Winter seasons assessment of atmospheric aerosol over Coalfield region of India using geoinformatics

UCLIM-00293; No of Pages 26 Urban Climate xxx (2017) xxx–xxx

Contents lists available at ScienceDirect

Urban Climate journal homepage: http://www.elsevier.com/locate/uclim

Winter seasons assessment of atmospheric aerosol over Coalfield region of India using geoinformatics Akshay Kumar ⁎, Akhouri Pramod Krishna Department of Remote Sensing, Birla Institute of Technology, Mesra, Ranchi 835215, Jharkhand, India

a r t i c l e

i n f o

Article history: Received 14 January 2017 Accepted 2 April 2017 Available online xxxx Keywords: Aerosol optical thickness (AOT) Coal mining Ǻngstrӧm exponent LULC GIS

a b s t r a c t Aerosol optical thickness (AOT), temperature and precipitable water vapour (PWV) were measured using MICROTOPS II Sunphotometer during the month of January 2011 and January 2014 for their analysis in the winter season over South Karanpura Coalfield region, Jharkhand, India. Patterns of AOT concentration (at five different wavelengths 340, 500, 870, 936 and 1020 nm) were measured along with water vapour and temperature, which were then spatially analyzed with reference to land use/land cover (LU/LC) of the region. Spatial distribution of AOT indicated higher concentration over industrial area (1.923–3.333 at 340 nm) and construction sites (N 2.955 at 340 nm) and lower concentrations (b 0.511 at 340 nm) over planned residential areas. To determine the aerosol size distribution, Ǻngstrӧm parameters (α, β) were calculated at wavelengths 340–870 nm (2011 and 2014) which ranged between 0.422 and 1.286 and 0.472 to 2.593 in January 2011 and 2014 respectively for ‘α’, whereas ‘β’ value lies between 0.127 and 1.379 in January 2011 and 0.031 to 1.923 in January 2014. The study indicates the presence of different particle sizes of aerosol associated with industries and coal mining activities which contribute significantly to increased aerosol concentration in the study area. © 2017 Elsevier B.V. All rights reserved.

1. Introduction Atmospheric aerosol or particulate matter (PM) is an important air pollutant that associated with adverse human health effects, visibility deterioration and uncertain impact on climate change (Pope, 2000; Cheung et al., 2005; IPCC, 2007; Sacks et al., 2011). Aerosols are tiny particles found in either solid or liquid state of ⁎ Corresponding author. E-mail addresses: [email protected] (A. Kumar), [email protected] (A.P. Krishna)

http://dx.doi.org/10.1016/j.uclim.2017.04.006 2212-0955 © 2017 Elsevier B.V. All rights reserved.

Please cite this article as: Kumar, A., Krishna, A.P., Winter seasons assessment of atmospheric aerosol over Coalfield region of India ..., Urban Climate (2017), http://dx.doi.org/10.1016/j.uclim.2017.04.006

2

A. Kumar, A.P. Krishna / Urban Climate xxx (2017) xxx–xxx

matter, suspended in the air (excluding cloud particles) with an extensive combination of sizes, ranging from 10−2 μm to 102 μm (Ranjan et al., 2007; Kumar et al., 2016). These particles have negligible terminal fall speed and are injected into the atmosphere through natural sources originating from volcanoes, dust storms, forests, grasslands, fires, vegetation, or through anthropogenic sources such as burning of fossil fuels and the alteration of surface covers (Kaskaoutis et al., 2007). Aerosols significantly affect the Earth's atmosphere by either scattering or absorbing the incoming solar radiation while it indirectly affects the cloud formation and its optical properties by acting as cloud condensation nuclei (Ranjan et al., 2007; Pawar et al., 2012; Wang et al., 2015). The study on aerosol concentration has received prominent attention in global science community in the past several years. It study employs long-term systematic measurement of aerosol properties which can quantify their impact on Earth's radiative balance, the region's climate, air quality and human health (IPCC, 2007; Srivastava et al., 2012; Wang et al., 2015). Air pollution leads to early fetal loss, premature delivery and lower birth weight. When fine and ultra-fine materials get inhaled by people, they get carried by the cells of the lungs and penetrate into the circulatory system where it lodges permanently in organs such as heart and liver. Air pollution further affects the functioning of the lung and increased the fatality of the patients with pre-existing heart and lung diseases (Penttinen et al., 2001; Heinrich, 2003; Ibald-Mulli et al., 2004). Children are most vulnerable due to their developing organ systems. Temporary changes in air pollution are associated with short-term changes in the pulmonary health of asthmatic children and also leading causes of inducing asthma in children (Kinney and Lippmann, 2000; Venn et al., 2001). Due to the adverse impact of air pollution on climate, human health and environment, it becomes essential to understand the characteristic of atmospheric aerosol in regional and global scale (Tiwari and Singh, 2013). Aerosol optical depth (AOD) also called aerosol optical thickness (AOT), and precipitable water vapour (PWV) are two very imperative physical parameters for studying the characteristic of atmospheric aerosol that is related to direct solar radiation by scattering the absorption process (Ranjan et al., 2007). Water vapour is the most abundant greenhouse gas that plays a substantial role in many atmospheric processes, such as radiative cooling, latent heat and convective activity (Zveryaev and Allan, 2005). This makes it imperative to understand the vertical and horizontal distribution of water vapour in analyzing the variance of hydrological cycle, forecasting of climate change and global warming studies (Bernstein et al., 2007). The Ǻngstrӧm wavelength exponent (α) and Ǻngstrӧm turbidity (β) formula (Ǻngstrӧm, 1964) are the most commonly used parameters to illustrate the wavelength dependence and effects of scattering and absorption of atmospheric aerosol. The Ǻngstrӧm exponent is calculated using the spectral variation of AOT, which has been employed by various researchers as a tool for estimating the particle size distribution and for extrapolating AOT throughout the broad spectral region as well as to distinguish the different aerosol types (Schuster et al., 2006; Kalapureddy and Devara, 2008; Kedia and Ramachandran, 2009). The relationship between the wavelength (α) and turbidity (β) follows a power law called Ǻngstrӧm power law which is a good representation of aerosol that has a wide variety of origin and composition (McCartney, 1976). Monitoring and evaluating land use/land cover (LU/LC) change in coal mining area has become an important priority for scientists, land managers, and policy makers as LULC changes are typically associated with mining (Prakash and Gupta, 1998). Vast forested areas are cleared due to mining of minerals and fossil fuels (Sarma, 2005). The flora, fauna, hydrology, and soil biology are permanently altered due to the surface or opencast coal mining, which are also responsible for massive overburden dumps (Tiwary and Dhar, 1994). The South Karanpura Coalfield region undertakes both surface and underground mining. Surface coal mining creates extensive environmental pollution and air quality deterioration through the dust and harmful gaseous pollutants not only within the mining premises but also in its adjoining localities (Armstrong et al., 1980). In India, the production of coal demands an increase in the rate by 20–25 Mt/year to meet the energy demand for the next 20–25 years (Ghose and Majee, 2007). The opencast mining technique is an efficient way to increase coal production and maintain the energy demand. In the year 1995–96, the total coal production in the country was 274 Mt which was 68% of the total production through opencast mining. It further increased to 70% during the year 2000 (Kumar, 1995). There are several sources of aerosol concentration in this study area that can be distinguished according to their different particle sizes. Coarse aerosol particles are emitted by coal mining, transportation activities, pulverization, dumping of overburden and fly ash whereas, fine particulate matter are produced through combustion processes such as coal power stations, iron works, local heating and transportation (Munroe et al., 2008). During biomass combustion, different kind of particles are released such as black carbon, organic Please cite this article as: Kumar, A., Krishna, A.P., Winter seasons assessment of atmospheric aerosol over Coalfield region of India ..., Urban Climate (2017), http://dx.doi.org/10.1016/j.uclim.2017.04.006

A. Kumar, A.P. Krishna / Urban Climate xxx (2017) xxx–xxx

3

carbon and volatile organic compounds (Streets et al., 2003). Among of them, black carbon also known as elementary carbon is the prime aerosol absorbing component in the atmosphere, which also affects the formation of clouds and accelerates solar heating (Uno et al., 2003). The primary sources of biomass burning aerosols are fossil fuel and domestic burning (cooking and heating) along with forest fire and agricultural residue (Kanakidou et al., 2004). Remote sensing (RS) and geographic information system (GIS) have often been employed to monitor and interpret the spatial distribution of atmospheric aerosols and LU/LC of an area. Pandey et al. (2016) investigated the natural plants metal uptake potential in fly ash dump sites of Patratu thermal power plant (P.T.P.S.), Jharkhand, India, which revealed that among Cd, Cr, Cu, Fe, Mn, Ni, and Pb only Cd, Cr, and Ni can be accumulated in different plants (although in less amounts), which indicated that plants can be used as good phytostabilizer of metals. The urban environment condition of Ranchi, Jharkhand was investigated by Kumar and Pandey (2013a) using various environmental indicator viz., ambient air quality, aerosol concentration, ambient noise level and urban green space in a spatiotemporal framework using geoinformatics. Their study indicated that the spatial distribution of AOT is higher at transportation junctions (0.30–0.35 at 340 nm) and road junctions (N0.30% at 340 nm) and low concentrations (b 0.22% at 340 nm) at planned residential areas. Spatial and temporal variation of AOT values were analyzed by Balakrishnaiah et al. (2012) over India, which exhibited high concentrations during summer season and low concentrations during winter. Ranjan et al. (2007) studied aerosol concentration over Rajkot city during July 2004 to July 2005 using MICROTOPS II Sunphotometer and observed high seasonal variation of AOT during summer (0.41) and low variation during winter (0.11). The correlation between biomass burning and aerosol pertaining to LU/LC change in Southeast Asia were investigated by Munroe et al. (2008) who found that coal mining and associated activities like blasting, transportation, dumping, etc. are the main factors responsible for air pollution. Chadwick et al. (1987) calculated that approximately 50% of the total coal dust were expelled during the journey time along an unpaved haul road while 25% of coal dust released during the loading and discharging of the dump truck. The present research focuses on the determination of causal factors responsible for environmental changes occurring in South Karanpura Coalfield and its surrounding regions. Furthermore, the relationship between the causal factors of environmental variations in the study area is also assessed. The study area comprises a significant number of coal-based industries, which consequently points to an increase in aerosol concentration due to biomass burning rather than interference from industrial aerosols. The investigation integrates different environmental factors (causal factors) such as aerosol concentration along with Ǻngstrӧm parameters, temperature, precipitable water vapour and LU/LC using geospatial technology to determine the temporal variation in the environmental condition in the study area. Further, the dependency of such environmental changes on each other also urges the need to evaluate and suggest suitable measures control the ill effects of environmental deterioration in the South Karanpura Coalfield region. 2. Study area This study area comprises the South Karanpura Coalfield region which is situated in Ramgarh and part of Hazaribagh districts of Jharkhand state, India (Fig. 1). It covers an area of 380 km2 as delineated from the Survey of India (SOI) topographical map (Sheet No. 73 E/6) on a scale of 1:50,000. The region lies between 23°35′ N to 23°44′N latitude and 85°15′E to 85°27′E longitude, situated at an altitude of 348 m above mean sea level. The coal-mining area is considerably recognized for its excellent quality non-coking coal. The geological formation of South Karanpura Coalfield belongs to the Gondwana System (CIL, 1993) with huge reserves of coal suitable for power generation. It is a hilly area being a part of the Chotanagpur plateau and is covered with lush green forest. The fertile land is partly cultivated and the agriculture is mostly rains fed. The climate of the area is subtropical and is characterized by three seasons viz. summer, winter and monsoon. The mean annual precipitation of the study area is 1400 mm, whereas, the temperature reaches up to 45 °C during summer and falls to 2 °C during winters. The area is drained by Damodar River with Nalkari River as its main tributary. The main urban settlements in the region are Patratu, Bhurkunda, Saunda, Barkakana, Giddi, Sayal and Simratanr. Among these, the Patratu region is a rapidly growing industrial town which is well known for coal mining and power generation. The Patratu thermal, is a symbol of an Indo-Russian relationship established in the decade of the sixties along with Jindal Steel and Power Limited (J.S.P.L.). There are some medium and small-scale industries also present in the study area. There are several agents responsible for air Please cite this article as: Kumar, A., Krishna, A.P., Winter seasons assessment of atmospheric aerosol over Coalfield region of India ..., Urban Climate (2017), http://dx.doi.org/10.1016/j.uclim.2017.04.006

