m−3 (89.0 ± 17.54 μg m−3) in the pre-monsoon; 36.09 ± 10.72 μg m−3 (59.83 ± 14.10 μg m− 3) in the monsoon and 38.23 ± 15.87 μg m− 3 (61.64 ± 21.33 μg m− 3) in the post-monsoon at brick cluster while 57.08 ± 15.72 μg m−3 (89.61 ± 17.94 μg m−3) in the winter; 53.51 ± 15.53 μg m−3 (77.39 ± 15.25 μg m−3) in the premonsoon; 31.32 ± 9.32 μg m−3 (52.03 ± 12.26 μg m−3) in the monsoon and 33.24 ± 13.80 μg m−3 (53.60 ± 18.55 μg m−3) in the postmonsoon at other monitoring sites, respectively. It merits noting that about 29.32% of PM2.5 and 23% of PM10 concentrations greater than the NAAQS limit in the winter on a daily basis at brick kilns cluster while 21.10% and 15.34% at other monitoring sites, respectively. Several researchers have reported that brick kilns clustered on the outskirts of cities pollute the air of the cities such as New Delhi (Guttikunda and Goel, 2013), Kathmandu (Tuladhar and Raut, 2002) and Dhaka (Begum et al., 2010). Emissions from brick kilns also affect the respiratory systems of on-site workers (Kaushik et al., 2012; Zuskin et al., 1998). The average ratio of PM10/PM2.5 was ~2.0 μg μg m during the brick manufacturing period registered higher loadings of these particles. Clearly, the ratios of PM10/PM2.5 were found to be significantly higher (0.87–4.48 μg μg m−3) than other monitoring sites during the brick manufacturing period. It is therefore clear that brick kilns activities significantly influence local air quality and these need to be addressed properly.

3.7. Estimation of total emissions of BC, PM2.5, and PM10 Based on the field measurements and secondary data resources, an inventory was prepared for total BC and PM (PM2.5 and PM10). In

Table 2 Comparison of real time mass concentrations of BC, PM2.5 and PM10 at Patna location with other locations of India. Location

Sampling period

BC (μg m)

PM2.5 (μg m)

PM10 (μg m)

References

Patna Udaipur Gorakhpur Balia Patna Delhi Udaipur Lucknow Agra Kanpur Patiala

January–December 2017 2011–2012 2013–2015 June to August 2014 2013–2014 2011–2013 2010–2011 2007–2009 December 2004 2002–2003, 2012–2013 2007–2010

21.86 ± 3.48 – 19 ± 14 4.03 – – – – 10.5–17.4 – –

111.08 42 ± 17 – 34.7 – 118 ± 81 41 ± 11 101 ± 22 – 95 57 ± 2

148.1 114 ± 31 – 43.7 192 ± 132 232 ± 131 108 ± 27 204 ± 26 – 281 97 ± 2

Present study Yadav et al., 2017 Vaishya et al., 2017 Tiwari et al., 2016b Tiwari et al. 2016a Tiwari et al. 2016a Yadav et al., 2014 Pandey et al., 2012 Safai et al., 2008 Sharma and Maloo, 2005; Mallik et al., 2014 Awasthi et al., 2011

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Fig. 8. Brick kilns are operational around the Patna. The red dots indicate brick kiln stacks, mapped from the Google Earth open source files for 2015. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

Patna, emissions from vehicles, brick kilns, industries, diesel generator sets and municipal waste burning, road dust are the key sources of air pollution. Based on the current emission estimates, we estimated possible mass concentrations of BC, PM2.5, and PM10 for the year 2015 (Fig. 10). BC emissions were estimated by using household's data, fuel use, industry, vehicle data and national emissions to understand the current sectoral BC emission (Reddy and Venkataraman, 2002a, 2002b). The ASIF concept was applied for calculation of exhaust discharge from the transport sector. That concept breaks transportation emissions into activities (A), modal share (S) in vehicle-km travelled/day, vehicle energy intensity (I) use per kilometer, and an emission factor (F) defined as the mass emitted per vehicle (Schipper et al., 2000). The daily average travel distance for different vehicles were defined as follows: 45 km for cars, 160 km for buses, 100 km for heavy trucks, 80 km for cabs, 140 km for automobiles. The Central Pollution Control Board has established emission norms for these vehicles, and same emission factors were considered during the assessment of emission. In so doing, it was assumed that the normal speeds of vehicles fell below 20 kmph in the city due to overcrowding and traffic jams. The emissions from industries/brick kilns were determined on the basis of fuel (coal, electricity, and biofuel) intake (4808 kg/day). The total domestic emissions were calculated based on the biofuel/CNG used for cooking and crop residue burning at homes for heating. The pollutants are released from an individual source or an area, depends on the local canopy and meteorological conditions such as wind speed, wind direction, pressure, temperature and moisture content, interact with other pollutants. After the interaction, the pollutants are either deposits on to a surface or lingers in the air in the form of pollution. For Patna's air quality analysis, the Atmospheric Transport Modeling

