Characterization and Spatiotemporal Variation of Urban Ambient Dust Fallout in Central India

Earlier studies on air pollution measurements found that significant dust fallout is a severe problem in central India, with levels of fine particulate matter being several time higher than the prescribed limits. This study mainly examined the spatiotemporal variation and source apportionment of ambient dust fallout (coarser dust particles size > 20 micron) in urban areas of central India. This paper deals the spatiotemporal variation of dust fallout at ambient levels of environmentally defined urban receptors. The dust fallout levels were found to be in the range of 13.73 ± 5.46 to 78.82 ± 34.81 g/m/month; twoto five-fold higher than earlier measurements in same region during 1999–2000. The spatiotemporal variation of dust fallout levels across the selected environmentally defined urban zones was found to be 89%, and the different spatial variabilities of 24 chemical constituents of the dust fallout found in this work indicate the complexity of its source signatures. Statistical boxplots of longitudinal data of dust fall chemical constituents are also carried out to assess the means and outliers among different percentile levels.


INTRODUCTION
Poor technical establishments for combustion processes involved with various ferrous and non-ferrous metallurgical industries, paved road makings, on-road traffic management and civil constructions in many urban areas of south and southeast Asian countries has shown multi-fold higher emission of coarser particulates having particle aerodynamic size > 10-20 µm (Murty and Murty, 1973;Tripathi et al., 1991;Khare et al., 1998;Thakur et al., 2004;Das et al., 2005;Budhavant et al., 2012) compared to fine particulate fractions; indicator aerosols of high temperature combustion activities (Buzcu-Guven et al., 2007;Gadkari and Pervez, 2008;Kothai et al., 2008).Regional and continental transportation along with chemical characterization of atmospheric dust fallout has been conducted earlier in different regions world over, described Urban scale dust fall is greatly affected by local sources of emissions, meteorology, topography and other physiographic factors results higher degree of spatiotemporal variations in coarser particulates compared to fine ones (Jackson et al., 1973;Das et al., 2005;Freiman et al., 2006;Balakrishna and Pervez, 2009;Wang et al., 2009).
Analysis of spatial and temporal variation of air pollution data are valuable, especially for dispersion modelling, urban and regional planning, and monitoring network design (Imandel et al., 1981;Liu et al., 1981;Vora and Bhatnagar, 1987;Irvine et al., 1989;Tripathi et al., 1991;Sharma and Pervez, 2003;Das et al., 2005;Dubey and Pervez, 2008;Zhang et al., 2010;Pervez et al., 2012).Earlier studies on dust fallout characterization in India are mainly focused on selected toxic load viz.mercury (Thakur et al., 2004), lead, arsenic (Deb et al., 2002) in specific environmentally defined locations of urban scale; lacking in comprehensive assessment of spatiotemporal variation observations of dust fallout pattern in urban centres.
The work presented here is the initial part of source apportionment studies on urban dust fallout that were conducted in an urban-industrial area of central India.Dust fallout measurements at ambient-outdoor atmospheric levels in different environmentally defined urban zones (residential, commercial and sensitive regions) along with their chemical characterization for markers species of identified sources has been conducted to generate longitudinal dust receptor database for source apportionment studies.Quantification data of dust fallout and their chemical parameters have been analysed for spatiotemporal variation pattern.

Study Design
The goal of the study is to investigate the spatiotemporal behaviour and source signatures of atmospheric dust fallout at defined receptor sites of the urban area Raipur, Chhattisgarh.This paper deals the investigation of spatiotemporal variation of dust fallout in an urbanindustrial area of Raipur located at 21°8'24''N, 81°22'48''E.The area of study is 87 mile -2 , having population density of 11,466 mile -2 .To achieve the objectives of the study, a non-probability based stratified random sampling plan using longitudinal study design in space-time framework has been adopted (Gilbert, 1987;U.S.EPA, 2003).Eight sampling sites have been selected from environmentally defined urban zones (residential, commercial and sensitive) for the dust fallout sampling at receptor zone of the study region (Fig. 1) on the basis of previous meteorological records of wind direction, population and anthropogenic activity pattern of the area.Out of eight sampling sites, four sampling sites were selected from different residential areas, two were from different commercial zones and remaining two sites from two different sensitive areas: one was academic institution and another belong to hospital area.Details of the sampling area have been furnished in (Table 1).Selection criteria of number and location of sampling sites in the study region is based on longitudinal study design' type described earlier ( Abt et al., 2000;Crist et al., 2008;Begum et al., 2009).In order to compare the spatial variability of dust fallout across eight sampling sites, coefficients of spatial variation were calculated.The overall geo mean (GM) across the eight sites and corresponding standard deviation were computed using measured concentrations at each site.The coefficient of spatial variation (CV) of dust fallout was computed by dividing the standard deviation by overall geometric mean value of 24 monthly measurements during the tenure of two years (Roosli et al., 2001).

