1.
D.P. Mukhopadhyay
Dpmcpcb@yahoo.com
Central Pollution Control Board ,Zonal Office, Kolkata
September 23, 2007
SOME RECENT ADVANCES IN THE FIELD OF
AIR QUALITY MANAGEMENT
organised by IAAPC
in a workshop
on
UNCERTAINTY AND ROLE OF STATISTICS IN CHEMICAL
CHARACTERISATION OF PARTICULATE
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Studies carried out in Eastern Part of India on Chemical
Characterisation of Particulates
• Characterisation of PM10 and PM2.5 at traffic intersection in
Kolkata and assessment of their impact on human health
• Characterization of PM10 during Diwali
• Characterisation of emission and dust form different sources
• Ambient Air Quality Monitoring at Port Canning, West
Bengal border in Bangladesh (MALE DECLARATION)
• Impact of sponge iron plant on environment and efficiency of
ESP in pollution control
• Ambient air quality at Jatin Das Park, Kolkata
• Identification of pollutant type, size and concentration in the
genesis of severe thunderstorm
Contd…2.
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3.
Studies carried out in Eastern Part of India on Chemical
Characterisation of Particulates
• Performance evaluation of RDS of Envirotech with respect to
Anderson Sampler
• Assessment of SO2 and NO2 by Active and Passive method at
Canning, West Bengal
• Assessment of BTX in major Traffic intersection in Kolkata
• Performance of Air Pollution control devices in Foundries in
Howrah, West Bengal
• Role of Statistics in interpretation of Air Quality data
• Evaluation of QC/QA status in ambient air quality monitoring
projects
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4.
Important prerequisite for Air Quality Management:
Monitoring
Basic Needs
1. Finance
2. Selection of site
3. Selection of sampler and their performance
4. Operational Discipline
1. Installation of sampler
2. Collection of sample and their analysis
5. Data Management
1. Reporting of the data with level of uncertainty
2. Interpretation
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5.
Potential Uncertainty Sources
• Sampling (cleaning procedure,
operation of filter paper, flow etc.)
• Transport and storage of sample
• Blank sample
• Analysts
• Quantification of detection limit
• Calibration
• Environmental influence parameters
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6.
Important Steps Of Uncertainty
• Identification of sources of error
• Minimization of error
• Identification of sources of uncertainty and estimation of
uncertainty
• Reporting of data alongwith level of uncertainty and
sensitivity analysis of uncertainty
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7.
Selection of Sampler and its Performance
• Availability of sophistical sampling equipment cannot ensure
reliable data
• Only operation discipline can ensure the reliability of data
• Performance of sampling equipment depends on controlling of
error from different sources not only on selection of equipment
• Major sources in Indian context are:
• Cleanliness
• Flow Accuracy
• Quality of Filter paper and mode of preparation
• Voltage fluctuation
• Design of Impingers
• High mass loading causing poor accuracy of flow
• Attrition of mass
• Artifact
• Sampling duration
• Moisture content (internal/external) in filter paper
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8.
Experimental evidence on Magnitude of
error on Operational Discipline
1. Cyclone were cleaned after 24 hours after using it on
completion of three shifts leading to error of 2.5% to 6%
(variation due to season)
2. Cyclone were cleaned on completion of 8-hourly shift leading to
reduction of error to < 1%
3. Black fine mass accumulated on the wall of the cyclone
particularly in Kolkata
4. This source of error was frequently observed in different
organisation in India
5. Now error is minimised significantly but uncertainty prevails at
the level of < 1% which is negligible.
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9.
Identification of sources of error and
their magnitude
• Several experiments were carried to estimate the
uncertainty budget on the sampling
• Outcome of these experiments revealed the following
major sources
• Flow accuracy
• Duration of sample
• Quality and preparation of filter paper
• Flow rate for SO2 and NO2
• Moisture content
• Design of Impingers
• Other sources may be considered as minor ECRD.IN

10.
Traceability in Air Quality Monitoring
• Measurement of the amount in sample taken for
analysis
• Preparation of the sample according to field and
defined experimental condition
• Calibration of an instrument with a standard
solution of known concentration
• Measurement of the instrument response
• Calculation of the concentration of the analyte in
original sample
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11.