A. Kumar, A.P. Krishna / Urban Climate xxx (2017) xxx–xxx Fig. 1. Location map of the study area and its representation on LISS-IV satellite image in a false colour composite (FCC). A large reservoir present in the area and the river Damodar flows from northwest to southeast.

4

Please cite this article as: Kumar, A., Krishna, A.P., Winter seasons assessment of atmospheric aerosol over Coalfield region of India ..., Urban Climate (2017), http://dx.doi.org/10.1016/j.uclim.2017.04.006

A. Kumar, A.P. Krishna / Urban Climate xxx (2017) xxx–xxx

5

pollution in the study area which includes industries, coal mining activities, vehicle emissions, burning of biomass, modern agricultural inputs, construction activity, etc. The location map of the study area is shown in Fig. 1. 3. Instrument and data collection 3.1. MICROTOPS-II Sunphotometer instrument The ground based measurements were carried out using a handheld and portable multiband MICROTOPS II Sunphotometer manufactured by the Solar Light Company, USA. It is very popular due to their ease of use, portability, and comparatively low cost. The handheld multi-band MICROTOPS II Sunphotometer measures the aerosol optical thickness, direct solar irradiance through each band, obtains the water vapour column (also called as precipitable water) and temperature along with a global positioning system (GPS) to obtain position and time. In the present study, a Trimble GeoExplorer 3000 GeoXT series handheld GPS was used during the field survey to acquire real-time sub-foot (b 30 cm) horizontal and 2–3 m real-time or post-processed vertical accuracy (Pandey and Kumar, 2013). The Sunphotometer has five accurately aligned optical collimators viz. 340 nm, 500 nm, 870 nm, 936 nm, and 1020 nm with a full field view of 2.5°. Each channel has a narrow-band interference filter and a photodiode for each wavelength. The bandwidth for channel 340 is 2 nm, whereas, the rest of the channels exhibit 10 nm. To ensure accurate alignment with the optical channels, a sun target, and pointing assembly is attached to the optical block and laser. When sun's rays are centered on the bull's-eye of the sun target, all optical channels are oriented directly on the solar disk. AOT and water vapour column are determined through Bouguer-Lambert-Beer law, which expresses the total attenuation of the direct solar beam through atmosphere (Ichoku et al., 2002): −2

V λ ¼ V 0λ D

exp½−τλ ðMÞ

ð1Þ

where Vλ is the signal measured by the instrument at wavelength λ, V0λ is the extraterrestrial signal at wavelength λ, D is Earth-Sun distance in Astronomical Units at time of observation, τλ is the total optical thickness (τλ = τaλ + τRλ + τO3λ) (τaλ is aerosol optical thickness, τRλ is Rayleigh (air) optical thickness and τO3λ is Ozone optical thickness) at wavelength λ and M represents the optical air mass. The AOT is obtained by subtracting optical thickness due to Rayleigh scattering from the total optical thickness. The MICROTOPS II ignores the optical thickness through processes such as O3 and NO2 absorption. The water vapour column is determined based on measurements at 936 nm (water absorption peak) and either by the 870 nm or 1020 nm (no absorption by water vapour). A nonlinear contribution of water vapour is significant in the channel 936 nm, because of which, an equivalent equation for this channel was given by Reagan et al. (1995) and Ichoku et al. (2002): −2

V w ¼ V 0w D

b

exp ½−τw M−k ðWMÞ

ð2Þ

where, Vw, V0w, D, τw, and M are defined in Eq. (1), while, the subscript w defines the 936 nm water vapour absorption channel; W represents vertical water vapour column thickness; k and b are instrument constants numerically derived for the 936 nm filter. The Sunphotometer extends the intensity of solar radiation at a particular wavelength and calculates the corresponding optical depth through the intensity of solar radiation at higher levels of atmosphere. The Langley method however, provides an easy process for calculating the optical depth (Schmid and Wehrli, 1995). A more detailed calibration and accuracy procedure for measurements through a Sunphotometer are discussed by Morys et al. (2001). 3.2. Satellite data Satellite images are a reliable source for mapping the LU/LC for a region during the time of their acquisition from space (Mushtaq and Pandey, 2013). The LU/LC map of the present study area was prepared using Landsat TM (pertaining to year 2011) and LISS-IV satellite image (pertaining to the year 2013). The Landsat Please cite this article as: Kumar, A., Krishna, A.P., Winter seasons assessment of atmospheric aerosol over Coalfield region of India ..., Urban Climate (2017), http://dx.doi.org/10.1016/j.uclim.2017.04.006

6

A. Kumar, A.P. Krishna / Urban Climate xxx (2017) xxx–xxx

TM data was downloaded from the website of United State's Geological Survey (USGS) (http://glovis.usgs. gov), whereas the LISS-IV satellite image was procured from National Remote Sensing Centre (NRSC) Hyderabad. The topographical map pertaining to sheet number 73 E/6 provides the details of the study area, and was obtained from the Survey of India (SOI) on 1:50,000 scale for ground reference. The specifications of satellite and ancillary data are presented in Table 1. 3.3. Meteorological data Aerosol is influenced by a number of parameters such as airflow, atmospheric humidity, and other meteorological conditions. The meteorological parameters pertaining to temperature, accumulated rainfall (RF), wind speed (WS) and relative humidity (RH %) was downloaded from the National Centre for Environmental Prediction (NCEP) Climate Forecast System Reanalysis (CFSR) global weather website (http://globalweather. tamu.edu/) for a period of 10 years (2005–2014) on daily basis in .csv file format for the study area. The daily data was then averaged monthly for the parameters WS, Temp and % RH, while the daily rainfall was added on a monthly basis to obtain the cumulative rainfall for the month of January for 10 years (2005–2014). The average temperature during January was found to lie in the range 16–18 °C, whereas mean wind speed during this month was found to be 2.42 m/s. The RH varied between 45 and 55% in January whereas the rainfall was observed to be negligible except for the year 2012 when the cumulative rainfall was found to be 1.77 mm. In general, airflow is calm, with less dust transport at the measurement site of the study area and hence meteorological conditions impose minor influence on aerosol transfer over distance. The aerosol transfer can however be considered to come mainly from land surface. 4. Methodology In the present study, the aerosol optical thickness (AOT) concentration, temperature, precipitable water vapour (PWV) content and satellite data based land use/land cover (LU/LC) maps were used to analyze environmental conditions in the South Karanpura Coalfield region. Winter season was chosen for data collection and comparison due to its intermediate nature regarding air pollution than summer (maximum concentration) and monsoon (minimum concentration) seasons. Layer stacking of bands 2, 3, and 4 was done for Landsat TM (2011) whereas, the bands 3, 2, and 1 of LISSIV (2013) image was used to obtain false colour composites (FCC) during both the years using ERDAS Imagine (version 2015) software. The SOI topographical map was rectified by georeferencing (using geographic latitude-longitude and WGS 84 datum) it and then using the georeferenced SOI map to register the satellite images through map to image as well as image to image registration procedure in ERDAS Imagine software. Visual interpretation of satellite images was made using elements of image interpretation (such as tone, texture, shape, size, pattern, association, etc.) for delineating and mapping various LU/LC classes using prior knowledge of the study area. The Landsat TM (January 2011) and LISS-IV (November 2013) satellite images were interpreted and eight classes of LU/LC, viz. Built-up land (urban/rural), industrial, settlement, cropland/fallowland, forest, wasteland, water body within coal mine, waterbody/river/reservoir and coal mining

Table 1 List of satellite data with their specifications. Data used

Path/row

Date of acquisition

Spectral resolution (μm)

Spatial resolution (m)

Swath (km)

Landsat 5 TM

140/44

22 January 2011

30

185

IRS-R2 LISS-IV

105/55

23 November 2013

120 5.8

70

Topographical map

No. 73E/6

Surveyed 1962

Band 1 Band 2 Band 3 Band 4 Band 5 Band 7 Band 6 Band 1 Band 2 Band 3 –

1:50,000 scale

–

= = = = = = = = = =

0.45–0.52 0.52–0.60 0.63–0.69 0.75–0.70 1.55–1.75 2.09–2.35 10.4–12.5 0.52–0.59 0.62–0.68 0.77–0.86

Please cite this article as: Kumar, A., Krishna, A.P., Winter seasons assessment of atmospheric aerosol over Coalfield region of India ..., Urban Climate (2017), http://dx.doi.org/10.1016/j.uclim.2017.04.006