Sampling Plan
A set of three glass based dust collection Jars with standard specifications (Dimension: dia-23" ht-45") (Katz, 1977;Lodge, 1989;Thakur et al., 2004b) have been placed at the height of 10-15 ft (roof-tops of the civil structures) in each sampling site (Esmen, 1973;Fisher, 1959).Dust collection jars have been washed with detergent and then cleaning was extended using acidic mixture of 3:1 ratio HCl and HNO 3 , than washed with ultra-pure double distilled water before sampling.About a litre of double distilled water was placed in each Jar and a net sheet (size: 20 mesh) was placed on mouth of the Jars.Field blank measurements have been carried out by placing similar type of collection jar; opening covered by polyethylene sheets and having a litre of double distilled water in each site for a single month simultaneously with dust fallout monitoring.Details of sample transportation, preservation, and extraction of soluble and insoluble fractions and weighing measurements have been described elsewhere (Balakrishna and Pervez, 2009).Five replicate measurements of weighing were done to minimize weighing error less than 5%.The sampling has been continued for 24 months, and a total of 192 samples from eight sites (24 samples from each site) for 24 months have been collected.The dust fall rate was calculated for each site using the following equation (Katz, 1969

Sample Preparation and Data Analysis
All the samples of dust fallout were partitioned to insoluble and soluble fractions.Portions of insoluble fractions were digested using recommended procedures described elsewhere (Balakrishna and Pervez, 2009).Soluble fractions, insolubles and digested portions of insoluble were then subjected to chemical analysis of selected constituents (OC, EC, Si, SO 4 2− , Cl − , NO 3 − , S, Fe, Al, Ca, Mg, Na, K, Cr, Mn, Ni, As, Hg, V, Zn, Cu, Pb, Co and Sb) using recommended protocol of analysis described earlier (Balakrishna and Pervez, 2009).Specialized cleaning and sampling techniques were used during all stages of sample collection to prevent contamination.Dust sampling jars after the sampling immediately transferred to the laboratory.The samplers were washed with double distilled water and the volume has been made up to 500 mL.Water samples having suspended dusts were partitioned to three portions and each portion was filtered using Whatman Filter Paper No. 42 in prewashed polyethylene bottles.All insoluble and soluble fractions of dust fall samples have been stored at 4°C or less until chemical analysis.
Insoluble in three What man filters(A,B,C) have been assigned to quantify selected inorganic species, Silica (Si) and carbon separately, while soluble fractions were collectively used for inorganic analysis of elemental and anions both.One dust loaded Whatman filter 42(A) was digested with 10 mL mixture of nitric acid (HNO 3 ) and hydrogen peroxide (H 2 O 2 ) in a ratio of 3:1 using Teflon digestion bombs.Teflon bombs were then kept in temperature controlled oven (Tempo, Model 1453) at 200°C for 6 hrs (Katz, 1977;Envirotech, 2000), followed by cooling and filtration using 0.001 M nitric acid.Final volume of the digested sample was made to 25 mL using distilled water.Digested samples (A) along with soluble fractions of dust fallout were analysed for selected cations (Fe, Al, Na, K, Ca, Mg, Cr, Mn, Ni, As, Hg, V, Zn, Cu, Pb, Co and Sb) and anions (SO 4 2− , NO 3 − , Cl − ), with sulphur content, respectively, using standard procedures.Organic carbon and elemental carbon along with Silica (Si) has also been determined in dust samples using remaining two dust loaded Whatman filters (B, C).Selection of chemical species has been decided on the basis of indicator species of selected industrial emissions (CPCB, 2010).
Elemental species (Fe, Al, Ca, Mg, Cr, Mn, Ni, As, Hg, V, Zn, Cu, Pb, Co and Sb) have been determined using inductive coupled plasma-atomic emission spectrophotometer (ICP-AES) (Jobin-Yvon Horiba ICP Spectrometer Version 3.0) (Katz, 1969;Montaser and Golightly, 1987).Calibration of instrument was done using Merck standard ICP solution of concentration range 0.1-10 ppm.The wavelengths used in AES ranges from the upper part of the vacuum ultraviolet (160 nm) to the limit of visible light (800 nm).
Organic Carbon and Elemental Carbon were determined using ignition-loss method due to higher concentration of OC and EC in dust loaded Whatman filter (B).It was reported that organic carbon is vaporized at 500°C while elemental carbon is eliminated at 900°C (CPCB, 2010).Another portion of insoluble fraction of dust fallout was placed in a preweighed platinum crucible and heated at 500°C for 6 hrs in a muffle furnace (Labtech Model TIC 4000), after cooling, crucible was weighed and the process was repeated till constant weight was obtained.The difference in weight of pre-and post-heating has been taken as organic carbon fraction of dust fallout.Again same sample was heated at 900°C till weighing difference became constant.The weight difference has been assigned to elemental carbon fraction (Balakrishna and Pervez, 2009;CPCB, 2010).
Third dust loaded Whatman filter (C) was used to quantify Si.It was ignited, in a platinum crucible and weighed.The residue was treated with few drops of H 2 SO 4 and then with HF, and ignited to a constant weight.The loss in weight was recorded as SiO 2 (Mendham et al., 2002).Sodium and potassium were determined in a portion of digested sample of insoluble dust fraction by flame photometer (Systronics, Model130) using recommended conditions of operation (Mendham et al., 2002).

