Bull Environ Contam Toxicol (2013) 91:704–710 DOI 10.1007/s00128-013-1121-5
Seasonal Air Quality Profile of Size-Segregated Aerosols in the Ambient Air of a Central Indian Region Dhananjay K. Deshmukh • Manas K. Deb Devsharan Verma • Jayant Nirmalkar
•
Received: 6 April 2013 / Accepted: 1 October 2013 / Published online: 16 October 2013 Ó Springer Science+Business Media New York 2013
Abstract Seasonal distribution trends of size-segregated aerosols i.e. submicron (PM1), fine (PM2.5) and coarse (PM2.5–10) and their relationship with meteorological variables employing correlation analysis were studied in the ambient air of central India from July 2009 to June 2010. The annual mean concentrations of PM1, PM2.5 and PM2.5–10 were found to be 65.7, 135.0 and 118.5 lg m-3, respectively. The annual mean PM2.5 concentration is three times higher than the National Ambient Air Quality Standards of India (NAAQS). Higher concentrations of PM1, PM2.5 and PM2.5–10 were found during winter due to enormous biomass burning especially during night time due to the use of combustible goods like fire wood and dung cake in the open space by the peoples to keep themselves warm and lower concentrations were observed during monsoon when there were high precipitations. PM2.5 showed high positive correlation with PM1 (r = 0.69) and moderate correlation with PM2.5–10 indicating that variation in PM2.5 mass is governed by the variation in PM1 mass or vice versa. Keywords Size-segregated aerosols Seasonal variation Meteorological variables Correlation analysis
The world is facing environmental crisis due to severe air pollution as result of rapid urbanization and industrialization, bloom in population and economic expansion
D. K. Deshmukh (&) M. K. Deb D. Verma J. Nirmalkar School of Studies in Chemistry, PT. Ravishankar Shukla University, Raipur 492010, Chhattisgarh, India e-mail: [email protected]; [email protected]
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(Mohanraj et al. 2011; Pipalatkar et al. 2012; Yang et al. 2012). Deteriorating air quality is a major problem faced by millions of urban inhabitants as most Indian cities are highly polluted with particulate matter (PM) concentrations well above the recommended limits of National Ambient Air Quality Standards of India (Das et al. 2006; Pandey et al. 2012). The effects of atmospheric PM on human health fundamentally depend on particle size. The sources and potential health effects of PM1 (Da \ 1.0 lm), PM2.5 (Da \ 2.5 lm) and PM2.5–10 (2.5 lm \ Da \ 10 lm) are very diverse. Particles with a size of above 2.5 lm are deposited in the nose or upper respiratory tract, while smaller particles i.e. PM1 and PM2.5 travel deep into the lungs with the potential to penetrate tissues and undergo interstitialization (Klejnowski et al. 2012). Analysis based on atmospheric aerosols estimates that the ambient air pollution causes *5 % of trachea, bronchus and lung cancer, *2 % of cardio-respiratory mortality and *1 % of respiratory infection mortality, globally (WHO 2005). In the light of aforesaid significance, several investigators have studied the mass concentration and seasonal variation of atmospheric aerosols in Indian region (Tiwari et al. 2009; Pandey et al. 2012) and particularly in central India (Deshmukh et al. 2010, 2012) and suggested that PM2.5–10 are mainly due to the re-suspension of soil particles, whereas PM1 and PM2.5 is a mixture of primary and secondary aerosols emitted from the variety of anthropogenic activities. The study was conducted in Durg (20°230 –22°020 N and 80°460 –81°580 E), a rapidly growing industrial city covering an area of *137 km2, from July 2009 to June 2010. Durg (Fig. 1) has a population of *0.6 million as per the census of India in 2011 (Deshmukh et al. 2012). The sampling site was located in front of a Government College of Science, in an area encompassed by educational and
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research institutes. The closest highway with significant road traffic is *100 m away from the sampling site having high volume of traffic –mostly consisting of diesel-powered buses and trucks, two-stroke motorcycles and threewheelers. The average daily traffic volume is *2,050–2,120 vehicles per hour. The major railway station is located towards the east direction at a distance of *500 m from the sampling point having a frequency of *160 trains per day. Durg and its surrounding have different kinds of industries like steel, power, cement and iron foundries, which are located at radial distances of *20–50 km. The study area is also affected by high agricultural activities in the rural zones. Therefore, wood and biomass burnings all over the year in the nearby villages are also the significant contributory sources of aerosols in Durg (Deshmukh et al. 2010). Consequently, it is now a serious concern to control the PM concentration in the central India region and the seasonal variations of sizesegregated PM and their relationship with the meteorological variables are needed to be known. Evaluation of relationship of PM1, PM2.5 and PM2.5–10 with meteorological variables will be very useful to understand the role of aerosols in urban weather and climate variability. Therefore, the present study was focused mainly on the seasonal distribution of PM1, PM2.5 and PM2.5–10 aerosols, and their relationships with prevailing meteorology during the study period in Durg, India.