Estimation of Precision, Accuracy and
Bias
Quarterly EstimateData Type Acceptance
Criteria 1 2 3 4
Precision
Collocation
< 10% CV 13% 11% 7.8% 9.2%
Accuracy-Flow
Rate
< + 4% Std.
0.4% 0.8% -1% -0.9%
Bias-Performance
Evaluations
< + 10% 9.5% 12% -6.1% 1.2%
Basic components and their criteria
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12.
Measurement of Uncertainty of PM10
• The Uncertainty of PM10 measurement was quantified
from precision data and collocated parallel measurements
• Uncertainty was + 4% (95% confidence interval) at
concentration range of 50 - 500 µg/m3
• The quantitation limit was determined from Standard
Deviation of field blanks
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13.
Advantages over error minimization
Comparative study was carried by Anderson Sampler and
Envirotech RDS Sampler
PM 10 g/m3
PM 10 g/m3
STATION
ANDERSON ENVIROTECH
%Difference
HAZRA 223 246 9.35
RABINDRASADAN 266 245 -8.0
TOLLYGUNGE 518 552 6.16
SHYAMBAZAR 351 334 -5.09
COSSIPUR 327 333 1.80
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14.
COMPARISON OF RESULTS OBTAINRD
THROUGH ACTIVE AND PASSIVE SAMPLER
SO2
(mg/m3
)
NO2
(mg/m3)
SO2
(mg/m3
)
NO2
(mg/m3)
SO2
(mg/m3
)
NO2
(mg/m3)
STP STP
2.0 23.3 5.4 15.7 63.0 -48.4
2.5 23.3 5.6 16.0 55.4 -45.6
3.8 18.1 9.3 9.6 59.1 -88.5
1.7 10.5 2.5 4.4 32.0 -138.6
0.9 10.1 0.7 2.5 -28.6 -304.0
0.3 4.0 1.0 1.8 70.0 -122.2
0.8 6.0 2.3 2.8 65.2 -114.3
0.8 11.2 1.1 2.6 27.3 -330.8
0.5 7.5 2.2 4.5 77.3 -66.7
1.0 12.9 4.5 11.3 77.8 -14.16
0.5 19.7 9.8 17.9 94.9 -10.1
1.7 14.1 9.1 9.6 81.3 -46.9
1.4 15.3 9.4 9.7 85.1 -57.7
0.3 6.8 3.0 4.2 90 -61.9
SO2 = 0.512 NO2 = 0.909
ACTIVE SAMPLER PASSIVE SAMPLER DIFFERENCE IN %
*Correlation between active sampler with passive sampler
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15.
Concentration and ratio of PM2.5 with PM10 (12 hrs.
mean value)
STATION PM 2.5 g/m3
PM 10 g/m3
RATIO
Hazra 179 256 0.76
Park Circus 370.5 553.5 0.67
Science City 221.5 342.5 0.65
Shyam Bazar 201.5 271 0.75
Cossipur 219 322.5 0.68
Rabindra Sadan 164 268.5 0.62
Average 225.9 335.7 0.69
SD 74.3 112 0.06
CV 32.9 33.4 8.1
Since consistent ratio of PM10/PM2.5 are being maintained and present
monitoring network are mainly covered by PM10 sampler. Chemical
characterization of PM10 can easily be extrapolated to PM2.5 based on
earlier observation. Strong data base can be maintained particularly
where PM2.5 sampler is not available. But one time study is required.
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16.
Chemical Behaviour of Particulates in Environment
• Several studies were carried out to evaluate chemical
character in Particulates in different area:
- Kolkata (Metropolitan)
- Bhubaneswar, Orissa (City)
- Asansol, West Bengal (City)
- Dhanbad, Bihar (City)
- Durgapur, West Bengal (City)
- Canning, Sundarban (Remote
Area),Transboundary station
- Moutorh, West Bengal (Rural Area)
Contd….2.
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17.
Chemical Behaviour of Particulates in Environment
-
Association of Chemicals with Particulate
Level of
Association
Name of chemical
Significant K, NH4, F, Cl,
NO2, NO3, SO4,
Cu, Mn, Fe, Zn,
Cd, Al, PAHs, EC,
OC
Not
significant
Hg, Ba, Pb, Na, Ca,
Mg,
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18.