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) have been determined ion chromatographically using soluble fractions of dust fallout.An ion chromatograph (Dionex Model ICS-2000) along with suitable columns has been used for analysis.The standard solution has been used from Dionex and it was Standard-II and mobile phase was 38 mM KOH.Stock solution of Standard-II was 1000 ppm of respective anionic species and diluted to 1 ppm using triple distilled water.The pH of the mobile phase solution was adjusted to 5.0.Total volume has been made up 50 mL (Dionex, 1981;Marko-Verga et al., 1984;Dionex, 2005).The sulphur in sample has also been determined by ion chromatography after oxidation of all sulphur species to sulphate using separate portion of insoluble fraction of dust fall.Oxidation with hydrogen peroxide first under the basic conditions and subsequently under the strongly acidic conditions were required for quantitative conversion of all sulphur species to sulphate.The procedure is based on oxidation of all type sulphur forms present in dust samples to sulphate and subsequently determined by ion chromatography (Dionex, 1981;Chriswell et al., 1986).Replicate measurements of all species have been done to maintain relative standard deviation within 5%.All chemicals and reagents used in chemical analysis were Analytical Merck grade.Samples are re-analyzed when quality control standards differ from specifications by more than ± 5% or when replicates (at levels exceeding 10 times detection limits) differ by more than ± 10%.Data of dust fallout levels and its chemical constituents along with spatial variability across the sites have been presented in Table 2. Statistical box plots of chemical constituents and temporal variability of dust fallout have been presented in Figs. 2 and 3, respectively.