Materials and Methods Aerosol sampling was performed on the terrace of a double-storied building at a height of *15 m above the ground level in a location free from obstruction to minimize the external effects of surrounding barriers, i.e. obstacles like huge trees or buildings which prevent the free movement of air. Andersen aerosol sampler (Model no. TE 20-800, USA; flow rate 28.3 ± 0.3 L min-1) was used for collection of size-segregated PM. Anderson sampler separated particles into nine size fractions according to
Fig. 1 Location of sampling site in Durg, India
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the following equivalent aerodynamic cutoff diameter, 0.0–0.4 lm (stage 1), 0.4–0.7 lm (stage 2) 0.7–1.0 lm (stage 3), 1.0–2.5 lm (stage 4), 2.5–3.3 lm (stage 5), 3.3–4.4 lm (stage 6), 4.4–5.8 lm (stage 7), 5.8–9.0 lm (stage 8) and 9.0–10 lm (stage 9). PM1, PM2.5 and PM2.5–10 particles presented the sum of particles of stage 1–stage 3, stage 1–stage 4 and stage 5–stage 9, respectively. Samples were collected on glass fiber filters (Whatman 41; diameter 81 mm). Each sampling was started at 06:00 h local time and carried for 24 h twice a week. Subsequently to the sampling, the whole sampler with samples loaded in different filters was covered with polyethylene bag and transported to the laboratory for gravimetric mass analysis. The filters were unloaded in a clean room, sealed in plastic bags and were kept in portable refrigerators and transported to the weighing room for gravimetric analysis. The limit of detection (LOD) of cascade impactor ranged from 0.5 to 2.0 lg m-3 for the entire size range. All weight measurements were repeated three times to ensure reliability and reading were accepted when the difference was not exceeding 5 lg. Also, 12 field blanks were collected during the entire sampling period to subtract the positive artifacts due to adsorption of gasphase components onto the filter during and after sampling. PM1, PM2.5 and PM2.5–10 concentrations were determined gravimetrically by following the weight difference of the filters before and after the sampling campaign. The filters were kept in desiccators in an environmentally conditioned room at a temperature of 20°C and at 50 % relative humidity for 24 h before and after sampling, and then weighed by an electronic balance (Sartorius CP225D, reading precision 10 lg). All the reported PM mass concentrations were adjusted to account for the field blank concentrations. Spearman correlation analysis was used to analyze the relationship between the concentrations of PM1, PM2.5, PM2.5–10 and meteorological variables. A level of p B 0.01 and 0.05 was considered to be statistically significant in this study. For statistical analysis, Statistical Package for Social Sciences (SPSS) for Windows version 16.0 was used. Weather wise, the whole sampling period is divided into four seasons, i.e. monsoon (July–September), winter (October–January), spring (February–March) and summer (April–June). Meteorological parameters, including temperature, relative humidity, wind speed, wind direction and precipitation were measured every 30 min. Ambient temperature and relative humidity were measured by a respective probe (Vaisala Company, Helsinki, Finland, Model QMH102), and wind speed and wind direction were recorded by the wind monitor (Vaisala Company, Helsinki, Finland, Model QMW110A). Both meteorological instruments were mounted 53 m above ground level.
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Results and Discussion The time-series plot of meteorological variables for the study period is shown in Fig. 2. The annual mean temperature and relative humidity were found to be 26.5°C and 65 %, respectively. The variation of relative humidity showed a steep decrease from winter to summer (67 %– 39.0 %) and then increase in the monsoon (83 %). The annual mean wind speed was found to be 5.2 m s-1. The total rainfall during the study period was monitored to be 1,272.2 mm; 80 % of which (998.3 mm) was observed during the monsoon. The total rainfall during summer was measured to be 166.0 mm. Winter and spring were found to be relatively calm seasons (72.2 % and 66.3 %) as compared to summer (17.9 %) and monsoon (16.8 %). The descriptive statistics on concentrations of PM1, PM2.5 and PM2.5–10 are given in Table 1. The concentration of PM1, PM2.5 and PM2.5–10 varied from 8.6 to 135.7, 26.7 to 263.0 and 36.0 to 250.6 lg m-3 with an annual mean and standard deviation of 65.7 ± 36.9, 135 ± 76.2 and 118.5 ± 32.8 lg m-3, respectively. The annual mean
concentration of PM2.5 is more than 8, 13 and 3 times higher as compared to annual-guidelines of USEPA (15 lg m-3; 2002), WHO (10 lg m-3; 2005) and NAAQS of India (40 lg m-3; 2009), respectively. Concerning the 24 h average of USEPA (35 lg m-3; 2002), WHO (25 lg m-3; 2005) and NAAQS of India (60 lg m-3; 2009) standards for PM2.5, 97 %, 100 % and 83 % of sampling-dates exceeded the respective standards. The concentrations of PM1, PM2.5 and PM2.5–10 found in Durg are compared with other studies conducted in the cities of India and rest part of the world (Table 2). From the comparison of above size-resolved aerosols found in Durg, India with those of the above mentioned studies in the cities of India and all over the world, it is concluded that Durg experiences significantly higher concentrations of PM1, PM2.5 and PM2.5–10 aerosols. This can be regarded as a real environmental problem that is posing a serious risk to the quality of life. Traffic density and combustion activities in governmental building and roadside areas are usually increased during working days. However, in weekend heating in