A. Kumar, A.P. Krishna / Urban Climate xxx (2017) xxx–xxx

7

area were mapped (Kumar and Pandey, 2013b). Ground-truthing was done to verify the various assigned LU/ LC classes. Finally, the area statistics of visually interpreted LU/LC classes were computed in ArcGIS. The ground-based AOT data along with temperature and PWV content was recorded using MICROTOPS II Sunphotometer (with an accuracy ±0.03) during the months of January 2011 and January 2014. At each site, five samples were collected and their average values were used. All the observations were recorded during clear sky conditions which imply cloud-free days or proper sunshine (during 10:00 to 16:00 h). The recorded values of AOT, temperature and PWV at observation sites were interpolated in GIS environment after importing the geographic locations of sampling points from the GPS. The spatial pattern of AOT, temperature and PWV concentrations were analyzed after interpolation through inverse distance weightage (IDW) technique. This method applies spatial correlation of variables and predicts the values of variables at unobserved locations based on those of observed locations (Guofeng et al., 2010; Kumar and Krishna, 2016). Ǻngstrӧm exponent (α) and turbidity coefficient (β) are other two important parameters for studying the atmospheric aerosol properties along with AOT. The Ǻngstrӧm exponent (α) provides the aerosol particle size which can be easily obtained by the Ǻngstrӧm power law (Ǻngstrӧm, 1964). The exponent ‘α’ denotes the relatively high proportion of small particles to large particles (Latha and Badrinath, 2004). In simple terms, when the aerosol particle size equals the size of air molecules, the value of ‘α’ approaches 4 whereas, when the size of an aerosol particle is large, the value of ‘α’ approaches 0. Thus, large values of ‘α’ indicate relatively small size particles. The turbidity coefficient ‘β’ on the other hand, estimates the aerosol loading over the site (Ranjan et al., 2007). The detailed methodology adopted in the present study has been shown in Fig. 2. 5. Results and discussion 5.1. Spatial variability of AOT, temperature and PWV concentration The concentration of AOT, PWV and temperature were simultaneously measured at 41 locations in the South Karanpura Coalfield region using MICROTOPS II Sunphotometer during the winter season of January 2011 and January 2014 (Tables 2 and 3) which were spatially analyzed in GIS environment to analyze the atmospheric condition of the study area. 5.1.1. Aerosol optical thickness concentration Aerosol concentration depends on the meteorological conditions and the altitude of an area. It is influenced by factors such as location, atmospheric condition, annual and diurnal cycles, and the presence of local sources. Ghrefat and Howari (2013) found that the highest concentrations are present near urban and industrial areas, owing to industrialization, urbanization, and an increase in population and traffic. The AOT concentration from a large-scale air polluted area provides a means to assess the degree of air pollution in those areas (Tulloch and Li, 2004). The spatial pattern of AOT distribution analyzed in GIS environment revealed variations in its concentration at all five wavelengths ranging from 340 to 1020 nm. The spatial distribution pattern of AOT in the wavelengths 340 to 1020 nm indicates that the AOT values are higher for smaller wavelengths and lower for larger wavelengths showing the dominance of fine particles in the atmosphere compared to larger size particles. The highly concentrated fine particles enhance the irradiance scattering at lower wavelengths which causes high AOT values at shorter wavelengths, whereas coarser particles provide matching contributions to AOT at long wavelengths (Schuster et al., 2006). In the present study, a high magnitude of mean AOT is observed at shorter wavelengths during both periods (January 2011 and 2014) indicating the dominance of coarse aerosols due to condensed growth and coagulation of submicron aerosols, which are more efficient in producing larger aerosols (Reddy et al., 2011). The gradual decrease of AOT with increasing wavelength indicates that the aerosol size distribution in this region exhibits Junge's power law distribution (Ranjan et al., 2007; Kumar et al., 2016) described by: dn −v ¼ Cr dr

ð3Þ

where, C represents a constant depending on the total number of particles. The AOT concentration over South Karanpura Coalfield region during the year 2011 (Fig. 3a–e) exhibits a significant variation in comparison to 2014 (Fig. 3f–j). It was found to range from 0.138 to 2.425 in January Please cite this article as: Kumar, A., Krishna, A.P., Winter seasons assessment of atmospheric aerosol over Coalfield region of India ..., Urban Climate (2017), http://dx.doi.org/10.1016/j.uclim.2017.04.006

A. Kumar, A.P. Krishna / Urban Climate xxx (2017) xxx–xxx

Fig. 2. Flowchart showing methodology of LU/LC, AOT, PWV and temperature distribution map generation and their comparisons.

8

Please cite this article as: Kumar, A., Krishna, A.P., Winter seasons assessment of atmospheric aerosol over Coalfield region of India ..., Urban Climate (2017), http://dx.doi.org/10.1016/j.uclim.2017.04.006

A. Kumar, A.P. Krishna / Urban Climate xxx (2017) xxx–xxx

9

Table 2 Details of data collected by Sunphotometer during January 2011. Sample no.

Site name

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40

Nalkari Bridge Nitua Basti Patratu Reservoir Reservoir Gate P.T.P.S. Jindal Steel Saunda (Ab. mine Fire)1 Saunda (Ab. mine Fire)2 Bhurkunda Mine Central Saunda Birsa Project (Urimari) Potanga (Ab. mine Fire) Damodar Bridge (Urimari) Hafua Road Glass Factory Chaingada (Sponge Iron) Hehal (Sponge Iron)1 Hehal (Sponge Iron)2 Rock Mine (Hehal) Lapanga (Damodar) Lapanga Bhurkunda Bazar Petrol Pump (Patratu) Railway Gate (Patratu) Sayal Road Hill View (Sayal) Pora Gate Central Saunda Damodar Bridge (Giddi) Giddi-A Raligarha Coal Mine1 Raligarha Coal Mine2 Giddi-C1 Argada Coal Mine1 Argada Coal Mine2 Giddi-C2 Giddi-C (Ab. Mine Fire) Dumuani (Giddi) Bhurkunda Thana Nalkari Bridge (Bhurkunda) Veena Talkies

41

AOT data collected in January 2011 Latitude Longitude Temp PWV AOT340 AOT500 AOT870 AOT936 AOT1020 (°C) (cm) (nm) (nm) (nm) (nm) (nm) 23.5797 23.6028 23.6136 23.6166 23.6351 23.6287 23.6617 23.6621 23.6571 23.6733 23.7043 23.7018 23.6889 23.6596 23.6386 23.6351 23.6150 23.6151 23.6087 23.6542 23.6523 23.6521 23.6558 23.6636 23.6750 23.6820 23.6850 23.6750 23.6730 23.6810 23.6950 23.6950 23.7060 23.7050 23.6970 23.6990 23.7010 23.6730 23.6530 23.6590

85.2722 85.2987 85.2858 85.2924 85.2862 85.3360 85.3380 85.3380 85.3649 85.3519 85.3033 85.2936 85.3023 85.2834 85.3745 85.3980 85.4270 85.4268 85.4275 85.3981 85.3972 85.3760 85.3008 85.3110 85.3240 85.3270 85.3330 85.3470 85.3660 85.3650 85.3670 85.3670 85.3760 85.3810 85.3820 85.3850 85.3850 85.3600 85.3590 85.3480

16.65 20.77 23.23 25.00 22.12 25.25 17.04 17.30 21.50 23.70 25.00 26.00 29.40 17.00 21.60 23.40 25.00 25.00 25.80 27.60 28.60 29.50 13.40 16.90 17.80 18.30 19.00 19.00 19.30 21.50 21.40 21.30 22.23 23.33 22.70 23.50 23.83 22.85 22.93 23.13

0.60 0.83 0.70 0.75 1.50 0.83 0.84 0.80 0.85 0.90 1.10 1.10 1.30 1.50 1.50 1.63 1.70 1.70 1.70 1.70 1.70 1.70 0.60 0.70 0.70 0.70 0.60 0.60 0.60 0.60 0.70 0.70 0.70 0.70 0.70 0.70 0.70 0.70 0.70 0.73

0.695 0.786 0.887 0.944 2.313 1.068 0.586 0.561 0.654 0.717 0.532 0.674 0.633 1.932 1.541 2.425 1.879 1.923 1.826 1.237 0.962 0.964 1.012 1.015 0.907 1.063 0.817 0.524 0.637 0.622 0.637 0.648 0.778 0.705 0.701 0.753 0.815 0.658 0.719 0.773

0.538 0.594 0.657 0.715 2.158 0.806 0.406 0.392 0.459 0.490 0.368 0.475 0.452 1.850 1.292 2.180 1.507 1.545 1.493 0.930 0.683 0.673 0.772 0.767 0.661 0.792 0.602 0.385 0.475 0.470 0.480 0.496 0.583 0.535 0.524 0.578 0.627 0.486 0.527 0.593

0.303 0.331 0.359 0.511 1.824 0.608 0.196 0.176 0.209 0.226 0.162 0.215 0.237 1.787 0.922 1.813 0.919 0.974 1.040 0.534 0.346 0.327 0.424 0.418 0.348 0.426 0.318 0.201 0.259 0.251 0.252 0.262 0.314 0.290 0.278 0.327 0.365 0.254 0.263 0.306

0.281 0.311 0.341 0.544 1.765 0.646 0.204 0.180 0.192 0.214 0.150 0.200 0.219 1.795 0.886 1.784 0.966 0.927 1.016 0.501 0.319 0.301 0.403 0.396 0.335 0.398 0.302 0.195 0.252 0.241 0.240 0.251 0.293 0.274 0.259 0.311 0.344 0.237 0.241 0.283

0.258 0.291 0.324 0.577 1.732 0.683 0.211 0.184 0.175 0.201 0.138 0.184 0.201 1.804 0.851 1.756 0.813 0.880 0.992 0.468 0.292 0.275 0.383 0.374 0.321 0.371 0.286 0.190 0.245 0.231 0.228 0.240 0.272 0.258 0.240 0.295 0.322 0.220 0.219 0.259

23.6570

85.3020

12.32

0.30

0.465

0.322

0.151

0.159

0.167

2011 and from 0.077 to 3.333 in January 2014. Thus, the AOT loading at all wavelengths (340–1020 nm) got enhanced during the year 2014 in comparison to 2011 (Table 4), with a mean value of 0.975 at 340 nm and standard deviation of 0.503 in 2011, while the mean and standard deviation during 2014 are 1.113 and 0.783 respectively. The average AOT concentrations at all locations revealed that the concentration of smaller size particles dominated the coarser sized particles during both periods (January 2011 and January 2014) (Fig. 4). The average AOT concentration at all wavelengths (340–1020 nm) during January 2011 was found to be lower than the mean as compared to January 2014 (Fig. 4). This points to the fact that the environmental condition has worsened after 2014, in the study area, which may be attributed to the recent changes in land use and land cover. The AOT concentration at 340 nm was found to be high over industrial areas (P.T.P.S., J.S.P.L., Bhurkunda, Chaingada, Dari and Hehal), followed by mining sectors such as Potonga, Railgada, Sayal, Saunda, Sirka, GiddiC and Bhurkunda colliery. The highest AOT concentration was recorded at the construction site of Burnpura Please cite this article as: Kumar, A., Krishna, A.P., Winter seasons assessment of atmospheric aerosol over Coalfield region of India ..., Urban Climate (2017), http://dx.doi.org/10.1016/j.uclim.2017.04.006

10

A. Kumar, A.P. Krishna / Urban Climate xxx (2017) xxx–xxx

Table 3 Details of data collected by Sunphotometer during January 2014. Sample no.