RESULTS AND DISCUSSION
The results documented in Table 2, Figs. 2 and 3 have explained the spatial and temporal variability of chemical constituents of dust fallout measured at ambient level of selected receptors of the study region.Dust fallout levels in selected receptor sites belong to different environmentally defined zones (residential, commercial and sensitive) have shown higher trend compared to prescribed standards in Australia (0.01 g/m 2 /month), as Indian standards have not yet developed (Table 2).Many-fold higher concentrations occurred in every receptor site compared to prescribed standards and earlier reported values (Ayling and Bloom, 1976;Egami et al., 1989;Gulson et al., 1995;Essumang et al., 2006;Wang et al., 2009).Higher deviation pattern from geomean compared to earlier studies conducted in the study region during 1999-2000 (Thakur and Deb, 2000;Deb et al., 2002b;Thakur et al., 2004c) has explained the multicomplexity in the source contributions to receptor dust fall.As far as spatial variability is concern, lower variability has been observed compared to PM 10 measurements across the sites in earlier studies (Dubey, 2011), attributed to uniformity in the source characteristics of dust fall in study region compared to PM 10 .The highest dust fall of 162.04 g/m 2 /month has been found at R-2 in April 2009, whereas the lowest level of 5.55 g/m 2 /month has been found at R-6 in September 2009.One of the residential sites (R-2) has shown 3-7 fold higher dust fall compared to all other sites due its close proximity to industrial areas.Other residential sites have shown comparable levels of dust fall compared to commercial zone with similar deviation pattern from geomean; explained the dominance of regional sources compared to local ones.Interestingly one site belong to sensitive zone (R-8) has shown second highest value of dust fall (28.39g/m 2 /month), more than sites belong to commercial zones.This might be due to concentric effect of various sources of dust emissions observed in wind channels over the Raipur region (Fig. 4).Other sensitive receptor site (R-7) has shown lower trend of dust fall compared to residential sites.Interestingly, every receptor site has shown uniform temporal variation with higher trend in summer and lower levels in post-rainy seasons during the sampling period of two years.R-2 has shown higher trend of temporal variation, while R-4 and R-7 have shown lower temporal variation; R-3 and R-8 have shown moderate temporal variation in dust fallouts.
Urban dust is mainly due to emissions resulting from industrial and road-traffic sources compared to crustal source, whereas rural dust is mainly of crustal origin (Gadkari and Pervez, 2007;Gadkari and Pervez, 2008;Pervez et al., 2012).Markers of emissions resulting from coal burning, road-traffic activities, civil constructions and biomass burning have been found in significant levels in dust fallout at all urban receptor sites.Inverse relationship in the dominance of Organic carbon (OC) and elemental carbon (EC) has been observed among the monitoring sites; highest OC was found at Hospital area (R-7), whereas EC has shown its strong presence at R-2 due to its close proximity to industrial area.Except Birgaon Residential Site (R-2), all other three residential sites have shown 2-3 folds higher OC compared to EC values in dust fallout, while commercial zones have shown lower trend of OC/EC ratio (1.2-1.8)compared to residential zones.Sensitive zones have shown 2-5 folds higher OC compared to EC in dust fallout.Lower trend of spatial variability in OC levels across the receptor sites is explained the dominance and continuity of local sources across the receptors.Very high spatial variability of EC is due to R-2 where EC was found exceptionally high compared to other sites, otherwise EC has also similar trend of spatial variability compared to OC. Statistical boxplots (Fig. 2) have shown that mean values of longitudinal measurements of OC and EC were mostly projected close to 75 th percentile and lower outliers have shown far projection from 5 th percentile compared to upper outliers' projection from 95 th percentile.
As far as anionic species is concern, Sulphates appear with good geomean at Birgaon (R-2) (4.38 g/m 2 /month), while nitrate was present in dust fall with high geomean (1.10 g/m 2 /month) at Shastri bazaar (R-5) compared to other receptors.In contrast to sulphate and nitrate, chloride has shown highest presence at Hospital site (R-8) belong to sensitive zone and comparable levels have been observed at one commercial site and one residential site.It has also been observed that relative strength of all three anionic species (SO 4 2− /NO 3 − /Cl − ) was similar in four sites (R-2, R-4,5,6,7) belong to different environmentally defined zones and the order of occurrence is: SO 4 . Except NO 3