Fig. 2 Time-series scheme of meteorological variables from July 2009 to June 2010
Table 1 Annual, working day and weekend mean, standard deviation and range of PM1, PM2.5 and PM2.5–10 concentrations in Durg, India from July 2009 to June 2010 PMsa (lg m-3)
Annual (n = 120) Mean ± SDb (Range)
Working day (n = 60) Mean ± SDb (Range)
Weekend (n = 60) Mean ± SDb (Range)
PM1
65.7 ± 36.9 (8.6–135.0)
68.5 ± 22.8 (21.3–135.0)
PM2.5
135.0 ± 76.2 (26.7–263.0)
150.0 ± 79.3 (33.2–263.0)
99.2 ± 50.3 (26.7–181.5)
PM2.5–10
118.5 ± 32.8 (36.0–250.6)
168.5 ± 55.6 (67.5–250.6)
106.0 ± 31.8 (36.0–210.3)
a
Particulate matters
b
Standard deviation
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59.2 ± 29.1 (8.6–92.3)
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governmental building and roadside areas are minimized and traffic density is also relatively decreased as compared to working days. Size-segregated PM showed a general weekly pattern and higher concentrations are found during working day and lower concentrations during weekend (Table 1). The higher concentrations of aerosols on working days than weekends resulted from the influence of local and regional anthropogenic activities such as re-suspension of soil particles by traffic and other anthropogenic activities such as industrial process and building construction. The PM concentrations on the weekends as compared with working days were reduced by 13.6 %, 33.9 % and 37.2 % for PM1, PM2.5 and PM2.5–10, respectively. This reduction could be attributed to the decrease of traffic density due to the official days-off of government institutions, schools and colleges. The effect of decreasing traffic density during weekends did not only cause low exhaust particulate emission, but also reduced emissions generated from tyre wears and re-suspension of soil particles (Latha and Highwood 2006; Lough et al. 2006). This is consistent with the observations of other investigators who have attributed the decrease on PM concentration on weekend days to the decrease in traffic density (Karar et al. 2006; Lonati et al. 2006). Seasonal mean concentrations of PM are given in Table 3. PM1, PM2.5 and PM2.5–10 concentrations in Durg reveal a general trend of maximum concentrations during winter as well as in spring, and minimum concentrations
Table 2 Comparison of mean mass concentrations (lg m-3) of PM1, PM2.5 and PM2.5–10 aerosols in Durg, India and different sites over the world
Sampling area
during summer and monsoon seasons. The concentrations of PM1 and PM2.5 in winter are double the concentrations observed in monsoon and summer. The high concentrations of PM1, PM2.5 and PM2.5–10 during winter are due to the huge agricultural activities and brick kiln emissions which utilize low quality coal and paddy husk (Deshmukh et al. 2010, 2012). During winter, after the crop harvesting, the biomass residues are set on fire on the field to fertilize the land for the coming year. People use combustible materials, such as fire wood and dung cakes, in open space for heating during winter, resulting in the release of large quantities of PM to the atmosphere. The higher dispersion rate of pollutants due to the high temperature is the reason for low aerosol mass during summer season in this area. The seasonal variation of meteorological variables such as temperature, wind speed and precipitation modulate the air quality and play important role in determining the levels, transport and diffusion of aerosols (Bhaskar and Mehta 2010; Sandeep et al. 2013). High relative humidity (95 %), low temperature (5.8°C) and relatively calm wind speed (1.0 m s-1) helped high aerosol mass loading in winter days. In winter during the months of October–January, low temperature favored the gas-to-particle conversion of air pollutants. Additionally, low wind speed and low boundary layer height limited the dispersion of air pollutants in winter (Kothai et al. 2008; Pandey et al. 2012). Conversely, high temperature (39°C) and high wind speed (10.2 m s-1) during the months of April–June in summer caused higher boundary layer height
Sampling period
PM1
PM2.5
Durg, India
Jul 2009–Jun 2010
65.7
135.0
118.5
Present study
Delhi, India
Jan 2007–Dec 2007
97.0
122.0
Tiwari et al. 2009
Ahmedabad, India
Dec 2006–Feb 2006
55.7
Korba, India
Apr 2005–Mar 2006
Kolkata, India
Jan 2006–Dec 2006
69.2
Bilaspur, India
Apr 2005–Mar 2006
53.9
Raipur, India
Apr 2005–Mar 2006
51.2
Kolkata, India
2001–2002
Mumbai, India Shenyang, China
113.8
PM2.5–10
Rengarajan et al. 2011 116.8
Deshmukh et al. 2010
112.6
Deshmukh et al. 2010
71.2 98.3
Reference
Chatterjee et al. 2012
86.8
Deshmukh et al. 2010
178.6
125.5
Das et al. 2006
2005–2006 2004–2005
103.0
107.8 89.0
Kothai et al. 2008 Han et al. 2010
Anshan, China
2004–2005
123.0
99.0
Jinzhou, China
2004–2005
89.0
Han et al. 2010
Lasi, Romania
Jan 2007–Mar 2008
27.7
Arsene et al. 2011
Helsinki, Finland
Oct 2002–Apr 2004
9.0
Lianou et al. 2011
Han et al. 2010
Athens, Greece
Oct 2002–Apr 2004
25.0
Lianou et al. 2011
Chuncheon, Korea
Dec 2005–Aug 2009
31.0
Han et al. 2010
Jinan, China
2006–2007