Behaviour of Particulates in Environment
Data were statistically processed to evaluate distribution of total
PAH in PM10 and PM2.5 and their variability
Location g/m3
of
PM2.5
g/m3
of PM 10
Difference
(%)
ng/g of
PM2.5
ng/g of
PM10
Difference
(%)
Hazra 8.9 9.4 -6 0.050 0.037 26
Park
Circus
16.4 17.5 -7 0.044 0.032 28
Science
City
10.6 11.8 -12 0.048 0.034 28
Shyam
Bazar
9.8 9.8 -1 0.049 0.036 25
Cossipur 10.5 11.3 -8 0.048 0.035 27
Rabindra
Sadan
8.3 9.8 -18 0.051 0.036 28
Average 10.7 11.6 -8 0.048 0.0351 27
SD 2.9 3.05 5.75 0.00 0.00 1.3
CV 27 26 -68 5 5 5
•Correlation coefficient was found highly significant(r=0.99) in both the cases
•PAHs have consistent affinity toward PM2.5 on normalising with Particulate ECRD.IN

19.
Metals
Parameters
µg/m3 of
PM2.5
µg/m3 of
PM10
Diff.(%)
µg/µg of
PM2.5
µg/µg of
PM10
Diff.(%)
Al 1.95 1.14 42 0.00594 0.00390 34
Cr 0.02 0.01 68 0.00005 0.00002 64
Mn 0.05 0.04 26 0.00016 0.00013 17
Cu 0.03 0.01 62 0.00010 0.00004 57
Cd BDL BDL NA BDL BDL NA
Fe 0.96 0.94 2 0.00292 0.00321 -10
Zn 0.33 0.11 67 0.00100 0.00037 63
Pb 0.38 0.22 42 0.00115 0.00075 35
Ni 0.01 0.00 68 0.00004 0.00001 64
Conc.of Metals in PM2.5 & PM10 at Ultadanga
Chemical Behaviour of Particulates in Environment
•Except Fe, there is a tendency of adherence of metals in
fine particulate.
•Metals are originated from the same sources
contributing Particulate.
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20.
Ions
Parameters
µg/m3 of
PM2.5
µg/m3 of
PM10
Diff. (%)
µg/µg of
PM2.5
µg/µg of
PM10
Diff.(%)
Na 12.04 10.375 14 0.0367 0.0355 3
K 3.01 1.715 43 0.0092 0.0059 36
Ca 5.84 4.8 18 0.0178 0.0164 8
Mg 0.59 0.34 42 0.0018 0.0012 33
NH4 10.67 6.115 43 0.0325 0.0209 36
F 1.42 0.96 32 0.0043 0.0033 23
Cl 6.64 6.38 4 0.0202 0.0218 -8
NO2 0.02 0.06 -200 0.0001 0.0002 -100
NO3 13.24 23.075 -74 0.09 0.084 7
SO4 38.14 38.16 0 0.1785 0.1807 -1
Conc.of Ions in PM2.5 & PM10 at Ultadanga
Chemical Behaviour of Particulates in Environment
• Almost same trend was observed in case of ions
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21.
EC/IC
PM 2.5 PM 10 PM 2.5 PM 10
OC 10.07 20.61 -105 0.07 0.068 2
EC 14.62 20.57 -41 0.10 0.068 35
TC 24.70 41.18 -67 0.17 0.135 22
OC 15.40 14.76 4 0.09 0.06 34
EC 18.91 24.62 -30 0.11 0.10 14
TC 34.30 39.39 -15 0.20 0.15 23
BETHUNE
LA MARTS
Diff (%)
Concentration of different Carbon Fraction in PM2.5 & PM10
Diff (%)
μg/m3 μg/μg
ParameterLocation
Chemical Behaviour of Particulates in Environment
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22.
Impact of PAH on Human Health
1-hydroxypyrene
nmole/l
Creatinine
gm/l
1- hydroxypyrene
nmole/gm of
creatinine.
Subject location
MAX MIN MAX MIN MAX MIN
Traffic police
(Rabindra sadan)
0.71 0.26 1.24 0.15 3.9 0.26
Traffic police
( Hazra)
0.48 0.25 1.24 0.20 1.25 0.14
COPD (patient SSKM
hospital)
0.58 0.27 0.78 0.39 0.85 0.63
Bethune
school
0.25 0.23 0.82 0.09 3.3 0.3
Quantification of 1-Hydroxypyrene from the urine

23.