Site name

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40

Nalkari Bridge Nitua Basti Patratu Reservoir Reservoir Gate P.T.P.S. Barnpur Cement Factory Jindal Steel Saunda (Ab. Mine Fire) Bhurkunda Mine Central Saunda Birsa Project (Urimari) Potanga (Ab. Mine Fire) Damodar Bridge (Urimari) Hafua Road Palu Basti Peepri tola Daridih School Khatal Patratu Glass Factory Hehal (Sponge Iron) Chaingada (Sponge Iron) Bhurkunda Bazar Petrol Pump (Patratu) Railway Gate (Patratu) Sayal road Hill view (Sayal) Central Saunda Damodar Bridge (Giddi) Giddi-A Giddi-C1 Giddi-C2 Argada Argada coal mine1 Argada coal mine2 Dari (Sponge Iron1) Dari (Sponge Iron2) Hesalong (Sponge Iron) Dumuani (Giddi) Bhurkunda Thana Nalkari Bridge (Bhurkunda) Veena Talkies

41

AOT data collection in January 2014 Latitude Longitude Temp PWV AOT340 AOT500 AOT870 AOT936 AOT1020 (°C) (cm) (nm) (nm) (nm) (nm) (nm) 23.5797 23.6028 23.6106 23.6166 23.6351 23.6181 23.6302 23.6617 23.6571 23.6733 23.7043 23.7000 23.6889 23.6539 23.6604 23.6743 23.6591 23.6590 23.6386 23.6170 23.6429 23.6499 23.6560 23.6636 23.6680 23.6851 23.6734 23.6730 23.6812 23.7050 23.6970 23.7062 23.7050 23.6970 23.7132 23.7089 23.7081 23.6730 23.6530 23.6591

85.2722 85.2987 85.2815 85.2924 85.2862 85.2743 85.3150 85.3380 85.3649 85.3520 85.3033 85.2829 85.3023 85.2960 85.2870 85.2591 85.2592 85.2921 85.3745 85.4270 85.3979 85.3629 85.3008 85.3110 85.3271 85.3199 85.3521 85.3660 85.3650 85.3810 85.3820 85.3761 85.3810 85.3820 85.3861 85.4038 85.4260 85.3600 85.3590 85.3480

16.7 20.8 21.0 21.4 20.6 20.9 21.7 20.2 19.4 22.1 22.1 22.5 23.9 17.1 18.8 19.4 19.4 20.0 24.7 25.7 25.4 24.5 16.3 26.0 25.6 24.3 22.1 21.1 24.0 24.0 24.2 24.8 25.0 25.0 25.4 25.8 25.8 22.8 18.6 17.7

0.6 0.8 1.7 1.7 1.7 1.8 1.6 0.5 0.3 0.5 0.4 0.5 0.4 1.8 1.8 1.7 1.8 1.8 0.5 1.8 1.7 0.6 0.6 0.5 0.5 0.4 0.5 0.3 0.3 0.4 0.4 0.4 0.4 0.5 0.5 0.4 0.4 0.3 0.3 0.3

0.685 0.781 2.691 1.790 3.333 2.955 1.740 0.801 0.623 0.593 0.541 0.570 0.536 2.028 1.795 1.730 1.697 2.443 0.518 2.315 2.112 0.543 0.823 0.605 0.589 0.568 0.593 0.511 0.571 0.608 0.603 0.693 0.634 0.717 0.769 0.744 0.679 0.588 0.742 0.560

0.538 0.594 2.319 1.323 2.999 2.596 1.313 0.491 0.295 0.289 0.245 0.265 0.239 1.472 1.276 1.263 1.233 2.057 0.266 2.280 2.003 0.334 0.511 0.283 0.275 0.262 0.289 0.213 0.276 0.281 0.286 0.349 0.308 0.377 0.441 0.423 0.358 0.289 0.375 0.246

0.303 0.331 1.830 0.724 2.555 2.163 0.787 0.267 0.114 0.114 0.086 0.101 0.085 0.745 0.624 0.660 0.620 1.584 0.107 1.913 1.713 0.149 0.254 0.105 0.101 0.098 0.114 0.077 0.137 0.118 0.125 0.163 0.138 0.201 0.276 0.252 0.192 0.142 0.149 0.089

0.281 0.331 1.834 0.722 2.565 2.174 0.787 0.281 0.160 0.154 0.131 0.139 0.127 0.731 0.621 0.652 0.612 1.608 0.104 1.847 0.927 0.140 0.283 0.139 0.139 0.137 0.154 0.125 0.180 0.157 0.163 0.203 0.175 0.236 0.313 0.294 0.231 0.187 0.194 0.136

0.258 0.291 1.839 0.720 2.574 2.185 0.788 0.295 0.206 0.193 0.176 0.178 0.169 0.717 0.618 0.645 0.604 1.632 0.102 1.833 0.890 0.131 0.312 0.173 0.177 0.177 0.193 0.174 0.223 0.197 0.203 0.243 0.212 0.271 0.350 0.335 0.270 0.233 0.239 0.183

23.6572

85.3021

15.1

0.8

1.227

0.734

0.317

0.347

0.378

Cement Limited (BCL), an industry involved with construction activities such as roads and buildings, etc. Such high concentrations may be due to the high frequency of transportation activities, loading/unloading of coal and smoke emissions from coal-based industries causing high dispersion of dust particles into the atmosphere. The AOT concentration was observed at low levels in residential areas. The high value of AOT at lower wavelengths corresponds to the presence of small size particles at the observation site. Various researchers have reported high AOT levels due to the large proportion of burning biomass fuels and heavily based mining industries (including coal) in Jharkhand and West Bengal (Di Girolamo et al., 2004; Dey et al., 2005; Prasad et al., 2006). The aerosol concentration during both years and at all wavelengths (340–1020 nm) is shown in Table 4. 5.1.2. Temperature Surface temperature is a major factor which controls the energy exchange process between the earth's surface and atmosphere. In the present study, temperature was recorded in South Karanpura using Please cite this article as: Kumar, A., Krishna, A.P., Winter seasons assessment of atmospheric aerosol over Coalfield region of India ..., Urban Climate (2017), http://dx.doi.org/10.1016/j.uclim.2017.04.006

11

Fig. 3. Map showing a variation of AOT at different wavelengths over South Karanpura Coalfield region during the year 2011 (a–e) and 2014 (f–j).

A. Kumar, A.P. Krishna / Urban Climate xxx (2017) xxx–xxx

Please cite this article as: Kumar, A., Krishna, A.P., Winter seasons assessment of atmospheric aerosol over Coalfield region of India ..., Urban Climate (2017), http://dx.doi.org/10.1016/j.uclim.2017.04.006

12

A. Kumar, A.P. Krishna / Urban Climate xxx (2017) xxx–xxx

Table 4 AOT concentration statistics over South Karanpura Coalfield region during both the periods (January 2011 and January 2014). Wavelengths

AOT 340 AOT 500 AOT 870 AOT 936 AOT 1020

Min

Max

Mean

Median

Mode

Std. deviation

2011

2014

2011

2014

2011

2014

2011

2014

2011

2014

2011

2014

0.465 0.322 0.151 0.150 0.138

0.511 0.213 0.077 0.104 0.102

2.425 2.180 1.824 1.795 1.804

3.333 2.999 2.555 2.565 2.574

0.975 0.764 0.481 0.467 0.449

1.113 0.787 0.503 0.505 0.526

0.778 0.59 0.318 0.301 0.275

0.693 0.375 0.192 0.231 0.258

0.637 0.475 0.327 0.311 0.258

0.593 0.289 0.114 0.139 0.193

0.503 0.476 0.436 0.433 0.258

0.783 0.766 0.654 0.615 0.605

Sunphotometer during daytime in the winter season. It varied between 12.32 °C to 29.50 °C in January 2011 and between 15.10 °C to 26.00 °C in January 2014 with a general west to east spatial variation of average temperature (Fig. 5a and b). However, the maximum temperature (22 °C to 29 °C) was recorded in the mid-western part of the study area. The temperature distribution during both periods is shown in Table 5. The winter season is particularly sensitive to AOT levels due to minimal precipitation during this season, along with the building up of aerosols over a prolonged period of existence in the atmosphere (Roy, 2008). There are some places in this region where there are surface mine fires, which are responsible for an increase in the average temperature of the region. The present study also revealed that temperature is high (N20 °C) in the vicinity of industrial areas as compared to mining, urban and forested areas. 5.1.3. Precipitable water vapour Water vapour is one of the essential components of environment as moisture and latent heat transform to water vapour state. Also, it is highly variable and unevenly distributed in the atmosphere. The water vapour is a critical link connecting the diverse elements of the hydrological cycle and hence an understanding of its use in the hydrological climate system and its variability on all spatial and temporal scales is essential (Rathore et al., 2005). Therefore, accurate and frequent measurement of atmospheric water vapour is essential as it plays a significant role in day to day weather prediction as well as in the climatic study. Several methods have been developed by atmospheric scientists to measure the vertical and horizontal distribution of water vapour. The PWV is an important parameter of water vapour, which is a measure of the total water vapour held in a small vertical column extending from the surface of the ground to the top of the atmosphere (Rathore et al., 2005; Kumar et al., 2016). Water vapours are a major contributor to AOT and its spatial and temporal variations have substantial impacts on atmospheric stability and radiation budget. The spatial distribution of water vapour content over South Karanpura Coalfield region is shown in Fig. 5c and d. The Sunphotometer and GPS provide reliable water vapour measurements which are restricted only by sparse distributions of sampling sites (Bokoye et al., 2007).

Fig. 4. Average concentrations of AOT (all wavelengths) over South Karanpura Coalfield region during January 2011 and 2014 (winter season).