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, both anionic species have shown higher degree of spatial variability across the receptors; vehicular exhaust emissions might be the main reason behind the observations of lower spatial distribution pattern of nitrate.NO 3 − has shown higher deviation from mean at R-2 and R-7; SO 4 2− at R-3 and Cl − at R-1, 4 & 6 have shown higher deviation pattern.In all case anionic species, mean is projected close to 50 th percentile; lower outliers are projected far away from 5 th percentile in case of Chloride ions as compared to other anionic species.
Sodium, Potassium and Magnesium has shown similar pattern of deviation from their respective geomean and higher geomean values of these species have been found at Sundar   Unexpectedly iron, silica and aluminium have shown different approach in dust fallouts measured in selected receptors.Iron and silica have shown uniform higher levels at R-2, while aluminium has shown higher level at R-1. Very high values of Si at R-2 compared to other receptors might be due to two reasons: 1) sandy character of local soils, 2) huge civil constructions in nearby areas.Higher iron content in dust fallouts at R-2 is due to contribution from ferrous metallurgical industrial sources located near to this site.Aluminium has shown major presence in dust fallouts at R-8, comparable to R-1.Sulfur has shown higher geomean value (4.38 g/m 2 /month) at R-2 and lower value at R-1. Iron and Aluminium both have shown several-fold  higher concentration compared to studies conducted in same area a decade before and China (Thakur et al, 2004;Yadav and Rajamani, 2006).
It has been studied that industrial sources were good contributor of heavy metals in urban dust fallout (Balakrishna and Pervez, 2009).Minor constituents (heavy metals) have shown different pattern of accumulation in dust fallout measured at different receptor zones, but Manganese distribution is nearly uniform among all receptor sites attributing to similar source of origin (Ferroalloy industries) in the study region.The heavy metals Arsenic, Mercury, Vanadium, Lead, Cobalt and Antimony has shown their geomean values higher at Birgaon (R-2) with 0.05, 0.02, 0.01, 0.11, 0.03 and 0.02 g/m 2 /month, respectively compared to earlier studies (Fang et al., 2011); among all these Lead has shown high geomean value with 0.11 to 0.46 g/m 2 /month high concentration, this is 5-folds higher concentration than Antimony levels; Antimony has shown lower geomean value i.e. 0.018 to 0.0635 g/m 2 /month concentration.Whereas Copper, Zinc, Chromium and Nickel appear with 0.05, 0.02, 0.01 and 0.02 g/m 2 /month, respective, high geomean value at Birgaon (R-2) but the concentrations found in different way in different sites.Copper has been found high concentration at R-3 with 1.17 g/m 2 /month; this concentration is four times greater than Chromium concentration (0.30); which is found at residential area R-3.Zn and Ni have found with high concentration at sensitive areas R-8 (1.1399 g/m 2 /month) and R-7 (0.88 g/m 2 /month).Manganese found at University campus (R-7) with good geomean value i.e. 0.52, but the high concentration has been found at residential site (R-3) with 0.711 g/m 2 /month.The statistical boxplot analysis expresses the mean value of As, Hg, Co and Sb have deviating towards 75 th percentile whereas V and Pb shown close to 25 th percentile and the lower outlier of As, Hg, Pb and Co are far from 5 th percentile; V and Sb shows very near to the 5 th percentile.The upper outliers of all heavy metals found more than 95 th percentile.Far away projection of upper outliers from 95 th percentile values of heavy metals in case of R-2 compared to other sites is explained the strong variation in the emission characteristics of industrial sources close to this receptor site.Earlier studies on heavy metal concentration in dust fallout have shown comparable levels with the present study (Thakur et al, 2004;yadav andRajamani, 2006 andWang et al, 2009).

CONCLUSION
In conclusion it has been found that ambient level dust fallout appears with high deposition in summer and shown significant increase from year to year; while rainy and post rainy seasons have shown low deposition rate.This might be due to higher increase in industrial belt in the state with incremental percentage of 53% during this decade and hundred-fold increase in civil construction in study region due to designation of state capital in yr 2000.Highest Temporal variability of ambient dust fallout measurement data has been observed at Birgaon (Industrial residential area R-2), whereas lowest value has been observed at R-7 compared to all sampling sites due to multi-complexity in the source characteristics close to these sites.Occurrence of higher degree of spatial variability across all receptor sites have also explained the variable approach of source characteristics with potential effect of local sources on dust fall compared to fine particulate matter.Spatiotemporal variability of dust fall chemical constituents have also given important conclusions on impact pattern of urban sources of dust emissions on environmentally defined receptors.Lower trend in spatial variability of NO 3 − , Na, Hg and OC compared to other chemical species have shown similar source origin across the receptors; peculiar indicators of emissions resulting from local municipal waste disposition.Significant presence and variable attachment of toxic species in dust fall measured across all receptors is explained the need of source apportionment of dust fallout in microscale compared to fine particulates for whom source characterization of their measurements at regional scale are many times represent real-world situation.

Fig. 1 .
Fig. 1.Site description of source and receptor sampling stations for dust fallout measurements in Raipur, Chhattisgarh.

Fig. 2 .
Fig. 2. Statistical Box plot Analysis of chemical constituents of ambient dust fallout.

Table 1 .
Identification and grouping of sampling sites in the study area.