Zongurdak, Turkey
2006–2007
150.0 28.1
Yang et al. 2012 Akyuz and Cabuk 2009
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708
Bull Environ Contam Toxicol (2013) 91:704–710
Table 3 Seasonal mean and standard deviation of PM1, PM2.5 and PM2.5–10 in Durg, India from July 2009 to June 2010 PMsa (lg m-3)
Monsoon (n = 30) Mean ± SDb
Winter (n = 39) Mean ± SDb
PM1
35.3 ± 16.5
101.3 ± 23.1
76.1 ± 36.0
PM2.5
73.9 ± 33.1
215.0 ± 58.0
113.0 ± 73.7
83.5 ± 25.2
PM2.5–10
83.1 ± 36.7
151.1 ± 37.8
129.9 ± 37.2
115.0 ± 22.2
a
Particulate matters
b
Standard deviation
Spring (n = 20) Mean ± SDb
Summer (n = 30) Mean ± SDb 37.5 ± 13.6
Table 4 Correlation analysis of PMs with meteorological variables in Durg, India from July 2009 to June 2010 PM2.5–10
PM2.5
Tc
PM1
RHd
RFe
VPf
WSg
Monsoon = upper diagonal triangle (n = 30); Winter = lower diagonal triangle (n = 39) PM2.5 PM2.5–10
0.65
a
PM1 Tc RH
d
0.55a
0.26 a
0.12
0.86
0.21
-0.31
-0.21
-0.32 -0.31
-0.23
0.24
-0.20
-0.14
RFe
-0.45b
-0.36
-0.33
-0.08
VPf
-0.49a
-0.32
-0.33
-0.26
WS
g
-0.10
-0.16
-0.38
-0.23
0.30
-0.58
-0.23
-0.53a
0.19
-0.81a
0.10
-0.59
a
-0.18
-0.59a
0.28
-0.62
a
0.22
-0.73a
-0.16
-0.14
-0.17
-0.56a
-0.08 -0.11 0.54a a
0.06
a
0.51
0.11
0.05
-0.09
0.20
0.38
-0.16
0.15
Spring = upper diagonal triangle (n = 20); Summer = lower diagonal triangle (n = 30) PM2.5 PM2.5–10
0.16
PM1
0.50a
T
c
a
RHd RF
e
0.80b 0.12
0.28 0.06
-0.59
-0.58
-0.36
0.27
b
a
0.32 0.07
-0.22 -0.19
-0.26 -0.27
-0.45 -0.11
-0.45b
0.19
-0.12
-0.15
-0.53a
-0.28
-0.08
-0.19
-0.56a
a
-0.54 -0.38
a
-0.50b -0.44b
b
-0.24
-0.10
-0.48
-0.56
-0.48
-0.21
VPf
-0.67a
-0.44b
-0.45b
-0.30
-0.04 0.62a
WSg
-0.78a
-0.51a
-0.62a
-0.51a
0.14
0.58a 0.09
0.16 -0.28
0.12 -0.05 0.41
0.43
Annual = lower diagonal triangle (n = 120) PM2.5
1.00
PM2.5–10
0.47b
PM1
0.69a
0.36
1.00
Tc
-0.63a
-0.60a
-0.57a
1.00
RHd
-0.52a
0.39
-0.66a
-0.33
1.00
RFe
-0.16
0.19
-0.12
-0.10
-0.13
VP
f
WSg a
1.00
b
-0.56
-0.45
-0.66
-0.54a
a
a
1.00
-0.49
-0.39
0.66
0.10
1.00
-0.55a
-0.69a
0.01
-0.13
0.41
1.00
p \ 0.01
b
p \ 0.05
c
Temperature
d
Relative humidity
e
Rainfall
f
Vapor pressure
g
Wind speed
which increased the dispersion of pollutants and reduced the concentrations of aerosols (Deshmukh et al. 2010). Low levels of PM1, PM2.5 and PM2.5–10 in monsoon are mainly
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due to wash out effect of rainfall, as 80 % of the total rainfall is received in this season (Karar et al. 2006; Tiwari et al. 2009; Deshmukh et al. 2010).
Bull Environ Contam Toxicol (2013) 91:704–710
The correlation analysis of PM1, PM2.5 and PM2.5–10 and meteorological variables is made in order to gain a first understanding as to how closely the mass levels of PM1, PM2.5 and PM2.5–10 are related to the influence of meteorological factors (Table 4). PM2.5 showed high correlation (r = 0.69) with PM1 and moderate correlation (r = 0.47) with PM2.5–10 and suggested that PM2.5 and PM2.5–10 might have been contributed by different origin sources (Rengarajan et al. 2011; Deshmukh et al. 2012). Seasonal correlation of PM2.5 with PM2.5–10 in winter (r = 0.65) is generally higher. PM1 also showed highly positive correlation with PM2.5 (r = 0.86) in winter, whereas poor correlation is observed with PM2.5–10 (r = 0.21). PM1, PM2.5 and PM2.5–10 showed significant negative relationship with temperature. The scavenging of PM from the atmosphere by precipitation is an important removal mechanism (Das et al. 2006; Bhaskar and Mehta 2010). PM1, PM2.5 and PM2.5–10 are negatively correlated with relative humidity and rainfall, and suggested that PM are settled down by precipitations particularly in the monsoon season. PM1, PM2.5 and PM2.5–10 also showed significant negative correlations with wind speed, which indicated the prevalence of local sources. Especially, local winds are playing active role on the transportation and dilution of air pollutants in the atmosphere (Tiwari et al. 2009; Arsene et al. 2011). Besides, high wind speed in summer and monsoon increased the mixing height and therefore PMs are more dispersed (Kothai et al. 2008; Pandey et al. 2012). In conclusion, the results showed that the annual mean concentration of PM2.5 was considerably higher than the air quality guidelines of NAAQS of India. The higher concentrations of PMs in Durg is due to the emissions from large number of industries and domestic biomass burning and higher vehicular density that release large amounts of anthropogenic aerosols in the atmosphere. Higher combustion emission and favorable meteorological conditions during winter favored the enhancement of PM concentrations that implies a higher risk for human health as PM1 and PM2.5 penetrate deeper into the respiratory tract. The increased rainfall in monsoon greatly reduced the seasonal average of PM concentrations. Ambient concentrations of PM1, PM2.5 and PM2.5–10 in Durg were higher than those reported at some selected sites in the world. The magnitude of PM air pollution in this region has a significant impact on the surrounding region. Therefore, it is recommended that some effective control measures should be implemented in order to reduce the local anthropogenic pollution in a central Indian region to protect human health. Acknowledgments Authors are thankful to the Head, School of Studies in Chemistry, PT. Ravishankar Shukla University, Raipur, India for providing laboratory facilities.