Please cite this article as: Kumar, A., Krishna, A.P., Winter seasons assessment of atmospheric aerosol over Coalfield region of India ..., Urban Climate (2017), http://dx.doi.org/10.1016/j.uclim.2017.04.006

13

Fig. 5. Map showing a variation of (a, b) temperature and (c, d) precipitable water vapour concentration, as recorded in the month of January in the year 2011 and 2014 by Sunphotometer.

A. Kumar, A.P. Krishna / Urban Climate xxx (2017) xxx–xxx

Please cite this article as: Kumar, A., Krishna, A.P., Winter seasons assessment of atmospheric aerosol over Coalfield region of India ..., Urban Climate (2017), http://dx.doi.org/10.1016/j.uclim.2017.04.006

14

A. Kumar, A.P. Krishna / Urban Climate xxx (2017) xxx–xxx

Table 5 PWV (cm) and Temperature (°C) statistics over South Karanpura Coalfield region for both periods (January 2011 and January 2014). Min

PWV (cm) Temperature (°C)

Max

Mean

Median

Mode

Std. deviation

2011

2014

2011

2014

2011

2014

2011

2014

2011

2014

2011

2014

0.138 12.32

0.302 15.10

1.804 29.50

1.800 26.00

0.449 21.98

0.834 22.00

0.275 22.70

0.50 22.10

0.258 25.00

0.50 19.40

0.258 3.980

0.602 3.017

During January 2011, the concentration of water vapour in the region varied from 0.138 to 1.804 cm with a mean value of 0.449 and standard deviation 0.258. Similarly, water vapour concentration during January 2014 ranged from 0.302 to 1.8 cm, with an average value of 0.834 and a standard deviation of 0.602 cm (Table 5). The Fig. 5 (c and d) demonstrate that the maximum concentration of PWV was high in the south-eastern side of the study area in the year 2011, whereas it was highest on the western aspect of the study area, along with some portion of northern and eastern parts in the year 2014. This may be due to an increase of industries in this region. It is observed that maximum concentration of PWV is recorded in the industrial areas and on the banks of Damodar River, whereas its minimum concentration is found near forest. The high value at industrial zones confirms that the humidity near industrial areas is greater as compared to the coal mining regions, forest, and urban areas. 5.2. Size characteristics of aerosols The Ǻngstrӧm exponent (α, β) is the simplest representation of the spectral variation of aerosol optical thickness (τ) which is determined through the spectral dependence of the measured optical thickness data. The coefficient of Ǻngstrӧm exponent (α, β) is computed using the Ǻngstrӧm empirical formula (Ǻngstrӧm, 1964) shown in Eq. (4): −α

ðτÞ ¼ βλ

ð4Þ

where, λ represents the wavelength in micrometer; τ represents AOT; α represents Ǻngstrӧm exponent and β represents turbidity coefficient, which is equal to columnar AOT at λ = 1 μm. The wavelength of α and β are independent and exhibits a spectral variation based on the physical and chemical characteristics of aerosol (Eck et al., 1999). The exponent ‘α’ characterizes the wavelength dependence of AOT and informs about the aerosol size distribution. ‘β’ corresponds to the columnar AOT at a unit wavelength (λ = 1 μm) and is related to the aerosol loading. The Angstrom wavelength exponent (α) can be calculated using the AOTs (τ) at two different wavelengths using Eq. (4):  −α

ð5Þ

 −α

ð6Þ

τ 1 ¼ β λ1 τ 2 ¼ β λ2 Thus,

−α

τ1 =τ 2 ¼ ðλ1 =λ2 Þ

α

¼ ðλ2 =λ1 Þ

Ln ðτ 1 =τ2 Þ ¼ α Ln ðλ2 −λ1 Þ

ð7Þ ð8Þ

For solving α, α ¼ Ln ðτ 1 =τ2 Þ⁄Ln ðλ2 =λ1 Þ

ð9Þ

The standard range for ‘α’ is 0.5 to 2.5 and for the natural environment, it ranges from 1.3 ± 0.5. There is a high proportion of small particles to large particles (r N 0.5 μ) when the value of ‘α’, is large, such that the ‘τ’ value of the larger wavelength is smaller than the ‘τ’ value of the shorter wavelength. As ‘τ’ values of the greater wavelength approach the ‘τ’ values of the shorter wavelength, the larger sized particles dominate the Please cite this article as: Kumar, A., Krishna, A.P., Winter seasons assessment of atmospheric aerosol over Coalfield region of India ..., Urban Climate (2017), http://dx.doi.org/10.1016/j.uclim.2017.04.006

A. Kumar, A.P. Krishna / Urban Climate xxx (2017) xxx–xxx

15

spatial distribution causing the lowering of ‘α’ values. It is not possible for the ‘τ’ value of a large wavelength to be equal to or greater than the ‘τ’ value of a short wavelength. β has been calculated from either wavelength (340 nm):  α

 α

β ¼ τ 1 λ1 ¼ τ 2 λ2

ð10Þ

where λ is in microns (340 nm = 0.340 μ). The ‘β’ values b 0.1 are generally associated with clear atmosphere while the values N 0.2 are associated with a relatively hazy atmosphere. Parameter ‘τ’ estimates the amount of aerosol present in the atmosphere, while ‘α’ identifies the fraction of accumulation mode particles (r b 1 μm) to coarse-mode particles (r N 1 μm) and thereby describes the aerosol size distribution (Tiwari and Singh, 2013).

Fig. 6. Variation of Ǻngstrӧm exponent (α) and Turbidity coefficient (β) over South Karanpura Coalfield region during (a) January 2011 (b) January 2014.

Please cite this article as: Kumar, A., Krishna, A.P., Winter seasons assessment of atmospheric aerosol over Coalfield region of India ..., Urban Climate (2017), http://dx.doi.org/10.1016/j.uclim.2017.04.006

16

A. Kumar, A.P. Krishna / Urban Climate xxx (2017) xxx–xxx

Thus, for the determination of aerosol size distribution and total columnar aerosol loading over South Karanpura Coalfield, the ‘α’ and ‘β’ are calculated and plotted for the wavelength pair 340 and 870 nm for both 2011 and 2014 (Fig. 6a and b). The high value of ‘α’ implies the dominance of smaller size aerosol particles and vice versa. The value of α340–870 lies between 0.422 and 1.286 with a mean of 0.942 and standard deviation 0.202 during January 2011 whereas in January 2014 the value of α340–870 lies between 0.472 and 2.593 with a mean of 1.490 and standard deviation of 0.640. This indicates that different sizes of aerosols are present in the atmosphere of South Karanpura Coalfield region owing to various sources of origin and weather parameters. The Fig. 6b, shows that ‘α’ is relatively higher, i.e. α N 1.0 (r = 0.5 μm) in January 2014 (winter season) indicating the existence of smaller sized aerosol particles due to industries (Power plant, Sponge iron factory and Steel plant) that generate aerosol due biomass burning and dust aerosol through coal mining activities. On the other hand, in January 2011 (winter season), the ‘α’ values are small i.e. α b 1.0 (r = 0.5 μm) indicating the dominance of larger sized aerosol particles in the year 2011. Fig. 6(a) and (b) shows the variation of the turbidity coefficient (β) values which lies between 0.12 and 1.37 in January 2011 and 0.03 to 1.92 in January 2014. The mean value of β340 is 0.942 and 1.490 during January 2011 and January 2014 respectively, whereas the standard deviation is 0.202 and 0.640 respectively. This shows the nature of variation of AOT at two different reference years. It is remarkable that the temporal variations of ‘α’ is opposite to that of ‘β’ where, high values of ‘β’ are related to the low values of ‘α’. Thus, the present study conducted in the South Karanpura Coalfield region concludes that ‘α’ and ‘β’ are negatively correlated to each other. The findings of the present study thus stand in agreement with the studies conducted by Satheesh et al. (2006) and Reddy et al. (2011). The Ǻngstrӧm parameters are shown in Table 6. 5.3. Spatial distribution of LU/LC during Jan 2011 and Nov 2013 Visual interpretation of satellite images was done using elements of image for delineation and mapping various LU/LC classes using prior knowledge of the study area. The Landsat TM (January 2011) and LISS-IV (November 2013) satellite images were interpreted to delineate various LU/LC classes, viz. Built-up land, industrial area, cropland/fallowland, forest, wasteland, water body within coal mine, waterbody and coal mining area (Fig. 7a and b). The built-up lands are scattered all over the study area. The important densely populated urban centers are Patratu, Bhurkunda, Giddi and Sayal. In 2011, the total area of settlements was 40.24 km2 which comprised 10.56% of the study area, whereas in 2013 it occupied 46.81 km2 which is 12.28% of the total study area, showing an increase of 6.57 km2 in 2013. In the present study area, the main industrial settlements are located in the central part of the South Karanpura Coalfield region and vicinity of the Damodar River. Patratu is the main urban and industrial town in the area in which leading industries such as Patratu Thermal Power Station (P.T.P.S.) and Jindal Steel and Power Limited (J.S.P.L.) are located, along with small-scale industries like coal washeries, sponge iron, glass, cement and brick factory (Kumar and Pandey, 2013b). The industrial settlements occupy an area of 16.8 km2 which is 4.41% of the total study area in the year 2013. It is observed Industrial settlements have developed mainly on wasteland and agricultural land. The agricultural lands in the study area are categorized into cropland and fallowland. It covered an area of 72.97 km2, in 2011, which accounted for 19.15% of the total study area. This increased to 77.87 km2 (20.44%) in the year 2013 (Table 7). The forest area includes dense, open, degraded forest and forest blank in the study area (Fig. 7a and b). The total forest coverage in the region was 132.02 km2 (34.65%) in 2011 but in 2013, it reduced to 128.25 km2 comprising 33.66% of the total geographic area. The maximum forest cover occupies the hilly region in northern and southern part of study area. The wasteland was categorized as open scrub, dense scrub and barren Table 6 Angstrom exponent (α) and Turbidity coefficient (β) statistics over South Karanpura Coalfield region for both periods (January 2011 and January 2014). Parameters

Angstrom exponent (α340–870) Turbidity coefficient (β340)

Min

Max

Mean

Median

Mode

Std. deviation

2011

2014

2011

2014

2011

2014

2011

2014

2011

2014

2011

2014

0.422 0.127

0.472 0.031

1.286 1.379

2.593 1.923

0.942 0.396

1.490 0.388

0.967 0.277

1.42 0.15

1.22 0.23

2.46 0.07

0.202 0.321

0.640 0.506

Please cite this article as: Kumar, A., Krishna, A.P., Winter seasons assessment of atmospheric aerosol over Coalfield region of India ..., Urban Climate (2017), http://dx.doi.org/10.1016/j.uclim.2017.04.006

A. Kumar, A.P. Krishna / Urban Climate xxx (2017) xxx–xxx

17

Fig. 7. Land use/land cover map of the study area (a) January 2011 (b) November 2013.