Particulates and its Chemical Nature
• Environmental behaviour of chemicals are mainly governed by
concentration of Particulates and their sizes
• Affinity of chemicals to adhere on the particulate depends on the sizes
• Nature of distribution of chemicals between PM10 and PM2.5 is almost consistent
• Ratio of PM10 / PM2.5 is also consistent
• Interrelation among the parameters and very good correlation of
these parameters with PM10 with few exception in particular area clearly
revealed the adherence of chemicals to particulate. Increase particulate enhances of
chemicals which is a cause of concern.
• Reliability of data and measurement of uncertainty
• Distribution pattern
• Behaviour of PM10 among the hours, shifts (8 hours), days, months
• Influence of meteorological condition
• Calibration function
• Influence of different activities leading to air pollution.
These observations prompted to study in details the following aspects using RSPM,
SO2 and NOx data generated by WBPCB through AAQMS.
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24.
Mode of Processing of Data
• Mean
• Median
• Coefficient of variation
• Auto correlation
• Multiple Regression Method
• Analysis of Variance
• Principal Component Analysis
Mode of Processing of Data
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25.
Distribution Pattern of Pollutants
• Distribution of 15 minutes peak
• Distribution of 1-hour mean data
Based on the following parameters Midpoints of Groups - Frequency
- Cumulative Frequency - Percent
Observations
• Distribution shows that the large majority of 1-hour
concentration are relatively small compared to 15 -
minutes values found to occur extremely high at few
occasions
• Such variability may be attributed to nearby sources
(point & non-point) having emission of random nature
influenced by meterological condition
• The level of sudden exposure to high concentration may
be quantified
Cont…2
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• Excedence of permissible limit of interest can be clearly
depicted
• Influence of different activities (Traffic, Industry,
burning landscape, meteorological condition responsible
for air pollution directly or indirectly can be easily
ascertained if date are processed accordingly.
• Consistency in concentration of Particulates can be
evaluated based on the ratio of 15-minutes / hourly /
daily data
Distribution Pattern of Pollutants
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27.
TABLE ANOVA TABLE OF RSPM (2005 - 2006)
MONTHS 2005 2006 2005 2006
JANUARY NS NS S S
FEBRUARY NS NS S NS
MARCH S NS S S
APRIL NS NS S NS
MAY S S NS NS
JUNE S NS S S
JULY NS NS S S
AUGUST NS NS NS NS
SEPTEMBER S NS S S
OCTOBER NS S S S
NOVEMBER S S S S
DECEMBER S S S S
* 'S'=Significant, 'NS'=Non-Significant
Variation among the shifts and days of each
• No systematic trend was observed
• Little variation among the days was observed
• Emission of pollutant to atmosphere and their retention are of
random nature
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28.
Influence of Meteorological Parameter
• Influence of Meteorological condition was evaluated using
pearson correlation coefficient
• These coefficients clearly indicated the strong association
of prevalent Particulate with wind speed and temperature
Pollutants RSPM SO2 NO2
Meteorologic
al
Shift-
1
Shift-
2
Shift-
3
Shift-
1
Shift-
2
Shift-
3
Shift-
1
Shift-
2
Shift-
3
Temperature -0.75 -0.80 -0.68 -0.27 -0.09 -0.35 -0.47 -0.44 -0.51
Relative
humidity
-0.25 -0.21 -0.25 -0.06 -0.101 -0.04 -0.26 -0.32 -0.32
Wind
Velocity
-0.67 -0.68 -0.52 -0.25 -0.11 -0.28 -0.35 -0.40 -0.42
Critical wind speed causes collapsing of assimilation capacity is
less than 1.2m/s. Lowering of temperature aggravate the problem.
PCA has established this observation.
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29.
Principal Component Analysis
1. SPM; 2. RPM; 3. SO2; 4. NO2; 5. NO; 6. O3; 7.CO; 8.
Temperature; 9.NMHC; 10. Wind Speed; 11. Relative Humidity.
2. Wind speed (WS) is mainly responsible for such variability of
pollutants as it figures in first component and Temperature is next to
the WS as it figures in second component
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30.
On thorough scrutinizing and processing the Automatic
AAQ data, the salient observations are given below:
• The excedence of NAAQS stipulated for residential area is
restricted to five months (Jan, Feb, Sep, Nov and Dec). But sudden
increase of the level of concentrations occurred at certain point of
time
• The variation of concentration of PM10 was significantly
high among the shifts as reflected from CV which even reached to
50%. But no definite trend
• Though variation of emission from different sources are
expected during day and night but that is not reflected in variation of
PM10. This aspect is more prominent during winter
• The strong association of PM10 with wind speed and
temperature revealed that variability of PM10 are mainly governed by
wind speed and temperature.