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Seasonal Air Quality Profile of Size-Segregated Aerosols in the Ambient Air of a Central Indian Region Dhananjay K. Deshmukh • Manas K. Deb Devsharan Verma • Jayant Nirmalkar
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Received: 6 April 2013 / Accepted: 1 October 2013 / Published online: 16 October 2013 Ó Springer Science+Business Media New York 2013
Abstract Seasonal distribution trends of size-segregated aerosols i.e. submicron (PM1), fine (PM2.5) and coarse (PM2.5–10) and their relationship with meteorological variables employing correlation analysis were studied in the ambient air of central India from July 2009 to June 2010. The annual mean concentrations of PM1, PM2.5 and PM2.5–10 were found to be 65.7, 135.0 and 118.5 lg m-3, respectively. The annual mean PM2.5 concentration is three times higher than the National Ambient Air Quality Standards of India (NAAQS). Higher concentrations of PM1, PM2.5 and PM2.5–10 were found during winter due to enormous biomass burning especially during night time due to the use of combustible goods like fire wood and dung cake in the open space by the peoples to keep themselves warm and lower concentrations were observed during monsoon when there were high precipitations. PM2.5 showed high positive correlation with PM1 (r = 0.69) and moderate correlation with PM2.5–10 indicating that variation in PM2.5 mass is governed by the variation in PM1 mass or vice versa. Keywords Size-segregated aerosols Seasonal variation Meteorological variables Correlation analysis
The world is facing environmental crisis due to severe air pollution as result of rapid urbanization and industrialization, bloom in population and economic expansion
D. K. Deshmukh (&) M. K. Deb D. Verma J. Nirmalkar School of Studies in Chemistry, PT. Ravishankar Shukla University, Raipur 492010, Chhattisgarh, India e-mail: [email protected]; [email protected]
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(Mohanraj et al. 2011; Pipalatkar et al. 2012; Yang et al. 2012). Deteriorating air quality is a major problem faced by millions of urban inhabitants as most Indian cities are highly polluted with particulate matter (PM) concentrations well above the recommended limits of National Ambient Air Quality Standards of India (Das et al. 2006; Pandey et al. 2012). The effects of atmospheric PM on human health fundamentally depend on particle size. The sources and potential health effects of PM1 (Da \ 1.0 lm), PM2.5 (Da \ 2.5 lm) and PM2.5–10 (2.5 lm \ Da \ 10 lm) are very diverse. Particles with a size of above 2.5 lm are deposited in the nose or upper respiratory tract, while smaller particles i.e. PM1 and PM2.5 travel deep into the lungs with the potential to penetrate tissues and undergo interstitialization (Klejnowski et al. 2012). Analysis based on atmospheric aerosols estimates that the ambient air pollution causes *5 % of trachea, bronchus and lung cancer, *2 % of cardio-respiratory mortality and *1 % of respiratory infection mortality, globally (WHO 2005). In the light of aforesaid significance, several investigators have studied the mass concentration and seasonal variation of atmospheric aerosols in Indian region (Tiwari et al. 2009; Pandey et al. 2012) and particularly in central India (Deshmukh et al. 2010, 2012) and suggested that PM2.5–10 are mainly due to the re-suspension of soil particles, whereas PM1 and PM2.5 is a mixture of primary and secondary aerosols emitted from the variety of anthropogenic activities. The study was conducted in Durg (20°230 –22°020 N and 80°460 –81°580 E), a rapidly growing industrial city covering an area of *137 km2, from July 2009 to June 2010. Durg (Fig. 1) has a population of *0.6 million as per the census of India in 2011 (Deshmukh et al. 2012). The sampling site was located in front of a Government College of Science, in an area encompassed by educational and
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research institutes. The closest highway with significant road traffic is *100 m away from the sampling site having high volume of traffic –mostly consisting of diesel-powered buses and trucks, two-stroke motorcycles and threewheelers. The average daily traffic volume is *2,050–2,120 vehicles per hour. The major railway station is located towards the east direction at a distance of *500 m from the sampling point having a frequency of *160 trains per day. Durg and its surrounding have different kinds of industries like steel, power, cement and iron foundries, which are located at radial distances of *20–50 km. The study area is also affected by high agricultural activities in the rural zones. Therefore, wood and biomass burnings all over the year in the nearby villages are also the significant contributory sources of aerosols in Durg (Deshmukh et al. 2010). Consequently, it is now a serious concern to control the PM concentration in the central India region and the seasonal variations of sizesegregated PM and their relationship with the meteorological variables are needed to be known. Evaluation of relationship of PM1, PM2.5 and PM2.5–10 with meteorological variables will be very useful to understand the role of aerosols in urban weather and climate variability. Therefore, the present study was focused mainly on the seasonal distribution of PM1, PM2.5 and PM2.5–10 aerosols, and their relationships with prevailing meteorology during the study period in Durg, India.