Please cite this article as: Kumar, A., Krishna, A.P., Winter seasons assessment of atmospheric aerosol over Coalfield region of India ..., Urban Climate (2017), http://dx.doi.org/10.1016/j.uclim.2017.04.006

18

A. Kumar, A.P. Krishna / Urban Climate xxx (2017) xxx–xxx

Table 7 Areal statistics of LU/LC classes during January 2011 and November 2013. LU/LC categories

Built-up land Industrial settlement Cropland/fallowland Forest Wasteland Waterbody/river/reservoir Waterbody within coal mine Coal mining area

January 2011

November 2013

Area (km2)

Area (%)

Area (km2)

Area (%)

40.24 13.8 72.97 132.02 85.89 16.35 3.12 16.58 380.97

10.56 3.62 19.15 34.65 22.55 4.29 0.82 4.35 100

46.81 16.8 77.87 128.25 75.98 16.93 3.52 14.9 381.06

12.28 4.41 20.44 33.66 19.94 4.44 0.92 3.91 100

land in the study area. The total area of wasteland including all the categories was found to be 85.89 km2 (22.55%) in 2011 which got reduced to 75.98 km2 (19.94%) in 2013. Waterbody within coal mines are elongated water bodies measuring up to 1 km in length that are formed due to the accumulation of rain water or ground water in the depressed excavations due to mining (Kumar and Pandey, 2013b). The South Karanpura Coalfield region has a large number of water bodies near the confluence of Damodar and Nalkari River. The area of waterbodies within coal mine was 3.12 km2 (0.82%) during 2011 which increased to 3.52 km2 during 2013 (0.92% of the total area). The natural and human-made water bodies (rivers/streams, lakes, tanks, and reservoirs) comprise the category of waterbodies in the present study area. The study area has a large reservoir in the middle part along with few ponds and tanks located in the south-western part of the region. During 2011, the waterbodies comprised 16.35 km2 (4.29% of the total study area) which remained constant in the year 2013 i.e. 16.93 km2. Coal mining regions are primarily situated in the vicinity of Damodar River (Kumar and Pandey, 2013b). The total coal mining area during 2011 was 16.58 km2, which decreased to 14.9 km2 during 2013 (3.91% of the total area) due to its conversion into the wasteland. Small mining pits were identified on FCC of the satellite image of the region, through its black tone, medium to smooth texture and linear to curvilinear pattern with an irregular shape. The overburden dumps in the study area were identified through its white to light blue tone, coarse to medium texture with contiguous pattern and irregular outer shape. A significant portion of the study area is classified as mining area with mining dump which forms large heaps of dumping materials. The dumps raise the topography of the region to 49 m above ground level (Pandey and Kumar, 2013). The accuracy of the prepared LU/LC map is reliable due to consistent field verifications.

5.4. Spatial relationship between AOT concentration with temperature, PWV and LU/LC The spatial distribution of AOT was analyzed to identify its interrelationship with temperature, PWV (recorded through Sunphotometer) and LU/LC and are discussed elaborately in the following sub-sections.

5.4.1. Relationship between AOT concentrations with temperature Several researchers have investigated the negative forcing of aerosol concentration on surface temperature. Anderson et al. (2003) found that the climate forcing of the aerosol is around −1 Wm−2, with uncertainties between − 1 to − 1.9 Wm− 2. In the present study, the relationship of temperature (recorded through a Sunphotometer) and aerosols stands parallel to the findings of previous studies (Roy, 2008; Kumar and Pandey, 2013a). The observed AOT values at all wavelengths were plotted against temperature for both the years which showed that temperature followed a diurnal trend with low level of relationship with AOT concentration (at all wavelengths) during both the years (Fig. 8a and b). The regression analysis between aerosol and temperature indicates a non-significant negative correlation between AOT concentration and temperature in the study area. Similar outcomes were reported by Kumar and Pandey (2013a), who noted that aerosol concentration does not bear a positive relationship with temperature. Please cite this article as: Kumar, A., Krishna, A.P., Winter seasons assessment of atmospheric aerosol over Coalfield region of India ..., Urban Climate (2017), http://dx.doi.org/10.1016/j.uclim.2017.04.006

A. Kumar, A.P. Krishna / Urban Climate xxx (2017) xxx–xxx

19

Fig. 8. Relationship between AOT and Temperature at major locations in South Karanpura Coalfield region during (a) January 2011 (b) January 2014.

5.4.2. Relationship between AOT concentrations with PWV The spatial variation of water vapour content over South Karanpura Coalfield region during the winter season (January) of 2011 and 2014 is shown in Fig. 5 (c, d). The relationship between average aerosol concentration and PWV were examined through regression analysis. The R2 value of 0.77 and 0.84 for the year 2011 and 2014 respectively, ascertain the relationship between AOT and PWV concentrations in the area. The AOT concentrations at all wavelengths are plotted against PWV for both years (Fig. 9a and b). The industries (P.T.P.S., Sponge iron factory, BCL) and coal mining activities are responsible for high AOT concentration (N2.0) and high PWV concentration (N 1.5 cm) in the study area, which are also found to be strongly correlated to each other (correlation coefficient N 0.8) during the winter season. The high value of AOT in the year 2014 is a result of increased AOT concentration during the study period. It is also observed that the relationship of PWV with Please cite this article as: Kumar, A., Krishna, A.P., Winter seasons assessment of atmospheric aerosol over Coalfield region of India ..., Urban Climate (2017), http://dx.doi.org/10.1016/j.uclim.2017.04.006

20

A. Kumar, A.P. Krishna / Urban Climate xxx (2017) xxx–xxx

Fig. 9. Relationship between AOT and PWV at major locations in South Karanpura Coalfield region during (a) January 2011 (b) January 2014.

finer particle aerosols is higher than the coarse aerosols particles. Thus, the substantial positive correlation of AOT with PWV indicates hygroscopic growth of aerosol particles. As studied by various authors (Ranjan et al., 2007; Alam et al., 2010; Kumar and Pandey, 2013a), the water vapour significantly contributes to AOT concentration. The present study also confirms this through the positive correlation between AOT concentration and PWV in South Karanpura region (Fig. 9a and b). 5.4.3. Relationship between AOT concentrations with LU/LC The LU/LC changes affect the biogeochemistry of atmosphere and its spatial distribution directly affects the aerosol concentration of any region. The relationships between AOT concentration and LU/LC during both the years were analyzed by constructing AOT contour maps at 0.1 intervals for all wavelengths (340–1020 nm) and superimposing it over the LU/LC map of January 2011(Fig. 10a–e) and January 2014 (Fig. 10f–j). It was Please cite this article as: Kumar, A., Krishna, A.P., Winter seasons assessment of atmospheric aerosol over Coalfield region of India ..., Urban Climate (2017), http://dx.doi.org/10.1016/j.uclim.2017.04.006

21

Fig. 10. Spatial relationship between AOT concentration and LU/LC during January 2011 (a–e) and January 2014 (f–j) over South Karanpura Coalfield region.

A. Kumar, A.P. Krishna / Urban Climate xxx (2017) xxx–xxx

Please cite this article as: Kumar, A., Krishna, A.P., Winter seasons assessment of atmospheric aerosol over Coalfield region of India ..., Urban Climate (2017), http://dx.doi.org/10.1016/j.uclim.2017.04.006

22

A. Kumar, A.P. Krishna / Urban Climate xxx (2017) xxx–xxx

observed that the areas with high aerosol concentration (N2.50) lies over the western (near P.T.P.S.) and southeastern parts (near Sponge iron factory) and is characterized by high industrial activity. On contrary, the areas with low aerosol concentration (b 0.60) are found in the northern (near the forest) and western parts (near Patratu reservoir) of the region. This indicates that the smoke released from industries is responsible for an increase in aerosol concentration in the industrial region as compared to the low concentration of aerosol in coal mining and urban areas. Thus, on comparing between both the years, an increased aerosol concentration is noted over the years owing to an increase in the number of industries [13.8 km2 (2011) to 16.8 km2 (2013)], Built-up land [40.24 km2 (2011) to 46.81 km2 (2013)] and decline in forest cover [132.02 km2 (2011) to 128.25 km2 (2013)]. The reduction in the coal mining area [16.58 km2 (2011) to 14.90 km2 (2013)] due to loss of the reserve, also contributes to the increased AOT levels since the wind blows away the overburden dump causing high AOT concentration. Proximity analysis determines the exposure potential of AOT concentration over LU/LC and examines the population within a certain specified range of pollution. The average AOT at 340 nm with reference to the proximity of the five major locations such as P.T.P.S., Bhurkunda urban center, Hehal (sponge iron), Religarha (sponge iron) and Birsa project coal mine (Urimari) were analyzed using concentric buffer zones of 0–1 km, 1–3 km, 3–5 km and 5–8 km (Fig. 11). The buffer zones were created for these regions based on its potential impact by various sources, as per the standards of environmental organization, which are also followed by researchers' worldwide. The Table 8 shows the mean value of AOT in the industrial and coal mining areas as 0– 1 b 1–3 b 3–5 b 5–8 km, whereas in the urban regions it shows 0–1 N 1–3 N 3–5 N 5–8 km due to the presence of industries and mining activity around the urban area. These indicate that the AOT concentration in the industrial area has the highest value followed by mining areas. The buffer analysis (Fig. 11) revealed that the AOT level is directly related to industries having a continuous emission of smoke, followed by mining areas in the region.

Fig. 11. Proximity buffer zones representation with reference to distance from central locations.

Please cite this article as: Kumar, A., Krishna, A.P., Winter seasons assessment of atmospheric aerosol over Coalfield region of India ..., Urban Climate (2017), http://dx.doi.org/10.1016/j.uclim.2017.04.006

A. Kumar, A.P. Krishna / Urban Climate xxx (2017) xxx–xxx

23

Table 8 Average concentration of AOT 340 nm with respect to buffer distance. Buffer distance (km)

P.T.P.S.