System dispersion model was utilized, using local meteorological data. This model allows multi-pollutant analysis in which each of the primary emissions are modeled separately due to the difference in their physiochemical properties and aggregated for total particulate matter over Patna. The ambient concentrations were modeled for the base year 2015, along with estimated contributions of various sources for BC, PM2.5 and PM10 for the projected from 2015 to 2040 (Fig. 11). Under the business as usual scenario, the ambient mass concentrations of BC, PM2.5 and PM10 are expected to get worse- the modeled annual average concentrations of BC in 2020, 2030 and 2040 will be at least 16.56%, 15.01%, and 17.15% higher than base year, 2015, respectively. Similarly, the annual average mass concentrations of PM10 will be 43.66%, 34.49% and 37.58% more than base year, 2015 while PM2.5 will be 50.21%, 47.16%, 50.58% higher in 2020, 2030 and 2040 from the base year, 2015, respectively. The residential and transportation sector remains the dominant source of emissions followed by industries (including brick kilns) and open biomass burning. These results stress upon the formulation of mitigation policies that can reduce local and climate impacts and save millions of lives in the process. An obvious question is what kind of policies would be needed to capture these benefits. To control emissions from different source, we need some technological interventions such as (a) replacement of the FCK technology with zigzag technology; relocation of southeast brick kilns cluster from the current location and replacement of the kiln fired clay brick kilns with brick made of alternate materials like fly ash, (b) introduction of Bharat-4 fuel standards and alternate fuelCNG for the in-use public and para-transit system (c) improvement in the public and para-transit operations system, increase share of nonmotorised vehicles and implementation of dedicated bus corridors (d)


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Fig. 9. The mass concentrations of BC, PM2.5 and PM10 at two brick kilns clusters and compared with other five monitoring sites of Patna during January–December 2015.

promotion of renewable energy in place of DG sets, (e) 100% LPG in both rural and urban households and (f) reduction of the road dust loading by using heavy or light-duty vehicles with vacuum cleaners to suck up dust from the roads. 4. Conclusions Local emission and long-range transport of pollutants from northwest and northern directions are key sources of pollution in the Patna. The annual concentrations of BC, PM2.5, and PM10 show clear seasonality

with the maximum in the winter and minimum in the monsoon season. The highest level (21.86 μg m−3) of BC was observed in the month of January due to brick kilns manufacturing, biomass burning for heating purpose and lower planetary boundary layer (PBL) depth as well as mixing height. The seasonal mass concentrations of BC were 13.92 ± 3.48 μg m−3 in the winter, 9.65 ± 3.0 μg m−3 in the premonsoon, 5.83 ± 1.90 μg m−3 in the monsoon and 7.86 ± 3.66 μg m−3 in the post-monsoon respectively. Similarly, the seasonal mean concentrations of PM2.5 (PM10) were 68.86 ± 18.83 μg m−3 (108.13 ± 21.49 μg m−3) in the winter; 64.62 ± 18.76 μg m−3 (93.45 ± 18.42

Fig. 10. Total sector wise emission of PM2.5 and PM10 in Patna during the January–December 2015.

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Fig. 11. Business as Usual Emission Projection of (a) BC mass concentrations, (b) PM2.5 and (c) PM10 from different sources up to 2040 in the Patna.

μg m−3) in the pre-monsoon; 37.83 ± 11.27 μg m−3 (62.82 ± 14.81 μg m−3) in the monsoon and 40.14 ± 16.66 μg m−3 (64.72 ± 22.40 μg m−3) in the post-monsoon. The research also revealed that about 76.67% of PM2.5 and 87.78% of PM10 data exceeded the threshold limit of NAAQS in the winter on the daily basis while 46.74% and 36.96% in pre-monsoon season, respectively. All of the sampling sites were influenced by the flow of clean air due to the prevailing south and southeast winds during the monsoon season. The diurnal distributions of BC, PM2.5, and PM10 showed two prominent peaks (morning and evening). The annual percentage reductions of BC, PM2.5, and PM10 were observed 32%, 30% and 18% during

weekends. The least squares regression analysis of PM2.5 and PM10 show good correlations with BC (R 0.5225 with p b 0.001) which represents major contribution from natural sources, transport of aged pollutants and windblown dust. The trajectory model suggested that the major source of BC, PM2.5, and PM10 during winter and pre-monsoon season from the Northwest Countries (Pakistan and Afghanistan) and northern region of India despite the strong convective mixing during this season. The ratios of PM10/PM2.5 observed during the brick manufacturing period were significantly higher than (0.87– 4.48 μg μg m−1) other monitoring sites. Under the business as usual scenario, the ambient mass concentrations of BC, PM2.5 and PM10 are


expected to get worse- the modeled annual average concentrations of BC in 2020, 2030 and 2040 will be at least 16.56%, 15.01%, and 17.15% higher than base year, 2015, respectively. Similarly, the annual average mass concentrations of PM10 will be 43.66%, 34.49% and 37.58% more than base year, 2015 while PM2.5 will be 50.21%, 47.16%, 50.58% higher in 2020, 2030 and 2040 from the base year, 2015, respectively. The residential and transportation sector remains the dominant source of emissions followed by industries (including brick kilns) and open biomass burning. As an outcome, there remains much to understand in how highly polluted communities in the developing cities can affect local and regional air quality. Acknowledgment Authors are grateful to Prof. M.S. Ashraf, Mr. Abhishek Kumar, and Mr. Afaq Ahmad, for their help and encouragement during the field observations. We are also thankful to M&G Analyser, Pune and Mr. Hemanth Krishna for their support during the measurement. 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