Materials and Methods Aerosol sampling was performed on the terrace of a double-storied building at a height of *15 m above the ground level in a location free from obstruction to minimize the external effects of surrounding barriers, i.e. obstacles like huge trees or buildings which prevent the free movement of air. Andersen aerosol sampler (Model no. TE 20-800, USA; flow rate 28.3 ± 0.3 L min-1) was used for collection of size-segregated PM. Anderson sampler separated particles into nine size fractions according to
Fig. 1 Location of sampling site in Durg, India
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the following equivalent aerodynamic cutoff diameter, 0.0–0.4 lm (stage 1), 0.4–0.7 lm (stage 2) 0.7–1.0 lm (stage 3), 1.0–2.5 lm (stage 4), 2.5–3.3 lm (stage 5), 3.3–4.4 lm (stage 6), 4.4–5.8 lm (stage 7), 5.8–9.0 lm (stage 8) and 9.0–10 lm (stage 9). PM1, PM2.5 and PM2.5–10 particles presented the sum of particles of stage 1–stage 3, stage 1–stage 4 and stage 5–stage 9, respectively. Samples were collected on glass fiber filters (Whatman 41; diameter 81 mm). Each sampling was started at 06:00 h local time and carried for 24 h twice a week. Subsequently to the sampling, the whole sampler with samples loaded in different filters was covered with polyethylene bag and transported to the laboratory for gravimetric mass analysis. The filters were unloaded in a clean room, sealed in plastic bags and were kept in portable refrigerators and transported to the weighing room for gravimetric analysis. The limit of detection (LOD) of cascade impactor ranged from 0.5 to 2.0 lg m-3 for the entire size range. All weight measurements were repeated three times to ensure reliability and reading were accepted when the difference was not exceeding 5 lg. Also, 12 field blanks were collected during the entire sampling period to subtract the positive artifacts due to adsorption of gasphase components onto the filter during and after sampling. PM1, PM2.5 and PM2.5–10 concentrations were determined gravimetrically by following the weight difference of the filters before and after the sampling campaign. The filters were kept in desiccators in an environmentally conditioned room at a temperature of 20°C and at 50 % relative humidity for 24 h before and after sampling, and then weighed by an electronic balance (Sartorius CP225D, reading precision 10 lg). All the reported PM mass concentrations were adjusted to account for the field blank concentrations. Spearman correlation analysis was used to analyze the relationship between the concentrations of PM1, PM2.5, PM2.5–10 and meteorological variables. A level of p B 0.01 and 0.05 was considered to be statistically significant in this study. For statistical analysis, Statistical Package for Social Sciences (SPSS) for Windows version 16.0 was used. Weather wise, the whole sampling period is divided into four seasons, i.e. monsoon (July–September), winter (October–January), spring (February–March) and summer (April–June). Meteorological parameters, including temperature, relative humidity, wind speed, wind direction and precipitation were measured every 30 min. Ambient temperature and relative humidity were measured by a respective probe (Vaisala Company, Helsinki, Finland, Model QMH102), and wind speed and wind direction were recorded by the wind monitor (Vaisala Company, Helsinki, Finland, Model QMW110A). Both meteorological instruments were mounted 53 m above ground level.
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Results and Discussion The time-series plot of meteorological variables for the study period is shown in Fig. 2. The annual mean temperature and relative humidity were found to be 26.5°C and 65 %, respectively. The variation of relative humidity showed a steep decrease from winter to summer (67 %– 39.0 %) and then increase in the monsoon (83 %). The annual mean wind speed was found to be 5.2 m s-1. The total rainfall during the study period was monitored to be 1,272.2 mm; 80 % of which (998.3 mm) was observed during the monsoon. The total rainfall during summer was measured to be 166.0 mm. Winter and spring were found to be relatively calm seasons (72.2 % and 66.3 %) as compared to summer (17.9 %) and monsoon (16.8 %). The descriptive statistics on concentrations of PM1, PM2.5 and PM2.5–10 are given in Table 1. The concentration of PM1, PM2.5 and PM2.5–10 varied from 8.6 to 135.7, 26.7 to 263.0 and 36.0 to 250.6 lg m-3 with an annual mean and standard deviation of 65.7 ± 36.9, 135 ± 76.2 and 118.5 ± 32.8 lg m-3, respectively. The annual mean
concentration of PM2.5 is more than 8, 13 and 3 times higher as compared to annual-guidelines of USEPA (15 lg m-3; 2002), WHO (10 lg m-3; 2005) and NAAQS of India (40 lg m-3; 2009), respectively. Concerning the 24 h average of USEPA (35 lg m-3; 2002), WHO (25 lg m-3; 2005) and NAAQS of India (60 lg m-3; 2009) standards for PM2.5, 97 %, 100 % and 83 % of sampling-dates exceeded the respective standards. The concentrations of PM1, PM2.5 and PM2.5–10 found in Durg are compared with other studies conducted in the cities of India and rest part of the world (Table 2). From the comparison of above size-resolved aerosols found in Durg, India with those of the above mentioned studies in the cities of India and all over the world, it is concluded that Durg experiences significantly higher concentrations of PM1, PM2.5 and PM2.5–10 aerosols. This can be regarded as a real environmental problem that is posing a serious risk to the quality of life. Traffic density and combustion activities in governmental building and roadside areas are usually increased during working days. However, in weekend heating in