Bhurkunda

Hehal (sponge iron)

Raligarha (sponge iron)

Birsa project coal mine (Urimari)

0–1 1–3 3–5 5–8

2.282 1.919 1.543 1.242

0.631 0.696 0.986 1.283

2.225 1.753 1.453 1.135

1.905 1.329 1.122 0.988

0.672 0.884 1.07 1.185

Further, the impact of LU/LC change on aerosol concentration was analyzed around the industrial settlement, coal mining area, waterbodies and waterbodies within coal mining region in South Karanpura Coalfield region using an AOT at 340 nm (due to a dominance of finer particle) for which contour maps for both the year were prepared (Fig. 12). The analysis revealed that the aerosol concentration has increased over the years owing to LU/LC changes especially within the pockets where more industrialization has taken place during the study period. This is followed by coal mining areas. The study shows that AOT concentration is high in plain regions owing to human activities, whereas low AOT values are observed in hilly terrains with dense forest areas with least LU/LC changes. 6. Conclusion The findings of this study provide an understanding about the role of aerosols over coal mining and adjacent regions during winter season. The spatial and temporal variation of AOT along with temperature, PWV, and LU/LC were examined and their relationships were studied. Ǻngstrӧm exponent (α, β) was used to study

Fig. 12. AOT concentration (January 2011 and 2014) at wavelength 340 nm and its spatial relationship with LU/LC of the year 2011 (January) and 2013 (November) over South Karanpura Coalfield and surrounding region.

Please cite this article as: Kumar, A., Krishna, A.P., Winter seasons assessment of atmospheric aerosol over Coalfield region of India ..., Urban Climate (2017), http://dx.doi.org/10.1016/j.uclim.2017.04.006

24

A. Kumar, A.P. Krishna / Urban Climate xxx (2017) xxx–xxx

the size distribution of aerosol particles in the South Karanpura Coalfield region. The spatial distribution of AOT over the region exhibits significant spatial clustering of higher proportions (N2.0) in the industrial area, whereas the lowest (b1.0) was observed over forest areas during January 2011 and January 2014. The value of Ǻngstrӧm exponent (α) and turbidity coefficient (β) lies between 0.422 and 1.286 and between 0.127 and 1.379 respectively during January 2011. In January 2014 ‘α’ lies between 0.472 and 2.593 and ‘β’ between 0.031 and 1.923 which indicates the presence of different sized aerosol particles in the study area. It was also observed that the concentration of finer particles increased in January 2014. The PWV and AOT concentrations are positively correlated with each other indicating that high concentration of water vapour will lead to high AOT levels and vice-versa. However, no significant correlation exists between AOT and temperature. Smoke emissions owing to biomass burning, anthropogenic activities and a large number of big and smallscale industries are the leading causes of increased air pollution and aerosol concentration in the study area. The present multi-parameter based study revealed that the environmental conditions in South Karanpura Coalfield region are adversely affected by industrial growth and concomitant environmental pollution. The carbonaceous aerosols and water vapour coupled with the frequent LU/LC changes in the coal mining regions adversely affect the radiation balance, air pollution, and aerosol distribution. The correlation between biomass burning and aerosol concentration indicated the impact of LU/LC change on regional AOT levels and hence provided better understanding regarding the extent and pattern of land-use practices being the sources of aerosol distributions. The findings of this study may be useful for scientists and policy makers. The results obtained in the present study points to the need for extensive land use transformation, as well as the formulation of an environmental development policy along with the imposition of strict regulations for sustainable land use, water, energy, and green cover. Acknowledgements This research was supported by Birla Institute of Technology, Mesra, Ranchi, under the Institute research fellowship for Ph.D. programme. Authors are thankful to Centre of Excellence in Climatology, Birla Institute of Technology, Mesra for providing MICROTOPS-II Sunphotometer instrument. Authors are also grateful to Mr. Atul Kumar Tiwari and Mr. Sams Raza for their support during data collection. References Ǻngstrӧm, A., 1964. The parameters of atmospheric turbidity. Tellus 16, 64–75. Armstrong, J.A., Russel, P.A., Darmel, D.C., 1980. Particle From Surface Mining, Part I - Vertical Measurements, Report No. EPA-600=9-80041 (Research Triangle Park, NC: USEPA, Industrial Environmental Research Laboratory). Anderson, T.L., Masonis, S.J., Covert, D.S., Ahlquist, N.C., Howell, S.G., Clarke, A.D., Mcnaughton, C.S., 2003. Variability of aerosol optical properties derived from in situ aircraft measurements during ACE-Asia: characterization of Asian aerosols and their radiative impacts on climate. J. Geophys. Res. 108 (D23) 15-1. Alam, K., Iqbal, J.M., Blaschke, T., Qureshi, S., Khan, G., 2010. Monitoring spatiotemporal variations in aerosols and aerosol–cloud interactions over Pakistan using MODIS data. Adv. Space Res. 46, 1162–1176. Bernstein, L., Bosch, P., Canziani, O., Chen, Z., Christ, R., Davidson, O., 2007. Summary for Policymakers; Climate Change 2007 Synthesis Report. Intergovernmental Panel on Climate Change (IPCC), Geneva, Switzerland. Bokoye, A.I., Royer, A., Cliché, P., O'Neill, N., 2007. Calibration of sun radiometer-based atmospheric water vapour retrievals using GPS meteorology. J. Atmos. Ocean. Technol. 24, 964–979. Balakrishnaiah, G., Reddy, B.S.K., Gopal, K.R., Reddy, R.R., Reddy, L.S.S., Swamulu, C., Ahammed, Y.N., Narasimhulu, K., Krishnamoorthy, K., Suresh, B.S., 2012. Spatiotemporal variation in aerosol optical and cloud parameters over southern India retrieved from MODIS satellite data. Atmos. Environ. 47, 435–445. Chadwick, M.J., Highton, N.H., Lindman, N., 1987. Environmental Impacts of Coal Mining and Utilisation. A Complete Revision of Environmental Implications of Expanded Coal Utilization. Pergamon Press, England. CIL, 1993. Coal Atlas of India, Central Mine Planning and Design Institute. Coal India Limited, Calcutta, India. Cheung, H., Wang, T., Baumann, K., Guo, H., 2005. Influence of regional pollution outflow on the concentrations of fine particulate matter and visibility in the coastal area of southern China. Atmos. Environ. 39, 6463–6474. Di Girolamo, L., Bond, T.C., Bramer, D., Diner, D.J., Fettinger, F., Kahn, R.A., Martonchik, J.V., Ramana, M.V., Ramanathan, V., Rasch, P.J., 2004. Analysis of Multi-angle Imaging SpectroRadiometer (MISR) aerosol optical depths over greater India during winter 2001–2004. Geophys. Res. Lett. 31 (23). http://dx.doi.org/10.1029/2004gl021273. Dey, S., Tripathi, S.N., Singh, R.P., Holben, B.N., 2005. Seasonal variability of the aerosol parameters over Kanpur, an urban site in IndoGangetic basin. Adv. Space Res. 36 (5), 778–782. Eck, T.F., Holben, B.N., Reid, J.S., Dubovik, O., Smirnov, A., O'Neill, N.T., Slutsker, I., Kinne, S., 1999. Wavelength dependence of the optical depth of biomass burning, urban, and desert dust aerosols. J. Geophys. Res. 104 (D-24), 31333–31349.

Please cite this article as: Kumar, A., Krishna, A.P., Winter seasons assessment of atmospheric aerosol over Coalfield region of India ..., Urban Climate (2017), http://dx.doi.org/10.1016/j.uclim.2017.04.006