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•The prevailing wind speed in the winter in the range of 1.0 to 1.3
meter/sec is not adequate for dispersion of the pollutants. Whereas,
little increase of this range may capable to disperse the pollutant in
the later month, namely March and April.
•The problems of accumulating RPM in surface layer has started
below 25 oC and further aggravated with decrease of temperature.
As a result, scenario of ambient air quality is worst during January
and improves with definite trend from the end of January except in
few (not exceeded ~2% of the all raw data) occasions. This may be
attributed to local effect such as traffic congestion, open burning,
etc.
•The frequency of exceeding the limits shows good agreement with
the degree of wind speed and level of ambient temperature. .
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32.
The ratio of SPM & RPM was lowest in the range of 0.32 to
0.63 during 06-14 hrs and 0.60 to 0.73 during 14-00 hrs to 22-
00 hrs and 0.51 to 0.86 during 22-00 hrs to 6-00 hrs. Increase in
ratio by 0.35 to 0.86 is mainly due to more contribution of the
RSPM.
Quantity of emission both from industries and vehicles remains
more or less uniform over the years but assimilation as well as
dispersion of the pollutants is completely dependant on
meteorological factors. Land use pattern may aggravate valley
effect that would accelerate inversion.
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33.
Distribution of ozone and O3 depleting substances in
the atmosphere and observed changes
Seasonal and day to day changes associated with
meteorological condition
Influence of dynamical processes on ozone abundance
 Transport of pollutants by Brewer-Dobson
Circulation from the main source to the region
of highest abundance
 Chemistry of ozone by causing changes in
temperature leading to chemical reaction
Ozone Chemistry
Simulation and Depletion
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34.
Distribution, Climatology and Natural variation
Forecasting day to day changes
• Total ozone variation are highly correlated with meteorological
disturbances
• Statistical model has already been developed based on multiple
regression of past values of AAQD and meteorological variable
• Distribution pattern based on possibility theory and using
AAQD generated in few sites adopting QA programme, initial
forecast produced by regression equation is correlated
• Outlier data were detected. These were mainly due to problem
in calibration of analyser
• Monthly average was verified with CV. If CV > 10 percent,
values greater 2  were eliminated with understanding the
logic
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35.
Atmospheric CO2 variability (hourly) at city
Monthly average was verified with CV. If CV > 10
percent, values greater 2  were eliminated with
understanding the logic for such high. Then ANOVA
technique was applied to evaluate the level of variation
among the three months. Then variability of diurnal cycle
on month to month basis normalising with synoptic
weather events. The harmonic regression was applied for
this. This observation clearly reflect rate of photo
synthesis, man made activities, say condition. Windrose
clearly indicate the source contribution limitation.
• Significant gaps in the data set as calibration failure
• Non functioning of sensor
• Localised effect
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36.
Data collection and processing
The data file in Excel and software in GW basic developed
for processing of AAQD
File - 1
Name Monitoring function
CP1 PM10 PM10 Measurement system
CP2 MET Meteorological Measurements
File - 2
Data files are ASCII files with comma separated field
and text surrounded with double quotes
Year, Month, Day, Time, Parameter-1, Parameter-2,
Parametern, Flag-1, Flag-2, Comment, ExNainf
These are created defining the field and limitations Contd…2
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37.
Data collection and processing
File - 3
File - 4
Weekly Data
Created from the time checked raw data files with understanding of
flagging events fresh in mind and stored in the same format
Monthly data
File - 5
Yearly data
File - 6
Overall interpretation of data giving emphasis on distribution pattern
(spatial / temporal). Chemical behaviour, trend, uncertainty
Software in GW Basic developed for transformation of data file and
processing of data using different statistical technique were developed in as
Monthly.Bas, Weekly.Bas, Regyr.Bas etc.
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38.
Flag Definition (Calibration gas, System check)
Z - Zero Check
V - Data passes all test
I - Intake sample line problem
Q - Unspecified questionable data
W1 - First working gas
W2 - Second Working Gas
SF - Sample Flow fluctuation
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