Fig. 2 Time-series scheme of meteorological variables from July 2009 to June 2010
Table 1 Annual, working day and weekend mean, standard deviation and range of PM1, PM2.5 and PM2.5–10 concentrations in Durg, India from July 2009 to June 2010 PMsa (lg m-3)
Annual (n = 120) Mean ± SDb (Range)
Working day (n = 60) Mean ± SDb (Range)
Weekend (n = 60) Mean ± SDb (Range)
PM1
65.7 ± 36.9 (8.6–135.0)
68.5 ± 22.8 (21.3–135.0)
PM2.5
135.0 ± 76.2 (26.7–263.0)
150.0 ± 79.3 (33.2–263.0)
99.2 ± 50.3 (26.7–181.5)
PM2.5–10
118.5 ± 32.8 (36.0–250.6)
168.5 ± 55.6 (67.5–250.6)
106.0 ± 31.8 (36.0–210.3)
a
Particulate matters
b
Standard deviation
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59.2 ± 29.1 (8.6–92.3)
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governmental building and roadside areas are minimized and traffic density is also relatively decreased as compared to working days. Size-segregated PM showed a general weekly pattern and higher concentrations are found during working day and lower concentrations during weekend (Table 1). The higher concentrations of aerosols on working days than weekends resulted from the influence of local and regional anthropogenic activities such as re-suspension of soil particles by traffic and other anthropogenic activities such as industrial process and building construction. The PM concentrations on the weekends as compared with working days were reduced by 13.6 %, 33.9 % and 37.2 % for PM1, PM2.5 and PM2.5–10, respectively. This reduction could be attributed to the decrease of traffic density due to the official days-off of government institutions, schools and colleges. The effect of decreasing traffic density during weekends did not only cause low exhaust particulate emission, but also reduced emissions generated from tyre wears and re-suspension of soil particles (Latha and Highwood 2006; Lough et al. 2006). This is consistent with the observations of other investigators who have attributed the decrease on PM concentration on weekend days to the decrease in traffic density (Karar et al. 2006; Lonati et al. 2006). Seasonal mean concentrations of PM are given in Table 3. PM1, PM2.5 and PM2.5–10 concentrations in Durg reveal a general trend of maximum concentrations during winter as well as in spring, and minimum concentrations
Table 2 Comparison of mean mass concentrations (lg m-3) of PM1, PM2.5 and PM2.5–10 aerosols in Durg, India and different sites over the world
Sampling area
during summer and monsoon seasons. The concentrations of PM1 and PM2.5 in winter are double the concentrations observed in monsoon and summer. The high concentrations of PM1, PM2.5 and PM2.5–10 during winter are due to the huge agricultural activities and brick kiln emissions which utilize low quality coal and paddy husk (Deshmukh et al. 2010, 2012). During winter, after the crop harvesting, the biomass residues are set on fire on the field to fertilize the land for the coming year. People use combustible materials, such as fire wood and dung cakes, in open space for heating during winter, resulting in the release of large quantities of PM to the atmosphere. The higher dispersion rate of pollutants due to the high temperature is the reason for low aerosol mass during summer season in this area. The seasonal variation of meteorological variables such as temperature, wind speed and precipitation modulate the air quality and play important role in determining the levels, transport and diffusion of aerosols (Bhaskar and Mehta 2010; Sandeep et al. 2013). High relative humidity (95 %), low temperature (5.8°C) and relatively calm wind speed (1.0 m s-1) helped high aerosol mass loading in winter days. In winter during the months of October–January, low temperature favored the gas-to-particle conversion of air pollutants. Additionally, low wind speed and low boundary layer height limited the dispersion of air pollutants in winter (Kothai et al. 2008; Pandey et al. 2012). Conversely, high temperature (39°C) and high wind speed (10.2 m s-1) during the months of April–June in summer caused higher boundary layer height
Sampling period
PM1
PM2.5
Durg, India
Jul 2009–Jun 2010
65.7
135.0
118.5
Present study
Delhi, India
Jan 2007–Dec 2007
97.0
122.0
Tiwari et al. 2009
Ahmedabad, India
Dec 2006–Feb 2006
55.7
Korba, India
Apr 2005–Mar 2006
Kolkata, India
Jan 2006–Dec 2006
69.2
Bilaspur, India
Apr 2005–Mar 2006
53.9
Raipur, India
Apr 2005–Mar 2006
51.2
Kolkata, India
2001–2002
Mumbai, India Shenyang, China
113.8
PM2.5–10
Rengarajan et al. 2011 116.8
Deshmukh et al. 2010
112.6
Deshmukh et al. 2010
71.2 98.3
Reference
Chatterjee et al. 2012
86.8
Deshmukh et al. 2010
178.6
125.5
Das et al. 2006
2005–2006 2004–2005
103.0
107.8 89.0
Kothai et al. 2008 Han et al. 2010
Anshan, China
2004–2005
123.0
99.0
Jinzhou, China
2004–2005
89.0
Han et al. 2010
Lasi, Romania
Jan 2007–Mar 2008
27.7
Arsene et al. 2011
Helsinki, Finland
Oct 2002–Apr 2004
9.0
Lianou et al. 2011
Han et al. 2010
Athens, Greece
Oct 2002–Apr 2004
25.0
Lianou et al. 2011
Chuncheon, Korea
Dec 2005–Aug 2009
31.0
Han et al. 2010
Jinan, China
2006–2007
Zongurdak, Turkey
2006–2007
150.0 28.1