A. Kumar, A.P. Krishna / Urban Climate xxx (2017) xxx–xxx

25

Ghose, M.K., Majee, S.R., 2007. Characteristics of hazardous airborne dust around an Indian surface coal mining area. Environ. Monit. Assess. 130 (1–3), 17–25. Guofeng, W., Leeuw, J., Skidmore, A.K., Yaolin, L., Prins, H.H.T., 2010. Comparison of extrapolation and interpolation methods for estimating daily photo synthetically active radiation (PAR) – a case study of the Poyang lake national nature reserve, China. Geo-spatial Information Science 13 (4), 235–242. Ghrefat, H.A., Howari, F.M., 2013. Rate of deposition and quality of sedimentation dust in Al Ain and Ras Al Khaimah, United Arab Emirates. Arab. J. Geosci. 6, 1033–1039. Heinrich, J., 2003. Nonallergic respiratory morbidity improved along with a decline of traditional air pollution levels: a review. Eur. Respir. J. 21 (40), 64–69. Ichoku, C., Levy, R., Kaufman, Y.J., Remer, L.A., Li, R.R., Martins, V.J., Holben, B.N., Abuhassan, N., Slutsker, I., Eck, T.F., Pietras, C., 2002. Analysis of the performance characteristics of the five-channel Microtops II Sunphotometer for measuring aerosol optical thickness and precipitable water vapour. Journal of Geophysical Research: Atmospheres. 107 (D13). Ibald-Mulli, A., Timonen, K.L., Peters, A., Heinrich, J., Wölke, G., Lanki, T., ten Brink, H.M., 2004. Effects of particulate air pollution on blood pressure and heart rate in subjects with cardiovascular disease: a multicenter approach. Environ. Health Perspect. 112 (3), 369. IPCC Climate Change, 2007. The Physical Science Basis, Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, United Kingdom and New York, USA. Kumar, U., 1995. Underground coal mining in India challenges and prospects. Transactions 92 (2), 1–7. Kinney, P.L., Lippmann, M., 2000. Respiratory effects of seasonal exposures to ozone and particles. Archives of Environmental Health: An International Journal 55 (3), 210–216. Kanakidou, M., Seinfeld, J.H., Pandis, S.N., Barnes, I., Dentener, F.J., Facchini, M.C., van Dingenen, R., Ervens, B., Nenes, A., Nielsen, C.J., Swietlicki, E., Putaud, J.P., Balkanski, Y., Fuzzi, S., Horth, J., Moortgat, G.K., Winterhalter, R., Myhre, C.E.L., Tsigaridis, K., Vignati, E., Stephanou, E.G., Wilson, J., 2004. Organic aerosol and global climate modelling: a review. Atmospheric Chemistry and Physics Discussions 4, 5855–6024. Kaskaoutis, D.G., Kambezidis, H.D., Hatzianastassiou, N., Kosmopoulos, P.G., Badarinath, K.V.S., 2007. Aerosol climatology: on the discrimination of the aerosol types over four AERONET sites. Atmos. Chem. Phys. Discuss. 7, 6357–6411. Kalapureddy, M.C.R., Devara, P.C.S., 2008. Characterization of aerosols over oceanic regions around India during pre-monsoon 2006. Atmos. Environ. 42 (28), 6816–6827. Kedia, S., Ramachandran, S., 2009. Variability in aerosol optical and physical characteristics over the Bay of Bengal and the Arabian Sea deduced from Ǻngstrӧm exponents. J. Geophys. Res. 114 (D14). Kumar, A., Pandey, A.C., 2013a. Spatio-temporal assessment of urban environmental conditions in Ranchi township, India using remote sensing and geographical information system techniques. International Journal of Urban Sciences 17 (1):117–141. http://dx.doi.org/ 10.1080/12265-934.2013.766501. Kumar, A., Pandey, A.C., 2013b. Evaluating impact of coal mining activity on land use/land cover using temporal satellite images in South Karanpura coalfields and environs, Jharkhand State, India. International Journal of Advanced Remote Sensing and GIS 2 (1):183–197 Available online at:. http://technical.cloudjournals.com/index.php/IJARSG/article-/vie/Tech-110/pdf. Kumar, A., Krishna, A.P., 2016. Assessment of groundwater potential zones in coal mining impacted hard-rock terrain of India by integrating geospatial and analytic hierarchy process (AHP) approach. Geocarto International 1–25. http://dx.doi.org/10.1080/ 10106049.2016.-1232314. Kumar, A., Pandey, A.C., Kumar, M., 2016. Evaluating variability of aerosol concentration in coal mining areas: a case study from Patratu coal mining region, Jharkhand. Conference GSI. 4:pp. 119–129. http://dx.doi.org/10.17491/cgsi/2016/95904. Latha, K.M., Badrinath, K.V.S., 2004. Association of aerosol optical depth with near surface aerosol properties in urban environment. Indian J. Radio & Space Phys. 33 (4), 256–259. McCartney, E.J., 1976. Optics of the Atmosphere: Scattering by Molecules and Particles. John Wiley & Sons, New York, p. 421. Morys, M., Mims, F.M.I.I.I., Hagerup, S., Anderson, S.E., Baker, A., Kia, J., Walkup, T., 2001. Design, calibration, and performance of MICROTOPS II handheld ozone monitor and sun photometer. J. Geophys. Res. 106, 14573–14582. Munroe, D.K., Wolfinbarger, S.R., Calder, C.A., Shi, T., Xiao, N., Lam, C.Q., Li, D., 2008. The relationships between biomass burning, landcover/-use change, and the distribution of carbonaceous aerosols in mainland Southeast Asia: a review and synthesis. Journal of Land Use Science 3 (2–3), 161–183. Mushtaq, F., Pandey, A.C., 2013. Assessment of land use/land cover dynamics vis-à-vis hydrometeorological variability in Wular Lake environs Kashmir Valley, India using multitemporal satellite data. Arab. J. Geosci. 7:4707–4715. http://dx.doi.org/10.1007/s12517-013-1092-1. Prakash, A., Gupta, R.P., 1998. Land-use mapping and change detection in a coal mining area - a case study in the Jharia coalfield, India. Int. J. Remote Sens. 19 (3):391–410 Available online at:. 10.1080-/014311698216053. Pope III, C.A., 2000. Review: epidemiological basis for particulate air pollution health standards. Aerosol Sci. Technol. 32, 4–14. Penttinen, P., Timonen, K.L., Tiittanen, P., Mirme, A., Ruuskanen, J., Pekkanen, J., 2001. Number concentration and size of particles in urban air: effects on spirometric lung function in adult asthmatic subjects. Environ. Health Perspect. 109 (4), 319. Prasad, A.K., Singh, R.P., Singh, A., 2006. Seasonal climatology of aerosol optical depth over the Indian subcontinent: trend and departures in recent years. Int. J. Remote Sens. 27 (12):2323–2329. http://dx.doi.org/10.1080/-01431160500043665. Pawar, G.V., Devara, P.C.S., More, S.D., Kumar, P., Aher, G.R., 2012. Determination of aerosol characteristics and direct radiative forcing at Pune. Aerosol Air Qual. Res. 12, 1166–1180. Pandey, A.C., Kumar, A., 2013. Analysing topographical changes in open cast coal-mining region of Patratu, Jharkhand using CARTOSAT-I Stereopair satellite images. Geocarto International 27 (9):731–744. http://dx.doi.org/10.1080/10106049-.2013838309. Pandey, S.K., Bhattacharya, T., Chakraborty, S., 2016. Metal phytoremediation potential of naturally growing plants on fly ash dumpsite of Patratu thermal power station, Jharkhand, India. Int. J. Phytoremediation 18 (1):87–93. http://dx.doi.org/10.1080/15226514.-2015.1064353. Reagan, J., Thome, K., Herman, B., Stone, R., Deluisi, J., Snider, J., 1995. A comparison of columnar water-vapor retrievals obtained with near-IR solar radiometer and microwave radiometer measurements. J. Appl. Meteorol. 34, 1384–1391. Rathore, P.K., Ernest Raj, P., Devara, P.C.S., 2005. Comparison of Precipitable Water Measured by Sunphotometer and That Estimated From Surface Meteorological Parameters, First Prof. R. Ananthakrishnan Memorial Conference on Atmospheric Science, Climate Change and Environment Studies. January 18–19, 2005. http://meteo.lcd.lu/papers/foreign_papers/-comparison_ precipitable_water.pdf.

Please cite this article as: Kumar, A., Krishna, A.P., Winter seasons assessment of atmospheric aerosol over Coalfield region of India ..., Urban Climate (2017), http://dx.doi.org/10.1016/j.uclim.2017.04.006

26

A. Kumar, A.P. Krishna / Urban Climate xxx (2017) xxx–xxx

Ranjan, R.R., Joshi, H.P., Iyer, K.N., 2007. Spectral variation of total column aerosol optical depth over Rajkot: a tropical semi-arid Indian station. Aerosol Air Qual. Res. 7, 33–45. Roy, S.S., 2008. Impact of aerosol optical depth on seasonal temperatures in India: a spatio-temporal analysis. Int. J. Remote Sens. 29 (3): 727–740. http://dx.doi.org/10.1080-/01431160701352121. Reddy, B.S.K., Kumar, K.R., Balakrishnaiah, G., Gopal, K.R., Reddy, R.R., Reddy, L.S.S., Narasimhulu, K., Bhaskara, Vijaya, Rao, S., Kiran Kumar, T., Balanarayana, C., Moorthy, K.K., Suresh Babu, S., 2011. Aerosol climatology over an urban site, Tirupati (India) derived from columnar and surface measurements: first time results obtaineds from a 30-day campaign. J. Atmos. Sol. Terr. Phys. 73, 1727–1738. Schmid, B., Wehrli, C., 1995. Comparison of sunphotometer calibration by use of the Langley technique and standard lamp. Appl. Opt. 34, 4500–4512. Streets, D.G., Bond, T.C., Carrnichael, G., Fernandes, S.D., Fu, Q., He, D., Klimont, Z., Nelson, S.M., Tsai, N.Y., Wang, M.Q., Woo, J.H., Yarber, K.F., 2003. An inventory of gaseous and primary aerosol emissions in Asia in the year 2000. J. Geophys. Res. 108 (D21). http://dx.doi. org/10.1029/2002JD003093. Sarma, K., 2005. Impact of Coal Mining on Vegetation: A Case Study in Jaintia Hills District of Meghalaya, India. 1–85. International institute for geo-information science and earth observation enschede (The Nederlands) and Indian institute of remote sensing, national remote sensing agency (NRSA), Department of space, Dehradun, India. http://www.itc.nl/library/-Papers_2005/msc/ereg-/sarma. pdf (accessed on: 15/10/2014). Satheesh, S.K., Deepshikha, S., Srinivasan, J., 2006. Impact of dust aerosols on earth atmosphere clear-sky albedo and its short wave radiative forcing over African and Arabian regions. Int. J. Remote Sens. 27, 1691–1706. Schuster, G.L., Dubovik, O., Holben, B.N., 2006. Ǻngstrӧm exponent and bimodal aerosol size distribution. J. Geophys. Res. 111, D07207. http://dx.doi.org/10.1029/2005JD006328. Sacks, J.D., Stanek, L.W., Luben, T.J., Johns, D.O., Buckley, B.J., Brown, J.S., Ross, M., 2011. Particulate matter induced health effects: who is susceptible? Environ. Health Perspect. 119:446–454. http://dx.doi.org/10.1289/ehp.1002255. Srivastava, A.K., Tripathi, S.N., Dey, S., Kanawade, V.P., Tiwari, S., 2012. Inferring aerosol types over the Indo-Gangetic Basin from ground based sunphotometer measurements. Atmos. Res. 109, 64–75. Tiwary, R.K., Dhar, B.B., 1994. Environmental pollution from coal mining activities in Damodar River Basin, India. Mine Water Environ. 13, 1–10. Tulloch, M., Li, J., 2004. Applications of satellite remote sensing to urban air-quality monitoring: status and potential solutions to Canada. Environmental Informatics Archives 2, 846–854. Tiwari, S., Singh, A.K., 2013. Variability of aerosol parameters derived from ground and satellite measurements over Varanasi located in the Indo-Gangetic Basin. Aerosol Air Qual. Res. 13:627–638. http://dx.doi.org/10.4209/aaqr.2012.06.0162. Uno, I., Carmichael, G.R., Streets, D., Satake, S., Takemura, T., Woo, J.H., Uematsu, M., Ohta, S., 2003. Analysis of surface black carbon distributions during ACE-Asia using a regional-scale aerosol model: characterization of Asian aerosols and their radiative impacts on climate. J. Geophys. Res. 108 (D23) ACE4-1. 10.1029/-2002JD003252. Venn, A.J., Lewis, S.A., Cooper, M., Hubbard, R., Britton, J., 2001. Living near a main road and the risk of wheezing illness in children. Am. J. Respir. Crit. Care Med. 164 (12), 2177–2180. Wang, L., Gong, W., Xia, X., Zhu, J., Li, J., Zhu, Z., 2015. Long-term observations of aerosol optical properties at Wuhan, an urban site in Central China. Atmos. Environ. 101, 94–102. Zveryaev, I.I., Allan, R.P., 2005. Water vapor variability in the tropics and its links to dynamics and precipitation. J. Geophys. Res. Atmos. 110 (D21). http://dx.doi.org/10.1029/2005-JD006033.

Please cite this article as: Kumar, A., Krishna, A.P., Winter seasons assessment of atmospheric aerosol over Coalfield region of India ..., Urban Climate (2017), http://dx.doi.org/10.1016/j.uclim.2017.04.006