Yang et al. 2012 Akyuz and Cabuk 2009
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708
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Table 3 Seasonal mean and standard deviation of PM1, PM2.5 and PM2.5–10 in Durg, India from July 2009 to June 2010 PMsa (lg m-3)
Monsoon (n = 30) Mean ± SDb
Winter (n = 39) Mean ± SDb
PM1
35.3 ± 16.5
101.3 ± 23.1
76.1 ± 36.0
PM2.5
73.9 ± 33.1
215.0 ± 58.0
113.0 ± 73.7
83.5 ± 25.2
PM2.5–10
83.1 ± 36.7
151.1 ± 37.8
129.9 ± 37.2
115.0 ± 22.2
a
Particulate matters
b
Standard deviation
Spring (n = 20) Mean ± SDb
Summer (n = 30) Mean ± SDb 37.5 ± 13.6
Table 4 Correlation analysis of PMs with meteorological variables in Durg, India from July 2009 to June 2010 PM2.5–10
PM2.5
Tc
PM1
RHd
RFe
VPf
WSg
Monsoon = upper diagonal triangle (n = 30); Winter = lower diagonal triangle (n = 39) PM2.5 PM2.5–10
0.65
a
PM1 Tc RH
d
0.55a
0.26 a
0.12
0.86
0.21
-0.31
-0.21
-0.32 -0.31
-0.23
0.24
-0.20
-0.14
RFe
-0.45b
-0.36
-0.33
-0.08
VPf
-0.49a
-0.32
-0.33
-0.26
WS
g
-0.10
-0.16
-0.38
-0.23
0.30
-0.58
-0.23
-0.53a
0.19
-0.81a
0.10
-0.59
a
-0.18
-0.59a
0.28
-0.62
a
0.22
-0.73a
-0.16
-0.14
-0.17
-0.56a
-0.08 -0.11 0.54a a
0.06
a
0.51
0.11
0.05
-0.09
0.20
0.38
-0.16
0.15
Spring = upper diagonal triangle (n = 20); Summer = lower diagonal triangle (n = 30) PM2.5 PM2.5–10
0.16
PM1
0.50a
T
c
a
RHd RF
e
0.80b 0.12
0.28 0.06
-0.59
-0.58
-0.36
0.27
b
a
0.32 0.07
-0.22 -0.19
-0.26 -0.27
-0.45 -0.11
-0.45b
0.19
-0.12
-0.15
-0.53a
-0.28
-0.08
-0.19
-0.56a
a
-0.54 -0.38
a
-0.50b -0.44b
b
-0.24
-0.10
-0.48
-0.56
-0.48
-0.21
VPf
-0.67a
-0.44b
-0.45b
-0.30
-0.04 0.62a
WSg
-0.78a
-0.51a
-0.62a
-0.51a
0.14
0.58a 0.09
0.16 -0.28
0.12 -0.05 0.41
0.43
Annual = lower diagonal triangle (n = 120) PM2.5
1.00
PM2.5–10
0.47b
PM1
0.69a
0.36
1.00
Tc
-0.63a
-0.60a
-0.57a
1.00
RHd
-0.52a
0.39
-0.66a
-0.33
1.00
RFe
-0.16
0.19
-0.12
-0.10
-0.13
VP
f
WSg a
1.00
b
-0.56
-0.45
-0.66
-0.54a
a
a
1.00
-0.49
-0.39
0.66
0.10
1.00
-0.55a
-0.69a
0.01
-0.13
0.41
1.00
p \ 0.01
b
p \ 0.05
c
Temperature
d
Relative humidity
e
Rainfall
f
Vapor pressure
g
Wind speed
which increased the dispersion of pollutants and reduced the concentrations of aerosols (Deshmukh et al. 2010). Low levels of PM1, PM2.5 and PM2.5–10 in monsoon are mainly
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due to wash out effect of rainfall, as 80 % of the total rainfall is received in this season (Karar et al. 2006; Tiwari et al. 2009; Deshmukh et al. 2010).
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The correlation analysis of PM1, PM2.5 and PM2.5–10 and meteorological variables is made in order to gain a first understanding as to how closely the mass levels of PM1, PM2.5 and PM2.5–10 are related to the influence of meteorological factors (Table 4). PM2.5 showed high correlation (r = 0.69) with PM1 and moderate correlation (r = 0.47) with PM2.5–10 and suggested that PM2.5 and PM2.5–10 might have been contributed by different origin sources (Rengarajan et al. 2011; Deshmukh et al. 2012). Seasonal correlation of PM2.5 with PM2.5–10 in winter (r = 0.65) is generally higher. PM1 also showed highly positive correlation with PM2.5 (r = 0.86) in winter, whereas poor correlation is observed with PM2.5–10 (r = 0.21). PM1, PM2.5 and PM2.5–10 showed significant negative relationship with temperature. The scavenging of PM from the atmosphere by precipitation is an important removal mechanism (Das et al. 2006; Bhaskar and Mehta 2010). PM1, PM2.5 and PM2.5–10 are negatively correlated with relative humidity and rainfall, and suggested that PM are settled down by precipitations particularly in the monsoon season. PM1, PM2.5 and PM2.5–10 also showed significant negative correlations with wind speed, which indicated the prevalence of local sources. Especially, local winds are playing active role on the transportation and dilution of air pollutants in the atmosphere (Tiwari et al. 2009; Arsene et al. 2011). Besides, high wind speed in summer and monsoon increased the mixing height and therefore PMs are more dispersed (Kothai et al. 2008; Pandey et al. 2012). In conclusion, the results showed that the annual mean concentration of PM2.5 was considerably higher than the air quality guidelines of NAAQS of India. The higher concentrations of PMs in Durg is due to the emissions from large number of industries and domestic biomass burning and higher vehicular density that release large amounts of anthropogenic aerosols in the atmosphere. Higher combustion emission and favorable meteorological conditions during winter favored the enhancement of PM concentrations that implies a higher risk for human health as PM1 and PM2.5 penetrate deeper into the respiratory tract. The increased rainfall in monsoon greatly reduced the seasonal average of PM concentrations. Ambient concentrations of PM1, PM2.5 and PM2.5–10 in Durg were higher than those reported at some selected sites in the world. The magnitude of PM air pollution in this region has a significant impact on the surrounding region. Therefore, it is recommended that some effective control measures should be implemented in order to reduce the local anthropogenic pollution in a central Indian region to protect human health. Acknowledgments Authors are thankful to the Head, School of Studies in Chemistry, PT. Ravishankar Shukla University, Raipur, India for providing laboratory facilities.
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